Methods, apparatus, and systems for wireless sensing measurement and reporting.

Abstract

This provides an efficient and effective method for measuring and reporting wireless sensing data. [Solution] The wireless data communication network comprises a transmitter that transmits a time-series wireless sounding signal (WSS) based on an associated wireless protocol, and a receiver. The wireless data communication network consists of a physical (PHY) layer, a media access control (MAC) layer, and at least one upper layer. The receiver receives a time-series WSS (TSWSS) based on a wireless protocol via the venue's wireless channel and performs a series of wireless sensing measurements based on the received TSWSS to obtain sensing measurement results. The receiver's PHY layer or MAC layer reports the sensing measurement results to at least one upper layer of the receiver. At least one upper layer of the receiver performs a sensing-based task based on the sensing measurement results.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application incorporates by reference in its entirety the disclosures of each of the following cases and claims the priority thereof. (a) U.S. Provisional Patent Application No. 63 / 253,083, titled "Methods, Apparatus, and Systems for Wireless Sensing, Detection, and Tracking", filed on October 6, 2021, (b) U.S. Provisional Patent Application No. 63 / 276,652, titled "Methods, Apparatus, and Systems for Wireless Monitoring of Vertical Signatures and Peripheral Movements", filed on November 7, 2021, (c) U.S. Provisional Patent Application No. 63 / 281,043, titled "Sensing Methods, Apparatus, and Systems", filed on November 18, 2021, (d) U.S. Provisional Patent Application No. 63 / 293,065, titled "Methods, Apparatus, and Systems for Speed Improvement and Separation", filed on December 22, 2021, (e) U.S. Provisional Patent Application No. 63 / 300,042, titled "Methods, Apparatus, and Systems for Wireless Sensing and Sleep Tracking", filed on January 16, 2022, (f) U.S. Provisional Patent Application No. 63 / 308,927, titled "Methods, Apparatus, and Systems for Wireless Sensing Based on Multiple Groups of Wireless Devices", filed on February 10, 2022, (g) U.S. Provisional Patent Application No. 63 / 332,658, titled "Methods, Apparatus, and Systems for Wireless Sensing", filed on April 19, 2022, (h) U.S. Patent Application No. 17 / 827,902, titled "Methods, Apparatus, and Systems for Speed Improvement and Separation Based on Audio Signals and Wireless Signals", filed on May 30, 2022, (i) U.S. Provisional Patent Application No. 63 / 349,082, titled "Methods, Apparatus, and Systems for Wireless Sensing Voice Activity Detection", filed on June 4, 2022, (j) U.S. Patent Application No. 17 / 838,228, titled "Methods, Apparatus, and Systems for Wireless Sensing Based on Channel Information", filed on June 12, 2022, (k) U.S. Patent Application No. 17 / 838,231, title "Method, Apparatus, and System for Identifying and Quantifying Devices for Wireless Sensing," filed June 12, 2022. (l) U.S. Patent Application No. 17 / 838,244, title "Method, Apparatus, and System for Wireless Sensing Based on Linkwise Motion Statistics," filed June 12, 2022. (m) U.S. Provisional Patent Application No. 63 / 354,184, Title: "Method, Apparatus, and System for Orienting Motion and Removing Outliers," Filing Date: June 21, 2022. (n) U.S. Provisional Patent Application No. 63 / 388,625, title "Wireless Sensing and Indoor Positioning Method, Apparatus, and System", filed July 12, 2022. (o) U.S. Patent Application No. 17 / 888,429, title "Method, Apparatus, and System for Wireless-Based Sleep Tracking," filed August 15, 2022. (p) U.S. Patent Application No. 17 / 891,037, title "Method, Apparatus, and System for Map Reconstruction Based on Wireless Tracking," filed August 18, 2022. (q) U.S. Patent Application No. 17 / 945,995, title "Method, Apparatus and System for Wireless Biological Monitoring Using High-Frequency Signals", filed September 15, 2022.

[0002] This instruction relates to wireless sensing in general. More specifically, it relates to methods, systems, and apparatus for performing wireless sensing measurements and reporting. [Background technology]

[0003] With the proliferation of Internet of Things (IoT) applications, billions of home appliances, phones, smart devices, security systems, environmental sensors, vehicles and buildings, and other wirelessly connected devices will transmit data and communicate with each other or with people, enabling everything to be constantly measured and tracked. Among the various approaches to measuring what is happening in the surrounding environment, wireless sensing has been attracting increasing attention in recent years for the ubiquitous deployment of wireless devices. Furthermore, since human activity affects the propagation of wireless signals, understanding and analyzing how wireless signals respond to human activity can reveal a wealth of information about that activity. As more bandwidth becomes available in next-generation wireless systems, wireless sensing will enable many smart IoT applications that are currently only imaginable in the near future. This is because wider bandwidth allows us to see more multipaths even in scattered environments such as indoors or in metropolitan areas, and treat them as hundreds of virtual antennas / sensors. While several technical standards, such as IEEE 802.11bf, support wireless sensing, many details of wireless sensing, such as how to perform wireless sensing measurement and reporting, are still not standardized. Therefore, there is a need for efficient and effective methods for measuring and reporting wireless sensing data. [Overview of the Initiative]

[0004] This instruction relates to wireless sensing in general. More specifically, it relates to methods, systems, and apparatus for performing wireless sensing measurements and reporting.

[0005] One embodiment describes a system in a wireless data communication network for wireless sensing. This system comprises a transmitter configured to transmit a time-series wireless sounding signal (WSS) based on a wireless protocol associated with the wireless data communication network, and a receiver. The wireless data communication network consists of a physical (PHY) layer, a media access control (MAC) layer, and at least one upper layer. The receiver is configured to receive a time-series WSS (TSWSS) based on the wireless protocol via a wireless channel of the venue, and to perform a plurality of wireless sensing measurements based on the received TSWSS to obtain sensing measurement results. The receiver's PHY layer or MAC layer reports the sensing measurement results to at least one upper layer of the receiver. At least one upper layer of the receiver performs a sensing-based task based on the sensing measurement results.

[0006] Another embodiment describes a wireless device in a wireless data communication network for wireless sensing. The wireless device comprises a processor, memory communicatively coupled to the processor, and a receiver communicatively coupled to the processor. Additional wireless devices in the wireless data communication network are configured to transmit time-series wireless sounding signals (WSS) based on a wireless protocol associated with the wireless data communication network. The wireless data communication network consists of a physical (PHY) layer, a medium access control (MAC) layer, and at least one upper layer. The receiver is configured to receive time-series WSS (TSWSS) based on the wireless protocol via the venue's wireless channel and to perform a number of wireless sensing measurements based on the received TSWSS to obtain sensing measurement results. The receiver's PHY layer or MAC layer reports the sensing measurement results to at least one upper layer of the receiver. The receiver's at least one upper layer performs a sensing-based task based on the sensing measurement results.

[0007] Another embodiment describes a method of wireless sensing. This method includes: transmitting a time-series wireless sounding signal (WSS) based on a wireless protocol associated with the wireless data communication network by a transmitter in a wireless data communication network, wherein the wireless data communication network comprises a physical (PHY) layer, a media access control (MAC) layer, and at least one upper layer; receiving a time-series WSS (TSWSS) based on a wireless protocol via a wireless channel of a venue by a receiver in the wireless data communication network; performing a plurality of wireless sensing measurements based on the received TSWSS and obtaining sensing measurement results; reporting the sensing measurement results to at least one upper layer of the receiver by the PHY layer or MAC layer of the receiver; and performing a sensing-based task based on the sensing measurement results by at least one upper layer of the receiver.

[0008] Other concepts relate to software for implementing this teaching regarding wireless sensing measurement and reporting. Additional novel features are partially defined in the following description and will be partially apparent to those skilled in the art by examining the following description and accompanying drawings, or by the manufacture or operation of the embodiments. Novel features of this teaching can be realized and achieved by implementing or using various embodiments of the methods, means and combinations described in the detailed embodiments below. [Brief explanation of the drawing]

[0009] The methods, systems, and / or apparatus described herein will be further described in terms of exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These embodiments are non-limiting exemplary embodiments, and similar reference figures in some of the drawings represent similar structures.

[0010] [Figure 1]An example of a wireless sensing procedure according to some embodiments of the present disclosure is shown.

[0011] [Figure 2] Another example of a wireless sensing procedure according to some embodiments of the present disclosure is shown.

[0012] [Figure 3] An example of a trigger-based wireless sensing measurement instance according to some embodiments of the present disclosure is shown.

[0013] [Figure 4] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 5] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 6] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 7] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 8] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 9] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 10] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 11] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 12] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown. [Figure 13] Various exemplary use cases of wireless sensing and reporting according to various embodiments of the present disclosure are shown.

[0014] [Figure 14] A diagram showing an example of measurement instance sharing within a wireless sensing session according to some embodiments of the present disclosure.

[0015] [Figure 15] A diagram showing an example of measurement instance sharing across a wireless sensing session according to some embodiments of the present disclosure.

[0016] [Figure 16] An exemplary block diagram of a first wireless device of a system for wireless sensing according to some embodiments of the present disclosure.

[0017] [Figure 17] An exemplary block diagram of a second wireless device of a system for wireless sensing according to some embodiments of the present disclosure.

[0018] [Figure 18] A flowchart showing an exemplary method for identifying a device used for wireless sensing according to some embodiments of the present disclosure.

[0019] [Figure 19] An example of bi - directional responder - to - responder sensing according to some embodiments of the present disclosure is shown.

[0020] [Figure 20] A number of stations (STAs) in an infrastructure - less mode forming an ad - hoc network according to some embodiments of the present disclosure are shown.

[0021] [Figure 21] Various use cases for infrastructure - less mode sensing according to some embodiments of the present disclosure are shown. [Figure 22]This disclosure illustrates various use cases for non-infrastructure mode sensing through several embodiments. [Figure 23] This disclosure illustrates various use cases for non-infrastructure mode sensing through several embodiments. [Figure 24] This disclosure illustrates various use cases for non-infrastructure mode sensing through several embodiments. [Figure 25] This disclosure illustrates various use cases for non-infrastructure mode sensing through several embodiments.

[0022] [Figure 26] This disclosure illustrates various use cases for updating proxy sensing (SBP) procedures through several embodiments. [Figure 27] This disclosure illustrates various use cases for updating proxy sensing (SBP) procedures through several embodiments. [Modes for carrying out the invention]

[0023] In one embodiment, this teaching discloses a method, apparatus, device, system, and / or software (method / apparatus / device / system / software) for a wireless monitoring system. Time-series channel information (CI) of a wireless multipath channel (channel) can be acquired (e.g., dynamically) using a processor, a memory communicably connected to the processor, and a set of instructions stored in the memory. Time-series CI (TSCI) can be extracted from radio signals (signals) transmitted through the channel between a type 1 heterogeneous wireless device (e.g., a radio signaler, TX) and a type 2 heterogeneous wireless device (e.g., a radio receiver, RX) in a venue. The channel can be influenced by representations of objects in the venue (e.g., motion, movement, representation, and / or changes in position / pose / shape / representation). The properties of the object and / or spatial-temporal information (STI (spatial-temporal information), e.g., motion information) and / or the motion of the object can be monitored based on the TSCI. Tasks can be performed based on the properties and / or STI. The presentation associated with the task may be generated within the user interface (UI) on the user's device. TSCI may be a radio signal stream. TSCI or each CI may be preprocessed. The device may be a station (STA). The symbol "A / B" means "A and / or B" in this instruction.

[0024] Expression can include arrangement, arrangement of movable parts, location, position, orientation, identifiable place, area, spatial coordinates, presentation, state, static representation, size, length, width, height, angle, scale, shape, curve, surface, area, volume, pose, posture, explicitness, body language, dynamic representation, movement, motion sequence, gesture, stretching, contraction, deformation, bodily representation (e.g., head, face, eyes, mouth, tongue, hair, voice, neck, limbs, arms, hands, legs, feet, muscles, movable parts), surface representation (e.g., shape, texture, material, color, electromagnetic (EM) properties, visual pattern, moisture, reflectivity, translucency, flexibility), material properties (e.g., biological tissue, hair, cloth, metal, wood, leather, plastic, artificial material, solid, liquid, gas, temperature), movement, activity, behavior, change in representation, and / or any combination thereof.

[0025] Radio signals may include transmit / receive signals, EM emissions, RF signals / transmits, licensed / unlicensed / ISM band signals, bandwidth-limiting signals, baseband signals, wireless / mobile / cellular communication signals, mesh signals, optical signals / communications, downlink / uplink signals, unicast / multicast / broadcast signals, signals compliant with standards (e.g., WLAN, WWAN, WBAN, international, industry, de facto, IEEE802, 802.11 / 15 / 16, WiFi, 802.11n / ac / ax / be, 3G / 4G / LTE / 5G / 7G / 8G, 3GPP®, Bluetooth, BLE, Zigbee, RFID, UWB, WiMax), standard frames, beacon / pilot / probe / query / handshake / synchronization signals, management / control / data frames, management / control / data signals, standardized wireless / cellular communication protocols, reference signals, source signals, operational probe / detection / sensing signals, and / or sequences of signals. Radio signals may include line-of-sight (LOS) components and / or non-LOS components (or path / link). Each CI may be extracted / generated / calculated / sensed at a layer of a Type 2 device (e.g., the PHY / MAC layer in the OSI model) and acquired by an application (e.g., software, firmware, driver, app, radio monitoring software / system).

[0026] A wireless multipath channel may include a communication channel, an analog frequency channel (e.g., having analog carrier frequencies around 700 / 800 / 900MHz, 1.8 / 1.8 / 2.4 / 3 / 5 / 6 / 27 / 60GHz), an encoding channel (e.g., in CDMA), and / or a wireless network / system channel (e.g., WLAN, WiFi, mesh, LTE, 4G / 5G, Bluetooth, Zigbee, UWB, RFID, microwave). It may include two or more channels. The channels may be continuous (e.g., having adjacent / overlapping bands) or non-continuous (e.g., non-overlapping WiFi channels, one at 2.4GHz and the other at 5GHz).

[0027] TSCI can be extracted from radio signals at the layers of a Type 2 device (e.g., the layers of the OSI reference model: physical layer, data link layer, logical link control layer, media access control (MAC) layer, network layer, transport layer, session layer, presentation layer, application layer, TCP / IP layer, internet layer, link layer). TSCI can be extracted from derived signals (e.g., baseband signals, motion detection signals, motion sensing signals) derived from radio signals (e.g., RF signals). It may also be a (radio) measurement sensed by a communication protocol (e.g., a standardized protocol) using an existing mechanism (e.g., radio / cellular communication standards / networks, 3G / LTE / 4G / 5G / 6G / 7G / 8G, WiFi, IEEE802.11 / 15 / 16). The motion detection signal may include a packet having at least one of a preamble, header, and payload (e.g., for data / control / management in a radio link / network). TSCI may be extracted from probe signals within the packet (e.g., training sequence, STF, LTF, L-STF, L-LTF, L-SIG, HE-STF, HE-LTF, HE-SIG-A, HE-SIG-B, CEF). Motion detection / sensing signals may be recognized / identified based on the probe signals. Packets may be standards-compliant protocol frames, management frames, control frames, data frames, sounding frames, excitation frames, illumination frames, null data frames, beacon frames, pilot frames, probe frames, request frames, response frames, association frames, reassociation frames, disassociation frames, authentication frames, action frames, report frames, pole frames, announcement frames, extension frames, query frames, acknowledgment frames, RTS frames, CTS frames, QoS frames, CF-Poll frames, CF-Ack frames, block acknowledgment frames, reference frames, training frames, and / or synchronization frames.

[0028] Packets may contain control data and / or motion detection probes. Data (e.g., ID / parameters / characteristics / settings / control signals / commands / instructions / notifications / broadcast-related information for Type 1 devices) may be obtained from the payload. Radio signals may be transmitted by Type 1 devices. They may be received by Type 2 devices. Databases (e.g., in a local server, hub device, cloud server, or storage network) may be used to store TSCI, characteristics, STI, signatures, patterns, behavior, trends, parameters, analysis, output responses, identification information, user information, device information, channel information, venue (e.g., map, environment model, network, proximity device / network) information, task information, class / category information, presentation (e.g., UI) information, and / or other information.

[0029] Type 1 / Type 2 devices may include at least one of the following: electronic equipment, circuits, transmitters (TX) / receivers (RX) / transceivers, RF interfaces, "Origin Satellite" / "Tracker Bot", unicast / multicast / broadcast devices, wireless source devices, source / destination devices, wireless nodes, hub devices, target devices, motion detection devices, sensor devices, remote / wireless sensor devices, wireless communication devices, wireless-enabled devices, standards-compliant devices, and / or receivers. A Type 1 (or Type 2) device may be heterogeneous, so that if multiple instances of a Type 1 (or Type 2) device exist, they may have different circuits, enclosures, structures, purposes, auxiliary functions, chips / ICs, processors, memory, software, firmware, network connectivity, antennas, brands, models, appearance, form, shape, color, materials, and / or specifications. Type 1 / Type 2 devices may include access points, routers, mesh routers, Internet of Things (IoT) devices, wireless terminals, one or more wireless / RF subsystems / wireless interfaces (e.g., 2.4GHz radios, 5GHz radios, fronthaul radios, backhaul radios), modems, RF front-ends, RF / wireless chips, or integrated circuits (ICs).

[0030] At least one of the following can be associated with identification information (ID), such as a UUID: Type 1 devices, Type 2 devices, links between them, objects, characteristics, STI, motion monitoring, and tasks. Type 1 / Type 2 / other devices can acquire / store / retrieve / access / preprocess / condition / process / analyze / monitor / apply TSCI. Type 1 and Type 2 devices can exchange network traffic on other channels (e.g., Ethernet, HDMI®, USB, Bluetooth, BLE, WiFi, LTE, other networks, wireless multipath channels) in parallel with radio signals. Type 2 devices can passively observe / monitor / receive radio signals from Type 1 devices on wireless multipath channels without establishing a connection (e.g., association / authentication) with a Type 1 device or requesting services from a Type 1 device.

[0031] A transmitter (i.e., a Type 1 device) can function as a receiver (i.e., a Type 2 device) temporarily, sporadically, continuously, iteratively, interchangeably, alternately, simultaneously, concurrently, and / or contemporaneously, and vice versa. A device can function as a Type 1 device (transmitter) and / or a Type 2 device (receiver) temporarily, sporadically, continuously, iteratively, simultaneously, concurrently, and / or concurrently. There may be multiple radio nodes, each being a Type 1 (TX) and / or Type 2 (RX) device. TSCI may be acquired for every two nodes when exchanging / exchanging radio signals. Object properties and / or STI may be monitored individually based on TSCI or together based on two or more (e.g., all) TSCIs.

[0032] The movement of an object can be monitored actively (whether the object is wearable / associated with the object in a Type 1 device, a Type 2 device, or both) and / or passively (whether both the Type 1 and Type 2 devices are not wearable / associated with the object). It can be passive because the object may not be associated with a Type 1 device and / or a Type 2 device. An object (e.g., a user, an automated guided vehicle, or an AGV) may not need to carry / attach any wearable / fixture (i.e., Type 1 and Type 2 devices are not wearable / attachable devices that the object needs to carry to perform a task). An object can be active because it may be associated with either a Type 1 device and / or a Type 2 device. An object may carry (or install) a wearable / fixture (e.g., a Type 1 device, a Type 2 device, or a device communicatively coupled to either a Type 1 or Type 2 device).

[0033] The presentation may be visual, audio, images, video, animation, graphical presentation, text, etc. The computation of the task may be performed by a processor (or logic unit) of a Type 1 device, a processor (or logic unit) of an IC of a Type 1 device, a processor (or logic unit) of a Type 2 device, a processor (or logic unit) of an IC of a Type 2 device, a local server, a cloud server, a data analysis subsystem, a signal analysis subsystem, and / or another processor. The task may be performed with or without reference to wireless fingerprints or baselines (e.g., collected, processed, computed, transmitted and / or stored in training phases / surveys / current surveys / previous surveys / recent surveys / initial wireless surveys, passive fingerprints), training, profiling, trained profiles, static profiles, surveys, initial wireless surveys, initial setup, installation, retraining, updates and resets.

[0034] A Type 1 device (TX device) may comprise at least one heterogeneous radio transmitter. A Type 2 device (RX device) may comprise at least one heterogeneous radio receiver. Type 1 and Type 2 devices may be colocations. Type 1 and Type 2 devices may be the same device. Any device may have a data processing unit / device, a computing unit / system, a network unit / system, a processor (e.g., a logic unit), memory communicably connected to the processor, and a set of instructions stored in memory that are executed by the processor. Several processors, memories, and instruction sets may be coordinated.

[0035] There may be multiple Type 1 devices that interact with the same Type 2 device (or multiple Type 2 devices) (e.g., communicate, exchange signals / controls / notifications / other data), and / or multiple Type 2 devices that interact with the same Type 1 device. Multiple Type 1 / Type 2 devices may be synchronous and / or asynchronous with the same / different window widths / sizes and / or time shifts, the same / different sync start times, sync end times, etc. Radio signals transmitted by multiple Type 1 devices may be sporadic, transient, continuous, repetitive, synchronous, simultaneous, and / or simultaneous. Multiple Type 1 / Type 2 devices may operate independently and / or cooperatively. Type 1 and / or Type 2 devices may have / are / are / have heterogeneous hardware circuitry (e.g., heterogeneous chips or heterogeneous ICs capable of generating / receiving radio signals, extracting CI from received signals, or making CI available). They may be connected in a way that allows them to communicate with the same or different servers (e.g., cloud servers, edge servers, local servers, hub devices).

[0036] The operation of one device may be based on its operation, state, internal state, storage, processor, memory output, physical location, computing resources, and network of other devices. Differential devices may communicate directly and / or via other devices / servers / hub devices / cloud servers. A device may be associated with one or more users, each with relevant settings. Settings may be selected, pre-programmed, and / or modified (e.g., adjusted, changed, corrected) / changed over time. A method may have additional steps. The steps and / or additional steps of a method may be performed in the order shown or in a different order. Any step may be performed in parallel, repeatedly, or otherwise repeated or otherwise. Users may be human, adult, elderly, male, female, youth, child, baby, pet, animal, organism, machine, computer module / software, etc.

[0037] For one or more Type 1 devices interacting with one or more Type 2 devices, any processing (e.g., time domain, frequency domain) may differ for different devices. Processing may be based on location, orientation, direction, role, user-related characteristics, settings, configuration, available resources, available bandwidth, network connectivity, hardware, software, processor, coprocessor, memory, battery life, available power, antenna, antenna type, antenna directivity / omnidirectional characteristics, power settings, and / or other parameters / characteristics of the device.

[0038] A radio receiver (e.g., a Type 2 device) may receive signals and / or other signals from a radio transmitter (e.g., a Type 1 device). A radio receiver may receive other signals from another radio transmitter (e.g., a second Type 1 device). A radio transmitter may transmit signals and / or other signals to another radio receiver (e.g., a second Type 2 device). The radio transmitter, radio receiver, another radio receiver, and / or another radio transmitter may be moving with an object and / or another object. The other object may be tracked.

[0039] Type 1 and / or Type 2 devices may be able to wirelessly connect with at least two Type 2 and / or Type 1 devices. A Type 1 device may be made to switch / establish a wireless connection (e.g., association, authentication) from a Type 2 device to a second Type 2 device at another location within the venue. Similarly, a Type 2 device may be made to switch / establish a wireless connection from a Type 1 device to a second Type 1 device at yet another location within the venue. Switching may be controlled by a server (or hub device), a processor, a Type 1 device, a Type 2 device, and / or another device. Different radios may be used before and after switching. A second radio signal (second signal) may be transmitted over a channel between a Type 1 device and a second Type 2 device (or between a Type 2 device and a second Type 1 device). A second TSCI of the channel extracted from the second signal may be obtained. The second signal may be the first signal. The properties of an object, STI, and / or other quantities may be monitored based on a second TSCI. Type 1 and Type 2 devices may be the same. Properties, STI, and / or other quantities with different timestamps may form a waveform. The waveform may be displayed in the presentation.

[0040] Radio signals and / or other signals may have embedded data. Radio signals may be a sequence of probe signals (e.g., repeated transmission of probe signals, reuse of one or more probe signals). Probe signals may change / vary over time. Probe signals may be standards-compliant signals, protocol signals, standardized radio protocol signals, control signals, data signals, radio communication network signals, cellular network signals, WiFi signals, LTE / 5G / 6G / 7G signals, reference signals, beacon signals, motion detection signals, and / or motion sensing signals. Probe signals may be formatted according to a radio network standard (e.g., WiFi), a cellular network standard (e.g., LTE / 5G / 6G), or another standard. Probe signals may include packets with a header and payload. Probe signals may have embedded data. The payload may include data. Probe signals may be replaced by data signals. Probe signals may be embedded in data signals. A radio receiver, a radio transmitter, another radio receiver, and / or another radio transmitter may be associated with at least one processor, a memory communicably connected to the individual processor, and / or an individual set of instructions stored in the memory, which, when executed, cause the processor to perform any and / or all steps required to determine the object's STI (e.g., motion information), initial STI, initial time, direction, instantaneous location, instantaneous angle, and / or velocity.

[0041] A processor, memory, and / or instruction set may be associated with a Type 1 device, one of at least one Type 2 device, an object, a device associated with an object, another device associated with a venue, a cloud server, a hub device, and / or another server.

[0042] A Type 1 device may broadcast signals to at least one Type 2 device through a channel within a venue. The signals are transmitted without the Type 1 device establishing a radio connection (e.g., association, authentication) with any Type 2 device, and without the Type 2 device requesting service from the Type 1 device. A Type 1 device may transmit to a specific Media Access Control (MAC) address common to two or more Type 2 devices. Each Type 2 device may adjust its MAC address to a specific MAC address. This specific MAC address may be associated with a venue. This association may be recorded in the association table of an association server (e.g., a hub device). A venue may be identified by a Type 1 device, a Type 2 device, and / or another device based on a specific MAC address, a sequence of probe signals, and / or at least one TSCI extracted from the probe signals.

[0043] For example, a Type 2 device may be moved to a new location within a venue (e.g., from another venue). A Type 1 device may be newly set up in a venue in such a way that Type 1 and Type 2 devices are unaware of each other. During setup, a Type 1 device may be instructed / guided / controlled to send a sequence of probe signals to a specific MAC address (e.g., using a dummy receiver, hardware pin configuration / connection, saved configuration, local configuration, remote configuration, downloaded configuration, hub device, or server). Upon power-up, a Type 2 device may scan for probe signals according to a table of MAC addresses (e.g., stored in a specified source, server, hub device, or cloud server) that can be used to broadcast across different locations (e.g., residential, office, enclosure, floor, multi-story building, shop, airport, mall, stadium, hall, station, subway, district, area, zone, region, local, city, country, continent). When a Type 2 device detects a probe signal sent to a specific MAC address, it can use the table to identify the venue based on that MAC address.

[0044] The location of a Type 2 device at a venue may be calculated based on a specific MAC address, a sequence of probe signals, and / or at least one TSCI obtained by the Type 2 device from the probe signals. This calculation may be performed by the Type 2 device.

[0045] A particular MAC address may change over time (e.g., adjust, change, modify). It may change according to time tables, rules, policies, modes, conditions, circumstances, and / or changes. A particular MAC address may be selected based on MAC address availability, a pre-selected list, collision patterns, traffic patterns, data traffic between a Type 1 device and another device, available bandwidth, random selection, and / or a MAC address switching plan. A particular MAC address may be the MAC address of a second wireless device (e.g., a dummy receiver, or a receiver that functions as a dummy receiver).

[0046] A Type 1 device may transmit a probe signal on a channel selected from a set of channels. At least one CI on the selected channel may be acquired by the respective Type 2 device from the probe signal transmitted on the selected channel.

[0047] The selected channel may change over time (e.g., adjust, change, modify). Such changes may be subject to time tables, rules, policies, modes, conditions, circumstances, and / or modifications. The selected channel may be chosen based on channel availability, random selection, a pre-selected list, same-channel interference, inter-channel interference, channel traffic patterns, data traffic between Type 1 devices and other devices, the effective bandwidth associated with the channel, security criteria, channel switching plans, standards, quality criteria, signal quality conditions, and / or considerations.

[0048] Information about a specific MAC address and / or selected channel may be transmitted between a Type 1 device and a server (e.g., a hub device) over a network. This information may further be transmitted between a Type 2 device and a server (e.g., a hub device) over another network. A Type 2 device may transmit information about a specific MAC address and / or selected channel to another Type 2 device (e.g., via a mesh network, Bluetooth, WiFi, NFC, ZigBee, etc.). The specific MAC address and / or selected channel may be selected by the server (e.g., a hub device). The specific MAC address and / or selected channel may be signaled on an announcement channel by a Type 1 device, a Type 2 device, and / or a server (e.g., a hub device). Any information may be pre-processed before communication takes place.

[0049] A wireless connection (e.g., association, authentication) can be established between a Type 1 device and another wireless device (e.g., using a signaling handshake). The Type 1 device may send a first handshake signal (e.g., a sounding frame, probe signal, request-to-send) to the other device. The other device may respond by sending a second handshake signal (e.g., a command, or clear-to-send) to the Type 1 device, triggering the Type 1 device to broadcast a signal (e.g., a sequence of probe signals) to multiple Type 2 devices without establishing a connection with the Type 2 devices. The second handshake signal may be a response or acknowledgment (e.g., ACK) to the first handshake signal. The second handshake signal may contain data containing information about the venue and / or the Type 1 device. The other device may be a dummy device with the purpose (e.g., primary purpose, secondary purpose) of establishing a wireless connection with the Type 1 device, receiving the first signal, and / or transmitting the second signal. Another device may be physically attached to the Type 1 device.

[0050] In another example, another device may send a third handshake signal to a Type 1 device to trigger it to broadcast a signal (e.g., a sequence of probe signals) to multiple Type 2 devices without another device establishing a connection (e.g., association, authentication) with any Type 2 device. The Type 1 device may respond to the third special signal by sending a fourth handshake signal to another device. Another device may be used to trigger two or more Type 1 devices for broadcasting. The triggers may be continuous, partially continuous, partially parallel, or fully parallel. Another device may have two or more radio circuits to trigger multiple transmitters in parallel. Parallel triggering may further be achieved using at least one yet another device to perform a trigger (similar to what another device does) in parallel with another device. After establishing a connection with a Type 1 device, the other device does not have to communicate with the Type 1 device (or may interrupt communication). Interrupted communication may be resumed. After establishing a connection with a Type 1 device, the other device may enter inactive mode, hibernation mode, sleep mode, standby mode, low power mode, off mode, and / or power down mode. Another device may have a specific MAC address so that the Type 1 device signals to that specific MAC address. The Type 1 device and / or the other device may be controlled and / or coordinated by a first processor relating to the Type 1 device, a second processor relating to the other device, a third processor relating to a specified source, and / or a fourth processor relating to the other device. The first and second processors may cooperate with each other.

[0051] A first sequence of probe signals may be transmitted by a first antenna of a Type 1 device to at least one first Type 2 device through a first channel in a first venue. A second sequence of probe signals may be transmitted by a second antenna of a Type 1 device to at least one second Type 2 device through a second channel in a second venue. The first and second sequences may or may not be different. The at least one first Type 2 device may / may not be different from the at least one second Type 2 device. The first and / or second sequences of probe signals may be broadcast without an established connection (e.g., association, authentication) between the Type 1 device and any Type 2 device. The first and second antennas may / may be the same.

[0052] The two venues may have different sizes, shapes, and multipath characteristics. The first and second venues may overlap. The respective surrounding areas around the first and second antennas may overlap. The first and second channels may be the same or different. For example, the first may be WiFi and the second may be LTE. Alternatively, both may be WiFi, but the first may be 2.4GHz WiFi and the second may be 5GHz WiFi. Alternatively, both may be 2.4GHz WiFi, but may have different channel numbers, SSID names, and / or WiFi settings.

[0053] Each Type 2 device may obtain at least one TSCI from its respective sequence of probe signals. This CI is for a separate channel between the Type 2 device and the Type 1 device. Some first Type 2 devices and some second Type 2 devices may be the same. The first and second sequences of probe signals may be synchronous / asynchronous. The probe signals may be transmitted with data or replaced by data signals. The first and second antennas may be the same.

[0054] A first sequence of probe signals may be transmitted at a first rate (e.g., 30 Hz). A second sequence of probe signals may be transmitted at a second rate (e.g., 200 Hz). The first and second rates may be the same or different. The first and / or second rates may be changed over time (e.g., adjusted, altered, modified). Changes may be subject to time tables, rules, policies, modes, conditions, circumstances, and / or modifications. Any rate may be changed over time (e.g., adjusted, altered, modified).

[0055] The first and / or second sequences of probe signals may be transmitted to the first MAC address and / or the second MAC address, respectively. The two MAC addresses may be the same or different. The first sequence of probe signals may be transmitted on the first channel. The second sequence of probe signals may be transmitted on the second channel. The two channels may be the same or different. The first or second MAC address and the first or second channel may change over time. Any changes may be subject to time tables, rules, policies, modes, conditions, circumstances, and / or modifications.

[0056] Type 1 devices and other devices may be controlled and / or coordinated, physically mounted, or be common devices / reside within common devices. They may be controlled / connected by a common data processor, or connected to a common bus interconnect / network / LAN / Bluetooth network / NFC network / BLE network / wired network / wireless network / mesh network / mobile network / cloud. They may share common memory or be associated with a common user, user device, profile, account, identification information (ID), identifier, home, house, physical address, location, geographic coordinates, IP subnet, SSID, home device, office device, and / or manufacturing device.

[0057] Each Type 1 device can be a signal source for its respective set of Type 2 devices (i.e., it transmits each signal (e.g., each sequence of probe signals) to its respective set of Type 2 devices). Each individual Type 2 device selects a Type 1 device from all Type 1 devices as its signal source. Each Type 2 device may be selected asynchronously. At least one TSCI may be obtained by each individual Type 2 device from a separate sequence of probe signals from a Type 1 device. This CI is a channel between the Type 2 and Type 1 devices.

[0058] An individual Type 2 device selects a Type 1 device from among all Type 1 devices as its signal source based on the Type 1 / Type 2 device identification information (ID) or identifier, the task being performed, past signal sources (e.g., history of past signal sources, Type 1 devices, other Type 1 devices, individual Type 2 receivers, and / or other Type 2 receivers), switching signal source thresholds, and / or user information, account, access information, parameters, characteristics, and / or signal strength (e.g., associated with Type 1 devices and / or individual Type 2 receivers).

[0059] Initially, a Type 1 device can be the signal source for an initial set of individual Type 2 devices (i.e., a Type 1 device sends individual signals (sequences of probe signals) to the initial set of individual Type 2 devices). Each of the initial individual Type 2 devices selects a Type 1 device from among all the Type 1 devices as its signal source.

[0060] A signal source (Type 1 device) of a particular Type 2 device may be modified (e.g., adjusted, changed, modified) if (1) the time interval between two adjacent probe signals received from the current signal source of the Type 2 device (e.g., between the current probe signal and the most recent probe signal, or between the next probe signal and the current probe signal) exceeds a first threshold, (2) the signal strength associated with the current signal source of the Type 2 device falls below a second threshold, (3) the processed signal strength associated with the current signal source of the Type 2 device falls below a third threshold, where the signal strength is processed by a low-pass filter, band-pass filter, median filter, moving average filter, weighted average filter, linear filter and / or nonlinear filter, and / or (4) the signal strength (or processed signal strength) associated with the current signal source of the Type 2 device falls below a fourth threshold for a significant percentage (e.g., 70%, 80%, 90%) of a recent time window. This percentage may exceed a fifth threshold. The first, second, third, fourth, and / or fifth thresholds may vary over time.

[0061] Condition (1) can occur when the Type 1 and Type 2 devices gradually move away from each other, resulting in some probe signals from the Type 1 device becoming too weak to be received by the Type 2 device. Conditions (2) to (4) can occur when the two devices move so far apart that the signal strength becomes very weak.

[0062] The signal source of a Type 2 device may not change if other Type 1 devices have a signal strength weaker than the coefficient of the current signal source (e.g., 1, 1.1, 1.2, or 1.5).

[0063] If a signal source is changed (e.g., adjusted, modified, or corrected), the new signal source may become effective in the near future (e.g., the next time after each). The new signal source may be a Type 1 device having the strongest signal strength and / or processed signal strength. The current and new signal sources may be the same or different.

[0064] A list of available Type 1 devices may be initialized and maintained by each Type 2 device. This list may be updated by checking the signal strength and / or processed signal strength associated with each set of Type 1 devices. A Type 2 device may select between a first sequence of probe signals from a first Type 1 device and a second sequence of probe signals from a second Type 1 device, based on individual probe signal rates, MAC addresses, channels, characteristics / features / states, tasks performed by the Type 2 device, the signal strengths of the first and second sequences, and / or other considerations.

[0065] A sequence of probe signals may be transmitted at a regular rate (e.g., 100 Hz). While the sequence of probe signals may also be scaled at regular intervals (e.g., 0.01 seconds per 100 Hz), each probe signal may experience small time perturbations due to timing requirements, timing control, network control, handshakes, message passing, collision avoidance, carrier sensing, congestion, resource availability, and / or other considerations.

[0066] The speed may be changed (e.g., adjusted, modified, corrected). Such changes may be subject to a timetable (e.g., changed every hour), rules, policies, modes, conditions, and / or modifications (e.g., changed whenever any event occurs). For example, the rate may normally be 100Hz, but may be changed to 1000Hz in demanding situations or to 1Hz in low-power / standby states. Probe signals may be transmitted in bursts.

[0067] The probe signal rate may vary based on the task performed by the Type 1 or Type 2 device (for example, a task may require 100Hz normally and 1000Hz instantaneously for 20 seconds). In one example, the transmitter (Type 1 device), receiver (Type 2 device), and associated tasks may be adaptively (and / or dynamically) associated with classes (e.g., low priority, high priority, urgent, critical, regular, privileged, non-subscription, subscription, paid, and / or non-paid classes). The rate (of the transmitter) may be adjusted for several classes (e.g., high priority classes). When the needs of that class change, the rate may be changed (e.g., adjusted, modified, corrected). If the receiver has very low power, the rate may be reduced to reduce the receiver's power consumption in response to the probe signal. In one example, the probe signal may be used to wirelessly transfer power to the receiver (Type 2 device), and the rate may be adjusted to control the amount of power transferred to the receiver.

[0068] The rate may be changed by (or based on) a server (e.g., a hub device), a Type 1 device, and / or a Type 2 device. Control signals may be communicated between them. The server may monitor, track, predict, and / or anticipate the need for tasks performed by the Type 2 device and / or the Type 1 device, and may control the Type 1 device to change the rate. The server may make scheduled changes to the rate according to a timetable. The server may detect an emergency and change the rate immediately. The server may detect an escalating condition and adjust the rate gradually.

[0069] Characteristics and / or STIs (e.g., motion information) may be monitored individually based on TSCIs associated with specific Type 1 devices and specific Type 2 devices, and / or jointly based on any TSCIs associated with specific Type 1 devices and any Type 2 devices, and / or jointly based on any TSCIs associated with specific Type 2 devices and any Type 1 devices, and / or globally based on any TSCIs associated with any Type 1 devices and any Type 2 devices. Any joint monitoring may be associated with users, user accounts, profiles, households, venue maps, venue environmental models, and / or user history, etc.

[0070] A first channel between a Type 1 device and a Type 2 device may differ from a second channel between another Type 1 device and another Type 2 device. The two channels may be associated with different frequency bands, bandwidths, carrier frequencies, modulation, radio standards, coding, encryption, payload characteristics, networks, network IDs, SSIDs, network characteristics, network settings, and / or network parameters, etc.

[0071] The two channels can be associated with different types of wireless systems (e.g., WiFi, LTE, LTE-A, LTE-U, 2.5G, 3G, 3.5G, 4G, Beyond 4G, 5G, 6G, 7G, cellular network standards, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA®, TD-SCDMA, 802.11 systems, 802.15 systems, 802.16 systems, mesh networks, Zigbee, NFC, WiMAX, Bluetooth, BLE, RFID, UWB, microwave systems, radar systems, etc.). For example, one could be WiFi and the other LTE.

[0072] The two channels may be associated with similar types of wireless systems but may be within different networks. For example, the first channel may be associated with a WiFi network called "Pizza and Pizza" in the 2.4GHz band with a 20MHz bandwidth, and the second channel may be associated with a WiFi network with the SSID "StarBud hotspot" in the 5GHz band with a 40MHz bandwidth. The two channels may be different channels within the same network (e.g., the "StarBud hotspot" network).

[0073] In one embodiment, a wireless monitoring system may include training a classifier of multiple events within a venue based on a training TSCI associated with multiple events. An event-associated CI or TSCI may be thought to include / constitute an event-associated wireless sample / characteristic / fingerprint (and / or venue, environment, object, object movement, state / emotional state / psychological state / situation / stage / gesture / walking / behavior / movement / activity / daily activity / history / object event, etc.).

[0074] For each of several known events occurring at a venue within individual training periods associated with a known event (e.g., survey, radio survey, initial radio survey), individual training radio signals (e.g., individual sequences of training probe signals) may be transmitted by a first type 1 heterogeneous radio device antenna to at least one first type 2 heterogeneous radio device at the venue via a radio multipath channel within the individual training period, using a processor, memory, and a set of instructions for a first type 1 device.

[0075] At least one separate time series of a training CI (training TSCI) may be acquired asynchronously from a (separate) training signal by each of at least one first type 2 device. The CI may be a channel CI between the first type 2 device and the first type 1 device during a training period associated with a known event. At least one training TSCI may be preprocessed. The training may be a radio survey (e.g., during the installation of the type 1 device and / or type 2 device).

[0076] For current events occurring at a venue during the current period, current radio signals (e.g., a sequence of current probe signals) may be transmitted by at least one second type 2 heterogeneous radio device and the antenna of a second type 1 heterogeneous radio device via a channel of the venue during the current period related to the current event, using a set of instructions from a processor, memory, and a second type 1 device.

[0077] At least one time series of the current CI (current TSCI) may be acquired asynchronously from the current signal (e.g., a series of current probe signals) by each of at least one second type 2 device. The CI may be the CI of the channel between the second type 2 device and the second type 1 device during the current period associated with the current event. At least one current TSCI may be preprocessed.

[0078] A classifier may be applied to classify at least one current TSCI acquired from a current sequence of probe signals by at least one second type 2 device, to classify at least one portion of a particular current TSCI, and / or to classify a combination of at least one portion of a particular current TSCI and another portion of another TSCI. The classifier may divide the TSCI (or characteristic / STI or other analytical values ​​or output responses) into clusters and associate these clusters with specific events / objects / targets / locations / movements / activities. Labels / tags may be generated for the clusters. The clusters may be stored and retrieved. The classifier may be applied to associate the current TSCI (or characteristic / STI or other analysis / output response, possibly related to the current event) with clusters, known / specific events, classes / categories / groups / groups / clusters / sets of known events / objects / locations / movements / activities, unknown events, classes / categories / groups / groups / lists / clusters / sets of unknown events / objects / locations / movements / activities, and / or other events / objects / locations / movements / activities / classes / categories / groups / groups / lists / clusters / sets. Each TSCI may contain at least one CI, each associated with its respective timestamp. Two TSCIs associated with two Type 2 devices may differ in terms of start time, duration, stop time, amount of CI, sampling frequency, and sampling period. Their CIs may have different characteristics. The first and second Type 1 devices may be in the same rocket within the venue. They may be the same device. At least one second Type 2 device (or their location) may replace at least one first Type 2 device (or their location). A specific second type 2 device and a specific first type 2 device may be the same device.

[0079] A subset of a first type 2 device and a subset of a second type 2 device can be the same. At least one second type 2 device and / or at least one subset of a second type 2 device can be at least one subset of a first type 2 device. At least one first type 2 device and / or at least one subset of a first type 2 device can be a replacement for at least one subset of a second type 2 device. At least one second type 2 device and / or at least one subset of a second type 2 device can be a replacement for at least one subset of a first type 2 device. At least one second type 2 device and / or at least one subset of a second type 2 device can be in the same respective locations as at least one subset of a first type 2 device. At least one first type 2 device and / or at least one subset of a first type 2 device can be in the same respective locations as at least one subset of a second type 2 device.

[0080] The antenna of a Type 1 device and the antenna of a second Type 1 device may be in the same location within the venue. The antennas of at least one second Type 2 device and / or at least one subset of second Type 2 devices may be in the same respective locations as each of the antennas of at least one subset of first Type 2 devices. The antennas of at least one first Type 2 device and / or at least one subset of first Type 2 devices may be in the same respective locations as each of the antennas of at least one subset of second Type 2 devices.

[0081] The first section of the first duration of the first TSCI and the second section of the second duration of the second section of the second TSCI can be aligned. A map can be calculated between the items of the first section and the items of the second section. The first section may include a first segment (e.g., a subset) of the first TSCI having a first start / end time, and / or another segment (e.g., a subset) of the processed first TSCI. The processed first TSCI may be the first TSCI processed by a first operation. The second section may include a second segment (e.g., a subset) of the second TSCI having a second start time and a second end time, and another segment (e.g., a subset) of the processed second TSCI. The processed second TSCI may be the second TSCI processed by a second operation. The first operation and / or the second operation may include subsampling, resampling, interpolation, filtering, transformation, feature extraction, preprocessing, and / or other operations.

[0082] The first item in the first section may be mapped to the second item in the second section. The first item in the first section may also be mapped to another item in the second section. Another item in the first section may also be mapped to the second item in the second section. The mapping may be one-to-one, one-to-many, many-to-one, or many-to-many. At least one of the following features may satisfy at least one constraint: the first item in the first section of the first TSCI, another item in the first TSCI, the timestamp of the first item, the time difference of the first item, the adjacent timestamp of the first item, another timestamp associated with the first item, the second item in the second section of the second TSCI, the timestamp of the second item, the time difference of the second item, the time difference of the second item, the adjacent timestamp of the second item, and another timestamp associated with the second item.

[0083] One constraint may be that the difference between the timestamp of the first item and the timestamp of the second item is upper bounded by an adaptive (and / or dynamically adjusted) upper threshold and lower bounded by an adaptive lower threshold.

[0084] The first section may be the entire first TSCI. The second section may be the entire second TSCI. The first duration may be equal to the second duration. The sections of a TSCI duration may be determined adaptively (and / or dynamically). A provisional section of a TSCI may be calculated. The start and end times of a section (e.g., provisional section, section) may be determined. This section may be determined by removing the start and end parts of the provisional section. The beginning of a provisional section may be determined as follows. Iteratively, items in a provisional section with incrementing timestamps may be considered the current item, which is one item at a time.

[0085] In each iteration, at least one activity measure / metric may be calculated and / or considered. At least one activity measure may be associated with at least one of the following: a current item associated with the current timestamp, a past item in the provisional section having a timestamp no greater than the current timestamp, and / or a future item in the provisional section having a timestamp no less than the current timestamp. The current item may be added to the beginning of the provisional section if at least one criterion (e.g., quality criterion, signal quality condition) associated with at least one activity measure is met.

[0086] At least one criterion related to the activity measure is (a) the activity measure is less than an adaptive (e.g., dynamically adjusted) upper threshold, (b) the activity measure is greater than an adaptive lower threshold, (c) the activity measure is continuously less than an adaptive upper threshold for at least a given number of consecutive timestamps, (d) the activity measure is continuously greater than an adaptive lower threshold for at least another given number of consecutive timestamps, (e) the activity measure is continuously less than an adaptive upper threshold for at least a given number of consecutive timestamps, or (f) the activity measure is continuously adaptive for at least another given number of consecutive timestamps. (g) an adaptive lower threshold is greater than (h) an adaptive lower threshold is greater than (i

[0087] The activity measure / index associated with an item at time T1 may comprise at least one of the following: (1) a first function of the item at time T1 and the item at time T1-D1, where D1 is a predetermined positive quantity (e.g., a constant time offset); (2) a second function of the item at time T1 and the item at time T1+D1; (3) a third function of the item at time T1 and the item at time T2, where T2 is a predetermined quantity (e.g., a fixed initial reference time; T2 may change over time (e.g., adjust, change, modify); T2 may be periodically updated; T2 may be the beginning of a period and T1 may be a sliding time in the period); and (4) a fourth function of the item at time T1 and another item.

[0088] At least one of the first, second, third, and / or fourth functions may be a function having at least two arguments X and Y (e.g., F(X,Y,...)). The two arguments may be scalars. The function (e.g., F) may be at least one of the functions X, Y, (XY), (YX), abs(XY), X^a, Y^b, abs(X^aY^b), (XY)^a, (X / Y), (X+a) / (Y+b), (X^a / Y^b), and ((X / Y)^ab), where a and b can be some predetermined quantities. For example, the function may simply be abs(XY) or (XY)^2, (XY)^4. The function may be a robust function. For example, the function may be (XY)^2 when abs(XY) is less than the threshold T, and (XY)+a when abs(XY) is greater than T. Alternatively, the function may be a constant when abs(XY) is greater than T. The function may also be constrained by a slowly increasing function when abs(Xy) is greater than T, so that outliers cannot seriously affect the result. Another example of the function may be (abs(X / Y)-a), where a=1. In this way, when X=Y (i.e., no change or activity), the function gives a value of 0. When X is greater than Y, (X / Y) is greater than 1 (if X and Y are positive), and the function is positive. When X is less than Y, (X / Y) is less than 1, and the function is negative. In another example, the function may be at least one of the following functions: X=(X_1-X_2-...-Y_1-...-Y_n), X_i, (Y_i), abs_X_i-Y_i, X_i^b, abs_X_i^a-Y_i^b, (X_i-Y_i)^a, (X_i+a) / (Y_i+b), (X_i^a / Y_i^b), and ((X_i / Y_i)^ab), where i is an n-tuple X and Y, and 1≦i≦n, for example, the component index of X_1 is i=1 and the component index of X_2 is i=2.The function may include a component-wise sum of at least one other function from among X_i, Y_i, (Y_i-i), (X_i-Y_i), X_i, Y_i^b, abs_X_i^a-Y_i^b, (X_i-Y_i)^a, (X_i+a) / (Y_i +b), (X_i^a / Y_i^b), and ((X_i / Y_i)^ab), where i is the component index of the n tuples X and Y. The function may be of the form sum_{i=1}^n(abs(X_i / Y_i)-1) / n, or sum_{i=1}^n w_i*(abs(X_i / Y_i)-1), where w_i is some weight for component i.

[0089] Maps can be computed using dynamic time warping (DTW). DTW may have constraints on the map, items of the first TSCI, items of the second TSCI, the first duration, the second duration, the first section, and / or the second section. Suppose in the map that the i-th region item maps to the j-th range item. The constraints may be acceptable combinations of i and j (constraints on the relationship between i and j). The mismatch cost between the first section of the first duration of the first TSCI and the second section of the second duration of the second TSCI can be computed.

[0090] The first and second sections may be aligned such that a map containing two or more links can be established between a first item in the first TSCI and a second item in the second TSCI. For each link, one of the first items having a first timestamp can be associated with one of the second items having a second timestamp. A mismatch cost can be calculated between the aligned first section and the aligned second section. The mismatch cost may have the functionality of an item-wise cost between the first and second items associated by a particular link in the map, and a link-wise cost associated with a particular link in the map.

[0091] The aligned first section and the aligned second section can be represented as the first and second vectors, respectively, having the same vector length. The mismatch cost may include at least one of the following: dot product, dot product quantifier, correlation-based quantity, correlation index, covariance-based quantity, discrimination score, distance, Euclidean distance, absolute distance, Lk distance (e.g., L1, L2, ...), weighted distance, distance quantifier, and / or another similarity value between the first and second vectors. The mismatch cost can be normalized by the respective vector lengths.

[0092] The parameters derived from the mismatch cost between the first section of the first duration of the first TSCI and the second section of the second duration of the second TSCI can be modeled using a statistical distribution. At least one of the scale parameter, location parameter, and / or other parameters of the statistical distribution can be estimated.

[0093] The first section of the first duration of the first TSCI may be a sliding section of the first TSCI. The second section of the second duration of the second TSCI may be a sliding section of the second TSCI.

[0094] A first sliding window may be applied to a first TSCI, and a corresponding second sliding window may be applied to a second TSCI. The first sliding window of the first TSCI and the corresponding second sliding window of the second TSCI can be aligned.

[0095] The mismatch cost between the aligned first sliding window of the first TSCI and the corresponding aligned second sliding window of the second TSCI can be calculated. Based on the mismatch cost, the current event can be associated with at least one of the known event, the unknown event, and / or another event.

[0096] The classifier may be applied to at least one of the first sections of the first duration of the first TSCI and / or the second sections of the second duration of the second TSCI to obtain at least one provisional classification result. Each provisional classification result may be associated with its respective first section and its respective second section.

[0097] The current event can be associated with at least one of the following based on mismatch cost: a known event, an unknown event, a class / category / group / grouping / list / unknown event, and / or another event. The current event can also be associated with at least one of the following based on the maximum number of provisional classification results corresponding to two or more sections of the first TSCI and two or more sections of the second TSCI. For example, the current event can be associated with a specific known event if its mismatch cost points to that specific known event N times in a row (e.g., N=10). In another example, the current event can be associated with a specific known event if the percentage of mismatch costs in the most recent N consecutive instances pointing to that specific known event exceeds a certain threshold (e.g., >80%).

[0098] In another example, the current event may be associated with a known event that achieves the minimum mismatch cost for most of the time within a period. The current event may be associated with a known event that achieves the minimum overall mismatch cost by a weighted mean of at least one mismatch cost associated with at least one first section. The current event may be associated with a specific known event that achieves the minimum of another overall cost. If there are no known events that achieve a mismatch cost lower than the first threshold T1 for a sufficient percentage of at least one first section, the current event may be associated with an “unknown event”. The current event may also be associated with an “unknown event” if no event achieves an overall mismatch cost lower than the second threshold T2. The current event may be associated with at least one of known events, unknown events, and / or other events based on the mismatch costs and additional mismatch costs associated with at least one additional section of the first TSCI and at least one additional section of the second TSCI. Known events may include at least one of the following: door closing events, door opening events, window closing events, window opening events, multi-state events, on-state events, off-state events, intermediate-state events, continuous-state events, discrete-state events, human presence events, human absence events, signs of biological presence events, and / or signs of biological absence events.

[0099] The projection for each CI can be trained using a dimensionality reduction method based on the trained TSCI. The dimensionality reduction method may include at least one of the following: principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher linear discriminant analysis, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or other methods. The projection can be applied to at least one of the trained TSCI associated with at least one event, and / or the current TSCI, for the classifier.

[0100] A classifier for at least one event may be trained based on a projection and a trained TSCI associated with at least one event. At least one current TSCI may be classified / classified based on the projection and the current TSCI. The projection may be retrained using a dimensionality reduction method and at least one of other dimensionality reduction methods, based on at least one of the trained TSCI, at least one current TSCI before retraining the projection, and / or additional trained TSCIs. Other dimensionality reduction methods may include at least one of principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher linear discriminant, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or yet another method. A classifier for at least one event may be retrained based on the retrained projection, a trained TSCI associated with at least one event, and / or at least one current TSCI. At least one current TSCI can be classified based on a retrained projection, a retrained classifier, and / or the current TSCI.

[0101] Each CI may contain a vector of complex values. Each complex value may be preprocessed to give a magnitude for the complex value. Each CI may be preprocessed to give a vector of non-negative real numbers containing the magnitudes of the corresponding complex values. Each training TSCI may be weighted in training the projection. A projection may contain two or more projection components. A projection may contain at least one top-level projection component. A projection may contain at least one projection component that may be useful to the classifier.

[0102] Channel / Channel information / Venue / Spatiotemporal information / Motion / Object

[0103] Channel information (CI) may be associated with / include the following: Signal strength, signal amplitude, signal phase, spectral power measurement, modem parameters (e.g., used in relation to modulation / demodulation in digital communication systems such as WiFi, 4G / LTE), dynamic beamforming information (e.g., including feedback or steering matrices generated by wireless communication devices according to standardized processes such as IEEE 802.11 or other standards), transfer function components, radio state (e.g., used in digital communication systems to decode digital data), measurable variables, sensing data, coarse / fine granular information of layers (e.g., physical layer, data link layer, MAC layer, etc.), digital settings, gain settings, RF filter settings, RF front-end switch settings, DC offset settings, DC correction settings, IQ compensation settings, effect on the radio signal due to the environment during propagation (e.g., venue), input signal Conversion of (radio signals transmitted by Type 1 devices) to output signals (radio signals received by Type 2 devices), stable behavior of the environment, state profile, radio channel measurements, received signal strength indicator (RSSI), channel state information (CSI), channel impulse response (CIR), channel frequency response (CFR), characteristics of frequency components (e.g., subcarriers) in bandwidth, channel filter response, timestamp, auxiliary information, data, metadata, user data, account data, access data, security data, session data, status data, supervisory data, home data, identity (ID), device data, network data, neighbor data, environmental data, real-time data, sensor data, stored data, encrypted data, compressed data, protected data, and / or other channel information. Each CI may be associated with a timestamp and / or arrival time. CSI may be used to equalize / cancel / minimize / reduce the multipath channel effect (of the transmit channel) in order to demodulate a signal similar to the signal transmitted by the transmitter over a multipath channel. CI may be associated with information relating to the frequency bandwidth, frequency signature, frequency phase, frequency amplitude, frequency trend, frequency characteristics, frequency similarity characteristics, time-domain elements, frequency-domain elements, time-frequency-domain elements, orthogonal resolution characteristics, and / or non-orthogonal resolution characteristics of the signal passing through the channel. TSCI may be a stream of radio signals (e.g., CI).

[0104] CI can be pre-processed, processed, post-processed, stored (e.g., in local memory, portable / mobile memory, removable memory, storage network, cloud memory, in a volatile or non-volatile manner), retrieved, transmitted, and / or received. One or more modem parameters and / or radio state parameters can be kept constant. Modem parameters can be applied to radio subsystems. Modem parameters can represent radio states. Motion detection signals (e.g., baseband signals, and / or packets decoded / demodulated from baseband signals, etc.) can be obtained by processing (e.g., down-converting) a first radio signal (e.g., RF / WiFi / LTE / 5G signal) by the radio subsystem using the radio state represented by the stored modem parameters. Modem parameters / radio states can be updated (e.g., using previous modem parameters or previous radio states). Both previous modem parameters / radio states and updated modem parameters / radio states can be applied to radio subsystems in a digital communication system. Both previous modem parameters / wireless status and updated modem parameters / wireless status can be compared / analyzed / processed / monitored in the task.

[0105] Channel information may also be modem parameters (e.g., stored or newly calculated) used to process the radio signal. The radio signal may contain multiple probe signals. Two or more probe signals may be processed using the same modem parameters. Two or more radio signals may also be processed using the same modem parameters. Modem parameters may include parameters that indicate the settings or overall configuration for the operation of the radio subsystem or baseband subsystem (or both) of the radio sensor device. Modem parameters may include one or more of the following for the radio subsystem: gain settings, RF filter settings, RF front-end switch settings, DC offset settings, or IQ compensation settings, or digital DC correction settings, digital gain settings, and / or digital filtering settings (e.g., for the baseband subsystem). CI may also be associated with information relating to the signal's duration, time signature, timestamp, time amplitude, time phase, time trend, and / or time characteristics. CI may be associated with information relating to the signal's time-frequency segmentation, signature, amplitude, phase, trend, and / or characteristics. CI may be associated with signal decomposition. A CI can be associated with information relating to the direction of the signal passing through the channel, the angle of arrival (AoA), the angle of the directional antenna, and / or the phase. A CI can be associated with the attenuation pattern of the signal passing through the channel. Each CI can be associated with a Type 1 device and a Type 2 device. Each CI can be associated with the antenna of a Type 1 device and the antenna of a Type 2 device.

[0106] CI can be obtained from communication hardware capable of providing CI (e.g., a Type 2 device or a Type 1 device). Communication hardware may include WiFi-enabled chips / ICs (integrated circuits), chips compliant with 802.11 or 802.16 or other wireless / wireless standards, next-generation WiFi-enabled chips, LTE-enabled chips, 5G-enabled chips, 6G / 7G / 8G-enabled chips, Bluetooth-enabled chips, NFC-enabled chips, BLE-enabled chips, UWB chips, and other communication chips (e.g., Zigbee, WiMAX, mesh networks). The communication hardware calculates the CI, stores it in buffer memory, and makes it available for extraction. The CI may comprise data and / or at least one matrix related to channel state information (CSI). At least one matrix may be used for channel equalization and / or beamforming, etc. A channel may be associated with a venue. Attenuation can be due to signal propagation in the venue, signal propagation through / around the air (e.g., the air in the venue), reflection, refraction, diffraction, refracting media / reflective surfaces such as walls, doors, furniture, obstacles and / or barriers. Attenuation can be due to reflection on surfaces and obstacles (e.g., reflective surfaces, obstacles) such as floors, ceilings, furniture, fixtures, objects, people, pets, etc. Each CI may be associated with a timestamp. Each CI may contain N1 components (e.g., N1 frequency-domain components in a CFR, N1 time-domain components in a CIR, or N1 decomposed components). Each component may be associated with a component index. Each component may be a real, imaginary, or complex number, magnitude, phase, flag, and / or set. Each CI may comprise a vector or matrix of complex numbers, a set of mixed quantities, and / or at least one multidimensional set of complex numbers.

[0107] The components of TSCI associated with a specific component index may form their own component time series, each associated with its respective index. TSCI can be divided into N1 component time series. Each component time series is associated with its respective component index. The motion characteristics / STI of an object may be monitored based on the component time series. In one example, one or more ranges of CI components (e.g., one range from component 11 to component 23, a second range from component 44 to component 50, and a third range having only one component) may be selected for further processing based on some criteria / cost function / signal quality metric (e.g., based on signal-to-noise ratio and / or interference level).

[0108] The component-specific properties of the TSCI time series can be calculated. These component-specific properties may be scalars (e.g., energy) or functions with domains and ranges (e.g., autocorrelation function, transformation, inverse transformation). The motion characteristics / STI of an object can be monitored based on the component-specific properties. The overall properties of the TSCI (e.g., overall properties) can be calculated based on the component-specific properties of each component time series of the TSCI. The overall properties may be a weighted average of the component-specific properties. The motion characteristics / STI of an object may be monitored based on the overall properties. The total may be a weighted average of the individual quantities.

[0109] Type 1 and Type 2 devices may support WiFi, WiMAX, 3G and beyond, 4G / 4G, LTE, LTE-A, 5G, 6G, 7G, Bluetooth, NFC, BLE, Zigbee, UWB, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, mesh networks, proprietary wireless systems, IEEE 802.11, 802.15, 802.16, 3GPP standards, and / or other wireless systems.

[0110] A common radio system and / or common radio channel may be shared by a Type 1 transceiver and / or at least one Type 2 transceiver. At least one Type 2 transceiver may use the common radio system and / or common radio channel to transmit their respective signals simultaneously (or asynchronously, synchronously, sporadically, continuously, repeatedly, simultaneously, concurrently, and / or temporarily). A Type 1 transceiver may use the common radio system and / or common radio channel to transmit signals to at least one Type 2 transceiver.

[0111] Each Type 1 and Type 2 device may have at least one transmit / receive antenna. Each CI may be associated with one of the transmit antennas of a Type 1 device and one of the receive antennas of a Type 2 device. Each pair of transmit and receive antennas may be associated with a link, path, communication path, signal hardware path, etc. For example, if a Type 1 device has M (e.g., 3) transmit antennas and a Type 2 device has N (e.g., 2) receive antennas, there may be M x N (e.g., 3 x 2 = 6) links or paths. Each link or path may be associated with a TSCI.

[0112] At least one TSCI can correspond to various antenna pairs between a Type 1 device and a Type 2 device. A Type 1 device may have at least one antenna. A Type 2 device may also have at least one antenna. Each TSCI can be associated with the antennas of a Type 1 device and a Type 2 device. Averaging or weighted averaging can be performed across the antenna links. Averaging or weighted averaging can be performed across at least one TSCI. Averaging can optionally be performed over a subset of at least one TSCI corresponding to a subset of antenna pairs.

[0113] The timestamps of some CIs in a TSCI may be irregular and may be corrected so that the corrected timestamps of time-corrected CIs are spaced uniformly in time. For multiple Type 1 devices and / or multiple Type 2 devices, the corrected timestamps may relate to the same or different clocks. An original timestamp associated with each CI may be determined. The original timestamps may not be spaced uniformly in time. The original timestamps of all CIs in a particular portion of a particular TSCI within the current sliding time window may be corrected so that the corrected timestamps of time-corrected CIs are spaced uniformly in time.

[0114] Characteristics and / or STI (e.g., motion information) include: Position, location, changed position, new position, new position, position, vertical position, distance, distance, movement, acceleration, acceleration, rotational speed, acceleration, direction of motion, azimuth angle, rotation, direction of motion, rotation, path, deformation, contraction, expansion, walking, expansion, walking, periodic motion, head movement, repetitive motion, periodic motion, pseudo-periodic motion, impact motion, sudden motion, falling motion, falling motion, transient motion, transient behavior, period of motion, frequency of motion, temporal profile, temporal characteristics, temporal characteristics, occurrence, change, temporal change, change in CI, change in frequency, change in timing, change in walking cycle, change in timing, walking cycle Changes in period, timing, start time, start time, end time, duration, exercise history, exercise type, exercise classification, frequency, frequency spectrum, object composition, object composition, approach, approach, identification, approach, approach, head movement velocity, head movement, respiratory rate, respiratory rate, respiratory time, breathing depth, exhalation time, inhalation time, exhalation time, exhalation time, ventilation time, ventilation interval, heart rate variability, hand movement direction, hand movement, leg movement, walking speed, hand movement velocity, hand movement velocity, positional characteristics, characteristics related to object movement (e.g., position / change of position), tool movement, machine movement, complex movement, and / or multiple Number motion combinations, events, signal statistics, signal dynamics, anomalies, motion statistics, motion parameters, motion instructions, motion magnitude, motion phase, similarity score, distance score, Euclidean distance, weighted distance, L_1 norm, L_2 norm, L_k norm for k>2, statistical distance, correlation, correlation indicator, autocorrelation, autocovariance, cross-covariance, inner product, cross product, motion signal transformation, motion features, presence of motion, absence of motion, motion localization, motion identification, motion recognition, presence of objects, absence of objects, entry and exit of objects, change of objects, motion cycles, motion counts, walking cycles Movement cycle, movement rhythm, movement, movement rhythm, deformation movement, gesture, draft, head movement, mouth movement, heart movement, visceral movement, movement tendency, size, volume, volume, shape, shape, tag, start / start position, end position, start / start amount, end amount, event, fall event, security event, accident event, home event, office event, factory event, warehouse event, manufacturing event, line assembly event, maintenance event, car-related event, navigation event, event tracking event, door event, door open event, door close event,Window events, window open events, window close events, repeatable events, one-time events, consumption, unconsumption, state, physical state, health state, well-being state, emotional state, mental state, other events, analysis, output response, and / or other information. Characteristics and / or STI may be calculated / monitored based on features calculated from CI or TSCI (e.g., feature calculation / extraction). Static segments or profiles (and / or dynamic segments / profiles) may be identified / calculated / analyzed / monitored / extracted / acquired / recovered / marked / presented / highlighted / stored / communicated based on feature analysis. Analysis may include motion detection / motion evaluation / presence detection. Computational workloads may be shared between Type 1 devices, Type 2 devices, and other processors.

[0115] Type 1 devices and / or Type 2 devices may be local devices. Local devices may include smartphones, smart devices, TVs, set-top boxes, access points, routers, wireless repeaters, repeaters, routers, repeaters, wireless signal repeater / extenders, speakers, fans, refrigerators, fans, microwaves, ovens, coffee machines, hot water pots, tables, chairs, lighting, lamps, door locks, cameras, motion sensors, motion sensors, fire hydrants, garage doors, switches, power adapters, computers, dongles, computers, dongles, electronic pads, sofas, tiles, accessories, home devices, vehicle devices, office devices, building devices, manufacturing devices, clocks, clocks, televisions, ovens, air conditioners, accessories, utilities, appliances, smart machines, smart vehicles, internet-enabled devices, computers, portable computers, tablets, smart homes, smart offices, smart parking lots, smart systems, and / or other devices.

[0116] Each Type 1 device may be associated with its own identifier (e.g., ID). Each Type 2 device may also be associated with its own identity (ID). An ID may include a code, a combination of text and codes, a name, a password, an account, an account ID, a web link, a web address, an index to some information, and / or another ID. An ID may be assigned. An ID may be assigned by hardware (e.g., hardwired, via a dongle and / or other hardware), software and / or firmware. An ID may be retrieved (e.g., in a database, in memory, on a server (e.g., a hub device), in the cloud, stored locally, stored remotely, stored permanently, stored temporarily). An ID may be associated with at least one record, account, user, household, address, telephone number, social security number, customer number, another ID, another identifier, timestamp, and / or data collection. The ID and / or parts of the ID of a Type 1 device may be made available to a Type 2 device. IDs may be used by Type 1 devices and / or Type 2 devices for registration, initialization, communication, identification, verification, detection, recognition, authentication, access control, cloud access, networking, social networking, logging, recording, cataloging, classification, tagging, association, pairing, transactions, electronic transactions, and / or intellectual property management.

[0117] Objects may include people, users, objects, passengers, children, the elderly, babies, sleeping babies, babies in cars, patients, workers, high-value workers, professionals, experts, waiters, customers in shopping malls, travelers at airports / stations / bus terminals / shipping terminals, factory / mall / supermarket / office / workplace staff / workers / customer service personnel, service personnel in sewage / ventilation systems / liftwells, lifts in liftwells, elevators, prisoners, subjects of tracking / monitoring, animals, plants, living things, pets, dogs, cats, smartphones, phone accessories, computers, tablets, mobile computers, dongles, computer accessories, network devices, WiFi devices, IoT devices, smartwatches, smart glasses, smart devices, speakers, keys, smart keys, wallets, handbags, backpacks, goods, cargo, luggage, equipment, motors, machinery, air conditioners, fans, HVAC equipment, and lighting fixtures. It may be a movable light fixture, television, camera, audio-visual equipment, stationary equipment, monitoring equipment, parts, signs, tools, carts, tickets, parking tickets, toll road tickets, airline tickets, credit cards, plastic cards, access cards, food packaging, cooking utensils, tables, chairs, cleaning equipment and tools, vehicles, automobiles, vehicles in parking lots, goods in warehouses, stores, supermarkets, and distribution centers, boats, bicycles, aircraft, drones, remote-controlled cars, airplanes, and boats, remote-controlled airplanes and boats, remote-controlled cars, airplanes, and boats, remote-controlled airplanes and boats, remote-controlled airplanes and boats, drones, drones, drones, drones, remote-controlled airplanes, airplanes, and boats, remote-controlled cars, airplanes, and boats, remote-controlled cars / airplanes / boats, robots, manufacturing equipment, assembly lines, materials / unfinished parts / robots / carts / transports in factories, tracked objects in airports / shopping marts / supermarkets, non-objects, the absence of an object, the presence of an object, objects with form, objects that change shape, objects without form, the mass of a liquid, the mass of a gas / smoke, fire, flame, electromagnetic (EM) source, electromagnetic medium, and / or other object.

[0118] The object itself may be connected to several networks, such as WiFi, MiFi, 3G / 4G / LTE / 5G / 6G / 7G, Bluetooth, NFC, BLE, WiMAX, Zigbee, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, mesh networks, ad-hoc networks, and / or other networks. The object itself may be bulky due to AC power, but it is moved during installation, cleaning, maintenance, and repair. It may also be installed on a movable platform such as a lift, pad, movable platform, elevator, conveyor belt, robot, drone, forklift, cage, boat, or vehicle. The object may have multiple parts, each part having different movements (e.g., position / change of position). For example, the object may be a person walking forward. While walking, his left and right hands may move in different directions with different instantaneous velocities, accelerations, movements, etc.

[0119] A radio transmitter (e.g., a Type 1 device), a radio receiver (e.g., a Type 2 device), another radio transmitter, and / or another radio receiver may move with an object and / or another object (e.g., in previous movements, current movements, and / or future movements). They may be communicatively coupled to one or more nearby devices. They may transmit TSCI and / or information related to TSCI to and / or to nearby devices. They may be together with nearby devices. A radio transmitter and / or radio receiver may be part of a small (e.g., coin-sized, cigarette pack-sized, or even smaller), lightweight portable device. The portable device may be wirelessly coupled to a nearby device.

[0120] Nearby devices may include smartphones, iPhones®, Android phones, smart devices, smart home appliances, smart vehicles, smart gadgets, smart TVs, smart refrigerators, smart speakers, smartwatches, smart glasses, smart pads, iPads®, computers, wearable computers, notebook computers, and gateways. Nearby devices may connect to cloud servers, local servers (e.g., hub devices), and / or other servers via the Internet, wired internet connections, and / or wireless internet connections. Nearby devices may also be portable. Portable devices, nearby devices, local servers (such as hub devices), and / or cloud servers can share computations and / or memory for tasks (e.g., acquiring TSCI, determining object properties / STI related to object movement (e.g., position / change of position), computation of time series of power (e.g., signal strength) information, determination / computation of specific functions, searching for local extrema, classification, identification of specific values ​​of time offset, denoising, processing, simplification, cleaning, wireless smart sensing tasks, CI extraction from signals, switching, segmentation, estimated trajectories / paths / trajectories, map processing, processing of trajectories / paths / trajectories based on environmental models / constraints / limits, modification, correction, adjustment, map-based (or model-based) modification, error detection, boundary hit, thresholding) and information. Nearby devices may / may not move with the object. Nearby devices may / may not move with the object. Nearby devices may / may not be portable / non-portable / movable / not movable. Nearby devices may use battery power, solar power, AC power, and / or other power sources. Nearby devices may have replaceable / non-replaceable batteries and / or rechargeable / non-rechargeable batteries. Nearby devices may be similar to the object. Nearby devices may have the same (and / or similar) hardware and / or software as the object.Nearby devices may be smart devices, network-enabled devices, devices with connectivity to WiFi / 3G / 4G / 5G / 6G / Zigbee / Bluetooth / NFC / UMTS / 3GPP / GSM / EDGE / TDMA / FDMA / CDMA / WCDMA / TD-SCDMA / ad-hoc networks / other networks, smart speakers, smartwatches, smart clocks, smart appliances, smart machines, smart devices, smart tools, smart vehicles, internet-enabled devices, computers, portable computers, tablets, and other devices. Nearby devices and / or a wireless receiver, a wireless transmitter, another wireless receiver, another wireless transmitter, and / or at least one processor associated with a cloud server (in the cloud) may determine the initial STI of an object. Two or more of them may together determine the initial spatial-temporal information. Two or more of them may share intermediate information in determining the initial STI (e.g., initial position).

[0121] In one example, a wireless transmitter (e.g., a Type 1 device, or TrackerBot) may move with the object. The wireless transmitter may send a signal to a wireless receiver (e.g., a Type 2 device, or Origin Register) or determine the object's initial STI (e.g., initial position). The wireless transmitter may also send signals and / or other signals to another wireless receiver (e.g., another Type 2 device, or another Origin Register) to monitor the object's movement (spatial-temporal information). The wireless receiver may also receive signals and / or other signals from the wireless transmitter and / or other wireless transmitters to monitor the object's movement. The positions of the wireless receiver and / or other wireless receivers may be known. In another example, a wireless receiver (e.g., a Type 2 device, or TrackerBot) may move with the object. The wireless receiver may receive signals transmitted from the wireless transmitter (e.g., a Type 1 device, or Origin Register) to determine the object's initial spatial-temporal information (e.g., initial position). The wireless receiver may also receive signals and / or other signals from another wireless transmitter (e.g., another Type 1 device or another Origin registration) to monitor the object's current movement (e.g., spatial-temporal information). The wireless transmitter may also transmit signals and / or other signals to the wireless receiver and / or another wireless receiver (e.g., another Type 2 device or another Tracker Bot) to monitor the object's movement. The locations of the wireless transmitter and / or other wireless transmitters may be known.

[0122] Venue is a sensing area, sensing area, room, house, office, property, workplace, corridor, passage, lift, liftwell, escalator, elevator, sewer, ventilation system, staircase, gathering venue, duct, air duct, pipe, tube, enclosed space, closed structure, semi-enclosed structure, enclosed area, area with at least one wall, plant, machine, engine, wooden structure, glass structure, metal structure, structure with walls, structure with doors, structure with gaps, structure with reflective surfaces, structure with fluid, building, rooftop, shop, factory, assembly line, hotel room, museum, classroom, school, university, government building, warehouse, garage, mall, airport, station, bus terminal, hub, transportation hub, transportation terminal, government facility, public facility, school, university, entertainment facility. Entertainment facilities, hospitals, pediatric and neonatal wards, nursing homes, elderly care facilities, community centers, stadiums, parks, grounds, sports facilities, swimming facilities, athletic fields, basketball courts, tennis courts, soccer stadiums, baseball fields, gymnasiums, halls, garages, shopping malls, supermarkets, manufacturing facilities, parking facilities, construction sites, mining facilities, etc. Transportation facilities, highways, roads, valleys, forests, timber, topography, landscapes, studies, courtyards, land, paths, amusement parks, urban areas, rural areas, suburbs, metropolitan areas, gardens, squares, music halls, city center facilities, aerial facilities, semi-open facilities, enclosed spaces, stations, logistics centers, warehouses, shops, storage facilities, underground facilities, spaces (e.g., outdoor spaces above ground), indoor facilities, outdoor facilities, outdoor facilities with walls, doors, and reflectors, open facilities, semi-open facilities, automobiles, trucks, buses, vans, containers, ships / boats, submarines, trains, trams, airplanes, vehicles, mobile dwellings, caves, tunnels, pipes, waterways, metropolitan areas, etc. The venue can be a space such as a busy commercial area with relatively tall buildings, a valley, a well, a duct, a passageway, a gas pipe, an oil pipe, a water pipe, interconnected passages / passages / roads / pipes / caves / pipe-like structures / air spaces / fluid spaces, a human body, an animal body, a body cavity, an organ, a bone, a tooth, a soft tissue, a hard tissue, a rigid tissue, a non-rigid tissue, a blood / body fluid container, a vent, an air duct, etc. The venue may be an indoor space or an outdoor space, and may include both the inside and outside of the space. For example, the venue may include both the inside and outside of a building. For example, the venue may be a building with one or more floors, and part of the building may be underground.The shape of the building may be, for example, circular, square, rectangular, triangular, or irregular. These are merely examples. This disclosure may be used to detect events in other types of venues or spaces.

[0123] A wireless transmitter (e.g., a Type 1 device) and / or a wireless receiver (e.g., a Type 2 device) may be embedded in a portable device (e.g., a module, or a device with a module) that can move with an object (e.g., in previous and / or current movements). The portable device may be connected via wired connections (e.g., USB, microUSB, Firewire, HDMI®, serial port, parallel port, and other connectors) and / or via wireless connections (e.g., Bluetooth, Bluetooth Low Energy (BLE)). The portable device may be a lightweight device. The portable device may be powered by a rechargeable battery, a rechargeable battery, and / or AC power. The portable device may be very small (e.g., on a sub-millimeter and / or sub-centimeter scale) and / or small (e.g., coin-sized, card-sized, pocket-sized, or larger). The portable device may be large, large, and / or large (e.g., heavy machinery to be installed).Portable devices include WiFi hotspots, mobile WiFi (MiFi), access points / micro USB / smartphones, tablets, computers, smart devices, WiFi-enabled devices, LTE-enabled devices, smart mirrors, smart batteries, smart lights, smart pens, smart rings, smart doors, smart clocks, smart belts, smart handbags, smart clothing, smart packaging, smart paper / books / magazines / printed materials / signs / displays / lighting systems, smart keys / tools, smart bracelets / chains / necklaces / wearables / accessories, smart pads / cushions / blocks, bricks / building materials, smart trash cans, smart food carriages / storage, smart balls / rackets, smart chairs / sofas / beds, smart shoes / carpets / mats / hand hats / handwear, smart hats / makeup / stickers / tattoos, smart mirrors, smart pills, smart pills, smart This may also include bottles / food containers, smart devices, IoT devices, WiFi-enabled devices, 3G / 4G / 6G-enabled devices, UMTS devices, 3GPP devices, EDGE devices, TDMA devices, CDMA devices, WCDMA devices, embedded devices, air conditioners, refrigerators, furnaces, ovens, cooking devices, televisions / set-top boxes (STBs) / DVD players / video players / remote controls, hi-fi, audio devices, speakers, lighting, doors, roofs / roofs / structures / equipment / installation / lawnmowers / garage tools, machinery / garage cans / 40ft / containers, 20ft / garage containers, factory / manufacturing equipment, repair tools, factory / production tools, machinery, machinery, machinery, vehicles, carts, wagons, warehouses, vehicles, automobiles, bicycles, boats, boats, baskets / boxes / buckets / containers, smart plates / cups / bowls / pots / mats / cooking utensils / kitchen tools / kitchenware / cabinets / tables / chairs / tiles / lighting / water pipes / faucets / gas ranges / ovens / dishwashers / etc. Portable devices may have batteries that are replaceable, non-replaceable, rechargeable, and / or non-rechargeable. Portable devices may be wirelessly charged.The portable device may be a smart payment card. The portable device may also be a payment card used in parking lots, highways, entertainment parks, or other places / facilities where payment is required. As described above, the portable device may have an identity (ID) / identifier.

[0124] Events can be monitored based on TSCI. Events can include objects (e.g., people and / or sick people) falling, spinning, stopping, or impacting (e.g., punching bags, doors, beds, chairs, tables, desks, cabinets, boxes, other people, animals, birds, tables, flies, tables, chairs, balls, bowling balls, tennis balls, soccer balls, baseballs, basketballs, volleyballs), the movement of two people's bodies (e.g., one person releasing a balloon, one person catching a fish, one person shaping clay, one person writing on paper, one person typing on a computer), a car moving around a garage, a person walking around an airport / mall / government / building / office / etc. with a smartphone, or an autonomously mobile object / machine moving around (e.g., vacuum cleaner, utility car, automobile, drone, self-driving car).

[0125] Tasks or wireless smart sensing tasks may include: object detection, presence detection, proximity detection, object recognition, activity recognition, object verification, daily activity monitoring, daily activity monitoring, daily activity monitoring, daily activity monitoring, well-being monitoring, vital signs monitoring, health status monitoring, baby monitoring, elderly monitoring, sleep monitoring, sleep state monitoring, gait monitoring, exercise monitoring, tool detection, tool recognition, tool verification, patient detection, patient monitoring, patient Verification, machine detection, machine recognition, machine verification, human detection, human recognition, baby detection, baby recognition, baby verification, human respiration detection, human respiration recognition, human respiration estimation, human respiration verification, human heart rate detection, human heart rate recognition, human heart rate estimation, fall detection, fall recognition, fall verification, fall verification, emotion detection, emotion recognition, emotion estimation, emotion verification, motion recognition, motion estimation, motion verification, degree of motion estimation, periodic motion detection, periodic motion recognition, periodic motion estimation, periodic motion verification, repetitive motion detection, repetitive motion recognition, repetitive motion estimation, repetitive motion verification, steady-state motion detection, steady-state motion recognition, steady-state motion estimation, steady-state motion verification, cyclo Steady-state motion detection, cyclostatic motion recognition, cyclostatic motion estimation, cyclostatic motion verification, transient motion detection, transient motion recognition, transient motion estimation, transient motion verification, trend detection, trend verification, respiration detection, respiration recognition, respiration estimation, human biometric authentication detection, human biometric authentication recognition, human biometric authentication estimation, human biometric authentication verification, environmental informatics detection, environmental informatics recognition, environmental informatics estimation, environmental informatics verification, gait detection, gait recognition, gait estimation, gait verification, gesture detection, gesture recognition, gesture estimation, gesture verification, machine learning, unsupervised learning, semi-supervised learning, clustering Ring, Feature extraction, Feature training, Principal component analysis, Eigenvalue decomposition, Frequency decomposition, Time decomposition, Time-frequency decomposition, Functional decomposition, Other decomposition, Training, Discrimination training, Semi-supervised training, Unsupervised training, Semi-supervised training, Neural networks, Sudden movement detection, Fall detection, Hazard detection, Life threat detection, Steady-state movement detection, Cyclo-steady-state movement detection, Intrusion detection, Intrusion movement detection, Suspicious movement detection, Security, Safety monitoring, Navigation, Guidance, Map-based processing, Map-based correction, Model-based processing / correction,Anomaly detection, location detection, indoor sensing, tracking, multiple object tracking, indoor tracking, indoor location tracking, indoor navigation, energy management, power transfer, wireless power transfer, object counting, vehicle tracking in parking lots, activation of devices / systems (e.g., security systems, access systems, alarms, sirens, speakers, televisions, entertainment systems, cameras, heater / air conditioning (HVAC) systems, ventilation systems, lighting systems, game systems, coffee machines, cooking appliances, cleaning appliances, housekeeping appliances), shape estimation, augmented reality, wireless communication, data communication, signal broadcasting, networking, coordination, management, encryption, protection, cloud computing, other processing and / or other tasks. Tasks may be performed by Type 1 devices, Type 2 devices, another Type 1 device, another Type 2 device, nearby devices, local servers (e.g., hub devices), edge servers, cloud servers and / or other devices. Tasks may be based on TSCI between any pair of Type 1 and Type 2 devices. A Type 2 device may be a Type 1 device, and vice versa. Type 2 devices can temporarily, continuously, sporadically, simultaneously, and / or concurrently perform the role (e.g., functionality) of Type 1 devices, and vice versa. The first part of the task may include at least one of the following: preprocessing, signal processing, signal processing, conditioning, signal processing, signal processing, adjustment, signal processing, mapping / continuous / mapping / continuous / adaptive / mapping / request, adjustment, feature extraction, coding, coding, modification, coding, modification, motion detection, motion detection, motion change detection, motion detection pattern, motion detection pattern, motion recognition pattern, vital sign detection, vital sign estimation, vital sign recognition, periodic motion detection, periodic motion estimation, repetitive motion detection / respiratory rate detection, respiratory rate detection, respiratory pattern detection, respiratory pattern estimation, respiratory pattern recognition, heart rate detection, heart rate estimation, heart rate pattern detection, heart rate pattern estimation, heart rate pattern recognition, gesture detection, gesture estimation, gesture recognition, velocity detection, velocity estimation, object position estimation, object tracking, navigation, acceleration estimation, acceleration detection, fall detection, change detection, intruder (and / or tort) detection, baby detection, baby monitoring,Patient monitoring, object recognition, wireless power transmission, and / or wireless charging.

[0126] The second part of the task is smart home tasks, smart office tasks, smart factory tasks (e.g., manufacturing using machinery or assembly lines), smart internet (IoT) tasks, smart home operations, smart office operations, smart building operations (e.g., moving supplies / parts / raw materials to machinery / assembly lines), IoT operations, smart system operations, turning on lights, controlling lights in at least one of rooms, areas and / or venues, playing sound clips, playing sound clips in at least one of rooms, areas and / or venues, playing welcome, greetings, welfare, first message and / or second message associated with the first part of the task, turning on appliances, controlling equipment in rooms, areas and / or venues, rooms, areas and / or controlling equipment within the venue, controlling electrical systems, turning on rooms, electrical systems, controlling rooms, areas, and / or electrical systems within the venue, turning on security systems, turning security systems off, controlling rooms, areas, and / or security systems within the venue, turning on mechanical systems, controlling mechanical systems, controlling rooms, areas, and / or mechanical systems within the venue, and / or controlling at least one of the following: air conditioning systems, heating systems, ventilation systems, lighting systems, lighting fixtures, stoves, entertainment systems, doors, fences, windows, garages, computer systems, networked devices, networked devices, systems, home appliances, home appliances, office equipment, lighting fixtures, robots (such as robotic arms), smart vehicles, smart machines, assembly lines, smart devices, Internet of Things (IoT) devices, smart home devices, and / or smart office devices.

[0127] Tasks may include: detecting a user returning home, detecting movement from one room to another, detecting windows / garage doors / blinds / curtains / panels / solar panels / sunshades, detecting / monitoring user actions (e.g., sleeping on the sofa, running in the bedroom, cooking on the sofa, watching TV, eating in the kitchen, going up / down stairs, returning to the break room), monitoring / detecting user / pet location, automatically doing something when a user is detected, turning lights on / off, turning on music / radio / home entertainment systems, turning on / off TVs / HiFi / set-top boxes / home entertainment systems / smart speakers / smart devices To adjust / control, to turn air conditioning systems on / off / adjust, to turn ventilation systems on / off / adjust, to turn heating systems on / off / adjust, to adjust / control curtains / light shades, to turn computers on / off / wake up, to turn coffee machines / hot water pots on / off / preheat control, to turn cookers / ovens / microwaves / other cooking devices on / off / control / preheat control, to turn ovens / microwaves / other cooking devices on / off / adjust, to check / adjust temperature forecasts, to check phone message boxes, to check emails, to check / adjust systems, to check / adjust / control systems / arms / safety protection systems / baby monitors, to check / control refrigerators (e.g., via speakers such as Google Home or Amazon Echo, on displays / screens, via web pages / emails).

[0128] For example, when a user arrives home in their car, the task may automatically detect the user or their car approaching, open the garage door upon detection, turn on the driveway / garage lights, and turn on the air conditioning / heater / fan, etc., as the user approaches the garage. When the user enters the house, the task may automatically turn on the entrance lights, turn off the driveway / garage lights, play a welcome greeting message, turn on music, turn on the user's preferred radio news channel, open the curtains / blinds, monitor the user's mood, adjust the lighting and sound environment according to the user's mood or current / imminent events (for example, if the user has scheduled dinner with their girlfriend in an hour, create romantic lighting and music), warm up food in the microwave that the user prepared in the morning, perform diagnostic checks on all systems in the house, check the weather forecast for tomorrow's work, check news of the user's interest, check the user's calendar and to-do list, play reminders, and check the phone answering system / messaging system / email. The task can include pre-turning on air conditioning / heating / ventilation systems or pre-setting the temperature of a smart thermostat, etc. The task can also include pre-turning on air conditioning / heating / ventilation systems or pre-setting the temperature of a smart thermostat, etc. The task can also include pre-turning on air conditioning / heating / ventilation systems or pre-setting the temperature of a smart thermostat, etc.As the user moves from the entrance to the living room, tasks may include turning on the living room lights, opening the living room curtains, opening the windows, turning off the entrance light behind the user, turning on the TV and set-top box, setting the TV to the user's preferred channel, and adjusting appliances according to the user's preferences and conditions / states (for example, adjusting the lighting and selecting / playing music to create a romantic atmosphere).

[0129] Another example is when the user wakes up in the morning; the task could be to detect that the user is moving around in the bedroom, open the blinds / curtains, open the windows, turn off the alarm clock, adjust the indoor temperature from a night temperature profile to a day temperature profile, turn on the bedroom light, turn on the bathroom light as the user approaches the bathroom, check for wireless or streaming channels and play the morning news, turn on the coffee machine and preheat the water, turn off the security system, etc. When the user walks from the bedroom to the kitchen, the task could be to turn on the kitchen and hallway lights and turn off the bedroom and bathroom lights, move music / messages / reminders from the bedroom to the kitchen, turn on the kitchen TV and change the TV to the morning news channel, lower the kitchen blinds, open the kitchen window to let in fresh air, unlock the back door for the user to check the backyard, adjust the kitchen temperature settings, etc. Another example is when a user leaves home for work. The task could include detecting the user's departure, playing a farewell message, opening and closing the garage door, turning garage and driveway lights on / off, turning lights off / dim to save energy (only if the user forgets), closing / locking all windows / doors (only if the user forgets), turning off appliances (especially stoves, ovens, and microwaves), turning on / arming the home security system to protect the house from intruders, adjusting the HVAC system to an "away from home" profile to save energy, and sending alerts / reports / updates to the user's smartphone.

[0130] Motion can include at least one of the following: no motion, static motion, non-motion, movement, change of place / location, deterministic motion, transient motion, falling motion, repetitive motion, periodic motion, pseudo-periodic motion, periodic / repetitive motion associated with breathing, periodic / repetitive motion associated with heartbeat, periodic / repetitive motion associated with living organisms, periodic / repetitive motion associated with machines, periodic / repetitive motion associated with artificial objects, periodic / repetitive motion associated with nature, compound motion with transient and periodic elements, repetitive motion, non-deterministic motion, stochastic motion, chaotic motion, random motion, compound motion with non-deterministic and deterministic elements, stationary random motion, pseudo-stationary random motion, cyclostationary random motion, non-stationary random motion, periodic autocorrelation function (A Steady random motion with CF), random motion with periodic ACF, periodic pseudo-steady motion, random motion with a periodic pseudo-periodic element in instantaneous ACF, mechanical motion, vehicle motion, drone motion, atmospheric motion, wind motion, weather-related motion, water-related motion, fluid-related motion, ground-related motion, electromagnetic property changes, underground motion, seismic motion, plant motion, animal motion, human motion, normal motion, abnormal motion, dangerous motion, rain, fire, flood, tsunami, explosion, collision, near-collision, human body motion, head motion, facial motion, eye movement, mouth movement, tongue movement, neck movement, finger movement, hand movement, arm movement, shoulder movement, body movement, chest movement, abdominal movement, waist movement, leg movement, foot movement, body joint movement, knee movement, elbow movement, upper body movement, lower body movement, skin movement, subcutaneous movement, subcutaneous tissue movement. Vasomotor movements, venous movements, organ movements, cardiac movements, pulmonary movements, gastric movements, intestinal movements, bowel movements, eating movements, respiratory movements, facial expressions, eye expressions, mouth expressions, speaking movements, singing movements, eating movements, gestures, hand gestures, arm movements, keystrokes, typing strokes, user interface gestures, man-machine interactions, walking, dancing movements, coordinated movements, and / or coordinated body movements.

[0131] A heterogeneous IC of a Type 1 device and / or any Type 2 receiver may comprise a low-noise amplifier (LNA), a power amplifier, a transmit / receive switch, a media access controller, a baseband radio, a 2.4 GHz radio, a 3.65 GHz radio, a 4.9 GHz radio, a 5 GHz radio, a 5.9 GHz radio, a radio less than 6 GHz, a radio less than 60 GHz, and / or another radio. A heterogeneous IC may comprise a processor, memory communicatively coupled to the processor, and a set of instructions stored in the memory to be executed by the processor. An IC and / or any processor may comprise at least one of a general-purpose processor, a dedicated processor, a microprocessor, a multiprocessor, a multicore processor, a parallel processor, a CISC processor, a RISC processor, a microcontroller, a centralized processing unit (CPU), a graphical processor unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), an embedded processor (e.g., ARM), a logic circuit, other programmable logic devices, discrete logic, and / or a combination thereof. Heterogeneous ICs may support broadband networks, wireless networks, cellular networks, wireless local area networks (WLANs), wide area networks (MANs), WLAN standards, WiFi, LTE-A, LTE-U, 802.11 standards, 802.11a, 802.11g, 802.11ac, 802.11ad, 802.11ah, 802.11ax, 802.11ay, network mesh standards, 802.16 standards, cellular network standards, 3G, 3.5G, 4G, 5G, 6G, 7G, 8G, 9G, UMTS, 3GPP, GSM, EDGE, TDMA, CDMA, WCDMA, TD-SCDMA, Bluetooth Low Energy (BLE), NFC, Zigbee, WiMAX, and / or other wireless network protocols.

[0132] The processor may include a general-purpose processor, a dedicated processor, a microprocessor, a microcontroller, an embedded processor, a digital signal processor, a centralized processing unit (CPU), a graphical processing unit (GPU), a multiprocessor, a multicore processor, and / or a processor with graphics capabilities, and / or a combination thereof. Memory may be volatile, non-volatile, random access memory (RAM), read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), a hard disk, flash memory, a CD-ROM, a DVD-ROM, a magnetic storage device, an optical storage device, an organic storage device, a storage system, a storage network, a network storage device, a cloud storage device, an edge storage device, a local storage device, an external storage device, an internal storage device, or other non-temporary storage media of a form known to those skilled in the art. The set of instructions corresponding to the method steps (machine executable code) may be directly embodied in hardware, software, firmware, or a combination thereof. The set of instructions may be embedded, preloaded, loaded at startup, loaded on the fly, loaded on demand, pre-installed, installed, and / or downloaded.

[0133] Presentations can be audiovisual (e.g., using a combination of visuals, graphics, text, symbols, color, shade, video, animation, sound, speech, audio, etc.), graphical (e.g., using a GUI, animation, video), textual (e.g., a webpage with text, messages, animated text), symbolic (e.g., emoticons, signs, hand gestures), or mechanical (e.g., vibration, actuator movement, haptics, etc.).

[0134] Basic operations

[0135] The computational workload associated with the method is shared among the processor, Type 1 heterogeneous wireless devices, Type 2 heterogeneous wireless devices, a local server (e.g., a hub device), a cloud server, and another processor.

[0136] Calculations, preprocessing, processing, and / or postprocessing may be applied to data (e.g., TSCI, autocorrelation, TSCI features). The operation may be preprocessing, processing, and / or postprocessing. Preprocessing, processing, and / or postprocessing may be operations.The operations include preprocessing, postprocessing, scaling, calculating line-of-sight (LOS) quantities, calculating quantities including LOS and NLOS, calculating single-link quantities (e.g., paths, communication paths, links between transmitting and receiving antennas), calculating quantities including multiple links, calculating operand functions, linear filtering, nonlinear filtering, folding, energy calculation, low-pass filtering, band-pass filtering, median filtering, interquartile filtering, mode filtering, finite impulse response (FIR) subsampling, upsampling, time correction, time-based correction, amplitude correction, phase correction, phase cleaning, amplitude cleaning, matched filtering, enhancement, restoration, denoising, smoothing, signal tuning, enhancement, restoration, spectral analysis, linear transformation, nonlinear transformation, inverse transformation, frequency transformation, inverse transformation, Fourier transform (FT), discrete-time FT (DFT), fast FT (FFT), wavelet transform, Hilbert transform, triangular transform, sine transform, cosine transform, DCT, power-2 transform, sparse transform, graph-based TSCI transformation, fast transformation, zero padding, cyclic padding, zero padding, feature extraction, decomposition, orthogonal projection, imperfect projection, eigenvalue decomposition (SVD), principal component analysis (ICA), grouping, thresholding, hard thresholding, clipping, first derivative, higher-order derivative, convolution, multiplication, least squares method, local deviation minimization, neural network, recognition, labeling, unsupervised learning, semi-supervised learning, comparison with another TSCI, similarity score calculation, quantization, matching tracking, compression, encryption, transmission, normalization, time normalization, frequency domain normalization, classification, labeling This may include ringing, labeling, learning, mapping, remapping, memory, retrieval, reception, representation, merging, joining, tracking, consistent filtering, Kalman filtering, interpolation error correction, execution, do nothing, time-varying processing, adjusted averaging, weighted averaging, arithmetic mean, geometric mean, harmonic mean, averaging over selected frequencies, averaging over antenna links, logical operations, substitution, combination, sorting, AND, OR, XOR, union, intersection, vector addition, vector subtraction, vector multiplication, vector division, inverse, norm, distance, and / or other operations. The operations may also be pre-processing, processing, and / or post-processing. The operations may be applied together to multiple time series or functions.

[0137] Functions (e.g., operand functions) can include: scalar functions, vector functions, continuous functions, magnitude functions, trigonometric functions, logical functions, linear functions, piecewise functions, real functions, vector-valued functions, inverse functions, integral derivatives, function derivatives, one-to-one functions, many-to-one functions, many-to-many functions, zero-crossing functions, absolute functions, indicator functions, mean, mode, median, range, statistics, histogram, variance, deviation, divergence, range, total variation, absolute deviation, total deviation, arithmetic mean, geometric mean, trimmed mean, percentile, square root, multiplier, cosine, cosine tangent, cotangent, secant, elliptic function, parabolic function, game function, zeta function, absolute value, threshold function, floor function, rounding function, quantization, piecewise constant function, compound function, time function processed by the operation (e.g., probability function, ergodic function, probability function, periodic function, probability function, inverse frequency transform, discrete time transform, Laplace transform, sine transform, cosine transform, trigonometric transform, wavelet transform, integer transform, power 2 transform, sparse transform, decomposition, original component analysis (PCA), independent component analysis (ICA), neural network, feature extraction, moving window function for time series, filtering function, convolution function, mean function, histogram, variance / standard deviation function, short-time transform, discrete transform) Discrete Fourier transform, discrete cosine transform, eigenvalue decomposition (SVD), singular values, alignment tracking, sparse transform, graph-based transform, graph processing, classification, graph signal processing, classification, labeling, machine learning, detection, feature extraction, network feature extraction, denoising, coding, encryption, remapping, vector quantization, high-pass filtering, aligned filtering, Kalman filtering, preprocessing, particle filtering, FIR filtering, autoregressive (AR) filtering, adaptive filtering, higher-order derivatives, integration, zero crossing, smoothing, mode filtering, sampling, random sampling, resampling function, downsampling, upsampling, interpolation, importance sampling, Monte Carlo sampling, compressed sensing, statistics, short-term statistics, long-term statistics, autocorrelation function, cross-correlation, moment generation function, time averaging, weighted averaging, special functions, Bessel function, error function, complementation function, beta function, gamma function, integral function, Gaussian function, Poisson function, etc.

[0138] Machine learning, training, discriminant training, deep learning, neural networks, continuous-time processing, distributed computing, distributed storage, and acceleration using GPUs / DSPs / coprocessors / multicores / multiprocessing may be applied to the steps (or each step) of this disclosure.

[0139] Frequency transformations may include Fourier transforms, Laplace transforms, Hadamard transforms, Hilbert transforms, sine transforms, cosine transforms, trigonometric transforms, wavelet transforms, integer transforms, squared transforms, combined zero-padding and transforms, Fourier transforms with zero-padding, and / or other transforms. Faster and / or approximate versions of the transforms may be performed. Transforms may be performed using floating-point and / or fixed-point arithmetic.

[0140] Inverse frequency transforms may include inverse Fourier transforms, inverse Laplace transforms, inverse Hadamard transforms, inverse Hilbert transforms, inverse sine transforms, inverse cosine transforms, inverse trigonometric transforms, inverse wavelet transforms, inverse integer transforms, inverse squared transforms, combined zero-padding and transforms, inverse Fourier transforms with zero-padding, and / or other transforms. Faster and / or approximate versions of the transforms may be performed. Transforms may be performed using floating-point and / or fixed-point arithmetic.

[0141] Quantities / features can be calculated from TSCI. Quantities may include at least one of the following: motion, position, location, coordinates, velocity, movement angle, displacement, displacement, pattern, time, trend, pattern, time pattern, repeating pattern, time pattern, mutually exclusive patterns, association / correlation, causal / correlation, short-term / impact, correlation, short-term / impact, correlation, trend, trend, statistics, typical behavior, typical behavior, time trend, time profile, periodic motion, periodic motion, repetition, repetition, motion, repetition, trend, change, abrupt change, frequency, transient change, frequency, transient change, respiration, behavior, event, dangerous event, alarm, alarm, warning, proximity, collision, power, signal, signal power, signal strength, signal strength, received signal strength indicator (RSSI), signal amplitude, signal, phase signal, frequency component Signal frequency components, non-orthogonal statistics, cardiopulmonary statistics, output statistics, heart rate, statistics / analysis, daily activity statistics, tracking, heart rate, statistics / analysis, medical statistics / analysis, early (or immediate or simultaneous) indicators / suggestions / indicators / verifiers / suggestions / suggestions / signs / detections / symptoms, disease / condition / situation, biometrics, baby, patient, machine, device, temperature, vehicle, parking lot, place, elevator, space, fluid flow, home, room, office, house, building, warehouse, storage, system, ventilation, fan, duct, person, human, automobile, boat, truck, airplane, drone, downtown, crowd, impulsive event, cyclostatic, environment, vibration, material, surface, 3D, 2D, local, global, presence, and / or other measurable quantity / variable.

[0142] Sliding window / algorithm

[0143] The sliding time window may have a time-varying window width. It may be initially smaller to enable fast acquisition and may increase over time to a steady-state size. The steady-state size may be related to the frequency being monitored, repetitive motion, transient motion, and / or STI. Even in a steady state, the window size may be changed (e.g., adjusted, modified) adaptively (and / or dynamically) based on battery life, power consumption, available computing power, changes in the target quantity, the nature of the motion to be monitored, etc.

[0144] The time shift between two sliding time windows in adjacent time instances can be constant / variable / locally adaptive / dynamically adjusted over time. When shorter time shifts are used, any monitoring updates may be more frequent, which can be used for rapidly changing situations, object movements, and / or objects. Longer time shifts can be used for slower situations, object movements, and / or objects.

[0145] Window width / size and / or time shift may be changed (e.g., adjusted, modified, corrected) according to user requests / selections. Time shift may be changed automatically (e.g., controlled by the processor / computer / server / hub device / cloud server) and / or adaptively (and / or dynamically).

[0146] At least one characteristic (e.g., characteristic value or characteristic point) of a function (e.g., autocorrelation function, autocovariance function, cross-correlation function, cross-covariance function, power spectral density, time function, frequency domain function, frequency transform) can be determined (e.g., by an object tracking server, a processor, a type 1 heterogeneous device, a type 2 heterogeneous device, and / or another device). At least one property of the function may include: maximum, minimum, extremum, limit, local extremum with positive time offset, first extremum with positive time offset, local extremum with negative time offset, nth extremum, constrained extremum, constrained maximum, significant extremum, slope, derivative, maximum slope, local extremum with positive time offset, local maximum slope, constrained maximum slope, maximum higher derivative, constrained higher derivative, constrained higher derivative, zero crossing with positive time offset, nth zero crossing with negative time offset, nth zero crossing with negative time offset, constrained zero crossing, zero crossing of slope, zero crossing of higher derivative, and / or another property. At least one argument of the function related to at least one property of the function may be identified. Some quantities (e.g., spatial-temporal information of an object) may be determined based on at least one argument of the function.

[0147] The characteristics (for example, the characteristics of the movement of an object in a venue) may include at least one of the following: instantaneous characteristics, short-term characteristics, repeatability characteristics, time-series characteristics, amplitude characteristics, time-series characteristics, variability characteristics, orthogonal decomposition characteristics, stochastic characteristics, stochastic characteristics, autocorrelation function (ACF), mean, variance, spread, deviation, divergence, range, absolute deviation, total deviation, statistics, duration, timing, trend, periodic characteristics, long-term characteristics, historical characteristics, current characteristics, past characteristics, predictive characteristics, position, distance, velocity, speed, acceleration, angular velocity, change in angular velocity, change in object, angular acceleration, direction of object, angle of rotation, deformation of object, shape of object, change in shape of object, change in size of object, change in structure of object, and / or change in characteristics of object.

[0148] At least one local maximum and at least one minimum of the function can be identified. At least one local signal-to-noise ratio (SNR) analogous parameter can be calculated for each pair of adjacent local maximums and minimums. The SNR analogous parameter may be a function (e.g., linear, logarithmic, exponential, monotonic) of the amount of the local maximum (e.g., power, magnitude) over the same amount of the local minimum. It may also be a function of the difference between the amount of the local maximum and the same amount of the minimum. Significant local peaks can be identified or selected. Each significant local peak may be a maximum with an SNR analogous parameter greater than threshold T1, and / or a maximum with an amplitude greater than threshold T2. At least one minimum and at least one minimum in the frequency domain can be identified / calculated using a persistence-based approach.

[0149] A set of significant local peaks can be selected from a set of identified significant local peaks based on selection criteria (e.g., quality criteria, signal quality conditions). The object's properties / STI can be calculated based on the selected set of significant local peaks and the frequency values ​​associated with that set. For example, the selection criteria might always correspond to selecting the strongest peak within a range. The strongest peak may be selected, but unselected peaks may still be significant (quite strong).

[0150] Significant unselected peaks may be stored and / or monitored as “reserved” peaks for use in future selections within future sliding time windows. For example, a particular peak (at a specific frequency) may appear consistently over time. Initially, it may be significant but not selected (because other peaks may be stronger). However, later, the peak may become stronger, more dominant, and selected. When it is “selected,” it may be considered “selected” retrospectively to the earlier time when it was significant but not selected. In such cases, the backtrace peak may replace a previously selected peak at an earlier time. The replaced peak may be a relatively weak peak or a peak that appears alone in time (i.e., appears only for a short time).

[0151] In another example, the selection criteria may not necessarily correspond to selecting the strongest peak within the range. Instead, it could consider not only the peak's "intensity" but also its "trace" (peaks that may have occurred in the past, especially peaks that have been identified for a long time).

[0152] For example, if a finite state machine (FSM) is used, it can select (one or more) peaks based on the state of the FSM. The decision threshold can be calculated adaptively (and / or dynamically) based on the state of the FSM.

[0153] Similarity scores and / or component similarity scores may be calculated based on pairs of temporally adjacent CIs of a TSCI (e.g., by a server (e.g., a hub device), a processor, a Type 1 device, a Type 2 device, a local server, a cloud server, and / or another device). The pairs may come from the same slide window or from two different slide windows. Similarity scores may also be based on temporally adjacent or non-adjacent CIs of pairs from two different TSCIs. Similarity scores and / or component similarity scores may be time-reverse resonance intensity (TRRS), correlation, cross-correlation, autocorrelation, correlation index, covariance, cross-covariance, autocovariance, dot product of two vectors, distance score, norm, metric, quality metric, signal quality condition, statistical properties, discrimination score, neural network, deep learning network, machine learning, training, discrimination, weighted averaging, preprocessing, denoising, signal tuning, filtering, time correction, timing compensation, phase offset compensation, transformation, component-specific behavior, feature extraction, finite state machine, and / or other scores. Characteristics and / or STI may be determined / calculated based on the similarity score.

[0154] Arbitrary thresholds can be predetermined, adaptively (and / or dynamically), and / or determined by a finite state machine. Adaptive determinations may be based on time, space, location, antenna, path, link, state, battery life, remaining battery life, available power, available computing resources, available network bandwidth, etc.

[0155] A threshold can be determined to be applied to test statistics to distinguish between two events (or two conditions, or two situations, or two states) A ​​and B. Data (e.g., CI, channel status information (CSI), power parameters) may be collected under A and / or B in a training context. Test statistics may be calculated based on the data. The distribution of test statistics under A may be compared to the distribution of test statistics under B (reference distribution), and the threshold may be selected according to several criteria. Criteria may include maximum likelihood (ML), maximum a posterio probability (MAP), discrimination training, minimum type 1 error for a given type 2 error, minimum type 2 error for a given type 1 error, and / or other criteria (e.g., quality criteria, signal quality criteria). The threshold may be adjusted to achieve different sensitivities to A, B and / or another event / condition / situation / state. Threshold adjustment may be automatic, semi-automatic, and / or manual. Threshold adjustment may be applied once, sometimes, often, regularly, repeatedly, sometimes, sporadically, and / or on demand. Threshold adjustment may be adaptive (and / or dynamically adjusted). Threshold adjustment may depend on objects, object movement / position / orientation / action, object properties / STI / size / characteristics / habits / behavior, venues, features / fixtures / furniture / barriers / materials / machines / living things / object boundaries / surfaces / mediums, maps, constraints of maps (or environment models), events / states / situations / conditions, time, timing, duration, current state, past history, users, and / or personal preferences, etc.

[0156] The stopping criteria (or skip, bypass, blocking, pause, pass, reject criteria) of an iterative algorithm may be that the change in the current parameter (e.g., offset value) in the update during an iteration is less than a threshold. The threshold may be 0.5, 1, 1.5, 2, or another number. The threshold may be adaptive (and / or dynamically adjusted). It may change as the iterations progress. For the offset value, the adaptive threshold may be determined based on the task, a specific value at the first time, the current time offset value, the regression window, the regression analysis, the regression function, the regression error, the convexity of the regression function, and / or the number of iterations.

[0157] Local extrema may be determined as the corresponding extrema of the regression function within the regression window. Local extrema can be determined based on a set of time offset values ​​within the regression window and a set of associated regression function values. Each of the associated sets of regression function values ​​associated with a set of time offset values ​​may be within the range of the corresponding extrema of the regression function within the regression window.

[0158] Searching for local extrema can include: robust search, minimization, maximization, optimization, statistical optimization, binary optimization, constraint optimization, convex optimization, global optimization, local optimization, energy minimization, linear regression, quadratic regression, higher-order regression, linear programming, nonlinear programming, stochastic programming, combinatorial optimization, constraint programming, constraint satisfaction, variational methods, optimal control, dynamic programming, mathematical programming, convex optimization, convex optimization, local optimization, convex optimization, local optimization, linear regression, quadratic regression Variational methods, optimal control, dynamic programming, mathematical programming, multiobjective optimization, multidimensional optimization, separable programming, spatial mapping, infinite-dimensional optimization, heuristics, metaheuristics, convex programming, semidefinite programming, conical programming, integer programming, quadratic programming, fractional programming, numerical analysis, simplex methods, iterative methods, gradient descent, subgradient methods, coordinate descent, conjugate gradient methods, Newton's method, successive quadratic programming, interior point methods, ellipsoid methods, shrinking gradient methods, pseudo-Newton's method, simultaneous perturbation stochastic approximation, interpolation methods, pattern search methods, line search, non-differential optimization, genetic algorithms, evolutionary algorithms, dynamic relaxation, hill climbing, particle swarm optimization, gravity search algorithms, simulated annealing, mimetic algorithms, differential evolution, dynamic relaxation, stochastic tunneling, taboo search, reaction search optimization, curve fitting, least squares, simulation-based optimization, variational, and / or variable. The search for local extrema can be associated with the objective function, loss function, cost function, utility function, fitness function, energy function, and / or energy function.

[0159] Regression is performed using a regression function to fit sampled data (e.g., CI, CI features, CI components) or another function (e.g., an autocorrelation function) to a regression window. The length and / or position of the regression window may change in at least one iteration. The regression function may be a linear function, a quadratic function, a cubic function, a polynomial function, and / or another function.

[0160] Regression analysis can analyze errors, aggregate errors, component errors, errors in projection domains, errors on selected orthogonal axes, absolute errors, squared errors, absolute deviations, squared deviations, higher-order errors (e.g., third-order, fourth-order), robust errors (e.g., squared errors for smaller errors, absolute errors for larger errors, or first-type errors for smaller errors and second-type errors for larger errors), other errors, weighted sum (or weighted mean) of absolute / squared errors (e.g., a radio transmitter with multiple antennas and a radio receiver with multiple antennas, where each pair of transmitter and receiver antennas forms a link), mean absolute error, mean squared error, and mean absolute deviation. Errors associated with different links may have different weights. One possibility is that some links and / or some components with higher noise or lower signal quality metrics may have smaller or larger weights. Weighted sum of squared errors, weighted sum of higher-order errors, weighted sum of robust errors, weighted sum of other errors, absolute cost, squared cost, higher-order cost, robust cost, other cost, weighted sum of absolute costs, weighted sum of squared costs, weighted sum of higher-order costs, weighted sum of robust costs, and / or weighted sum of other costs.

[0161] The regression error determined may be an absolute error, a squared error, a higher-order error, a robust error, another error, a weighted sum of absolute errors, a weighted sum of squared errors, a weighted sum of higher-order errors, a weighted sum of robust errors, and / or a weighted sum of another error.

[0162] The time offset associated with the maximum (or minimum) regression error of a regression function for a particular function within a regression window can be the current time offset, which is updated in the iteration.

[0163] Local extrema can be explored based on a quantity that includes the difference between two different errors (e.g., the difference between the absolute error and the squared error). Each of the two different errors may include the absolute error, the squared error, a higher-order error, a robust error, another error, a weighted sum of absolute errors, a weighted sum of squared errors, a weighted sum of higher-order errors, a weighted sum of robust errors, and / or a weighted sum of another error.

[0164] The quantity may be compared to reference data or reference distributions such as the F-distribution, the central F-distribution, another statistical distribution, thresholds, thresholds related to probability / histograms, thresholds related to the probability / histogram of finding false peaks, thresholds related to the F-distribution, thresholds related to the central F-distribution, and / or thresholds related to another statistical distribution.

[0165] The regression window may be determined based on at least one of the following: the motion of an object (e.g., change in position / location), a quantity related to the object, at least one characteristic of the object related to the motion of the object and / or STI, the estimated location of a local extremum, noise characteristics, estimated noise characteristics, the motion of an object (e.g., change in position / location), a quantity related to the object, at least one characteristic of the object related to the motion of the object and / or STI, the estimated location of a local extremum, noise characteristics, estimated noise characteristics, signal quality metric, F-distribution, central F-distribution, another statistical distribution, threshold, preset threshold, threshold related to probability / histogram, threshold related to desired probability, threshold related to probability of finding a false peak, threshold related to F-distribution, threshold related to central F-distribution, threshold related to another statistical distribution, the condition that the quantity at the center of the window is maximum within the regression window, the condition that the quantity at the center of the window is maximum within the regression window, the local extremum of a particular function for a particular value at the first time in the regression window, another regression window, and / or the condition that only one of the other conditions exists.

[0166] The width of the regression window may be determined based on the specific extrema to be searched for. Extrema may include: the first extremum, the second extremum, the maximum extremum, the first extremum with a maximum extremum positive offset, the second extremum with a maximum extremum positive offset, the second extremum with a maximum extremum positive offset, the first extremum with a maximum extremum negative offset, the second extremum with a maximum extremum negative offset, the first extremum with a maximum extremum negative offset, the second extremum with a maximum extremum negative offset, the first extremum with a maximum extremum positive offset, the first extremum with a second extremum positive offset, the second extremum with a positive offset, the second extremum with a positive offset, and the first extremum with a positive offset and a negative time offset.

[0167] The current parameter (e.g., time offset value) may be initialized based on the target value, target profile, trend, historical trend, current trend, target velocity, target velocity profile, historical velocity trend, motion or movement of an object (e.g., position / change of position), at least one feature of the object related to the motion of the object and / or STI, position quantity of the object, initial velocity of the object related to the motion of the object, predefined value, initial width of the regression window, time duration, value based on the signal's carrier frequency, value based on the signal's subcarrier frequency, signal bandwidth, antenna quantity related to the channel, noise characteristics, signal hmetric, and / or adaptive (and / or dynamically adjusted) value. The current time offset may be at the center, left, right, and / or another fixed relative position of the regression window.

[0168] In the presentation, the information may be displayed along with a venue map (or environmental model). The information may include: location, zone, region, coverage area, modified location, approximate location, location relative to the venue map, location relative to the venue segmentation, direction, path, path, trace (e.g., location within a time window such as the last 5 seconds or the last 10 seconds; the time window duration may be adjusted adaptively (and / or dynamically); the time window duration may be adjusted adaptively (and / or dynamically) with respect to velocity, acceleration, etc.), path history, approximate region / zone along the path, Past location history / summary, past location history of interest, frequently visited areas, customer traffic, crowd distribution, crowd behavior, crowd control information, speed, acceleration, motion statistics, respiratory rate, heart rate, presence / absence of people, pets, or objects, presence or absence of vital signs, actions, gesture control (control of devices using gestures), location-based gesture control (control of devices using gestures), location-based actions, and identification information (ID) or identifiers of respected entities (pets, people, self-propelled machines / devices, vehicles, drones, cars, boats). (bicycles, bicycles, fan-equipped machines, air conditioners, televisions, machines with moving parts), user identification information (people, etc.), user position / velocity / acceleration / direction / movement / gestures / gesture control / motion trace, user ID or identifier, user activity, user state, user sleep / rest characteristics, user emotional state, user vital signs, venue environmental information, venue weather information, earthquake, explosion, storm, rain, fire, temperature, collision, event open, door event, event close, door event, event open, impact, event win End-close, event fall-down, burning event, freezing event, water-related event, wind-related event, air movement event, accident event, pseudo-periodic event (e.g., running on a treadmill, jumping up and down, jumping off a rope, artificial jumping, etc.), recurring event, crowd event, vehicle event, user gestures (e.g., hand gestures, arm gestures, foot gestures, leg gestures, body gestures, head gestures, face gestures, mouth gestures, eye gestures, etc.).

[0169] Location may be two-dimensional (e.g., having 2D coordinates) or three-dimensional (e.g., having 3D coordinates). Location may be relative (e.g., on a map or environment model) or relational (e.g., midway between point A and point B, around a corner, on a staircase, on a table, on the ceiling, on the floor, on a sofa, close to point A, distance R from point A, within radius R from point A, etc.). Location may be expressed in rectangular coordinates, polar coordinates, and / or other representations.

[0170] Information (e.g., location) may be marked with at least one symbol. The symbol may change over time. The symbol may flash and / or pulsate with or without changing color / intensity. Its size may change over time. The orientation of the symbol may change over time. The symbol may be a number that reflects an instantaneous quantity (e.g., vital signs / respiratory rate / heart rate / gestures / state / user state / action / movement, temperature, network traffic, network connectivity, device / machine state, remaining power of device, device state, etc.). The rate of change, size, orientation, color, intensity, and / or symbol may reflect their respective movements. The information may be presented visually and / or described orally (e.g., using pre-recorded speech or speech synthesis). The information may be described in text. The information may also be presented in a mechanical manner (e.g., animated gadgets, movement of moving parts).

[0171] User interface (UI) devices may include smartphones (e.g., iPhone®, Android phones), tablets (e.g., iPad®), laptops (e.g., notebook computers), personal computers (PCs), devices with graphical user interfaces (GUIs), smart speakers, devices with voice / audio / speaker functions, virtual reality (VR) devices, augmented reality (AR) devices, smart cars, in-car displays, voice assistants, in-car voice assistants, etc.

[0172] The map (or environmental model) may be two-dimensional, three-dimensional, and / or higher-dimensional (e.g., time-varying 2D / 3D map / environmental model). Walls, windows, doors, entrances, exits, and restricted areas may be marked on the map or model. The map may include a floor plan of the facility. The map or model may have one or more layers (overlays). The map / model may be a maintenance map / model including water pipes, gas pipes, cables, air ducts, crawl spaces, ceiling layouts, and / or underground layouts. The venue may be segmented / divided / subdivided / grouped into multiple zones / areas / geographic areas / sectors / sections / regions / districts / areas / regions / areas / areas / broad areas, for example, bedrooms, living rooms, storage rooms, walkways, kitchens, dining rooms, pet rooms, garages, first floors, second floors, break rooms, offices, conference rooms, reception areas, various office areas, various warehouse areas, various facility areas, etc. The segments / areas / regions may be presented in the map / model. Different areas may be color-coded. Different areas may be presented with characteristics (e.g., color, brightness, color intensity, texture, animation, blinking, blinking speed, etc.). Logical segmentation of venues can be performed using at least one heterogeneous type 2 device, or server (e.g., a hub device), or a cloud server, etc.

[0173] Herein lies an example of the disclosed system, apparatus, and method. Stephen and his family wish to install the disclosed wireless motion detection system to detect motion in their 2,000-square-foot, two-story townhouse in Seattle, Washington. Because his house has two staircases, Stephen decided to use one Type 2 device (named A) and two Type 1 devices (named B and C) on the first floor. The first floor mainly consists of three rooms arranged in a straight line: the kitchen, dining room, and living room, with the dining room in the center. The kitchen and living room are on opposite sides of the house. He placed one Type 2 device (A) in the dining room, one Type 1 device (B) in the kitchen, and the other Type 1 device (C) in the living room. With this arrangement of devices, he is effectively dividing the ground floor into three zones (dining room, living room, and kitchen) using the motion detection system. When motion is detected by the A / B pair and the A / C pair, the system analyzes the motion information and associates the motion with one of the three zones.

[0174] When Stephen and his family go out on weekends (for example, to go camping on a long weekend), Stephen uses a mobile phone app (for example, an Android phone app or an iPhone® app) to turn on a motion detection system. When the system detects motion, an alert signal (for example, an SMS text message, email, or push message to the mobile phone app) is sent to Stephen. Stephen pays a monthly fee (e.g., $10 / month) for a service company (e.g., a security company) to receive the alert signal via a wired network (e.g., broadband) or wireless network (e.g., home WiFi, LTE, 3G, 2.5G, etc.) and take security steps with Stephen (e.g., calling him to check on the problem, checking into the house, contacting the police on Stephen's behalf, etc.). Stephen loves his elderly mother and cares about her well-being when he is home alone. While the rest of the family is out (e.g., to go to work, go shopping, or go on vacation), his mother uses her mobile app to turn on the motion detection system and assures him that she is OK. He then uses the mobile app to monitor his mother's movements at home. Stephen uses a mobile app to see his mother moving around the house in three different areas, and according to her daily routine, he knows she's doing well. Stephen appreciates that the motion detection system can help monitor his mother's well-being while he's away from home.

[0175] On a typical day, his mother would wake up around 7 a.m. She would cook breakfast in the kitchen for about 20 minutes. Then she would eat breakfast in the dining room for about 30 minutes. After that, she would exercise in the living room every day and sit on the living room sofa watching her favorite TV shows. The motion detection system allows Stephen to see the timing of movements in each of the three areas of the house. If the movements match the daily routine, Stephen knows that his mother should be fine. However, if the movement pattern looks unusual (for example, no movement until 10 a.m., staying in the kitchen for a long time, or moving for a long time without stopping), Stephen suspects something is wrong and calls his mother to check on her. Stephen can even have someone else (for example, a family member, neighbor, paid person, friend, social worker, or service provider) check on his mother.

[0176] Sometimes, Stephen feels like he's just relocating a Type 2 device. He simply unplugs the device from its original AC power outlet and plugs it into another. He's pleased that the wireless motion detection system is plug-and-play and that relocation doesn't affect the system's operation. It works instantly when powered on.

[0177] Later, Stephen becomes convinced that the disclosed wireless motion detection system can indeed detect motion with very high accuracy and very low alarms, and that he can actually use the mobile app to monitor motion on the ground floor. He decides to install a similar setup (i.e., one Type 2 device and two Type 1 devices) on the second floor to monitor the bedrooms on the second floor. Again, setting up the system is very easy, requiring only plugging the Type 2 and Type 1 devices into AC power outlets on the second floor. No special installation is required. He can also monitor motion on both the first and second floors using the same mobile app. Each Type 2 device on the first / second floor can interact with both Type 1 devices on the first and second floors. Stephen is pleased to see that doubling the investment in Type 1 and Type 2 devices would more than double the capabilities of the combined system.

[0178] According to various embodiments, each CI (CI) may include at least one of the following: channel state information (CSI), frequency domain CSI, frequency domain CSI related to at least one subband, frequency domain CSI, time domain CSI, channel response, estimated channel response, channel impulse response (CIR), channel frequency response (CFR), channel characteristics, channel filter response, radio multipath channel CSI, radio multipath channel information, timestamp, auxiliary information, data, metadata, user data, account data, access data, security data, session data, status data, supervisory data, home data, identification information (ID), identifier, device data, network data, neighbor data, environmental data, real-time data, sensor data, stored data, encrypted data, compressed data, protected data, and / or another CI. In one embodiment, the disclosed system has hardware components (e.g., a radio transmitter / receiver with an antenna, analog circuitry, power supply, processor, memory) and corresponding software components. According to various embodiments of this teaching, the disclosed system includes a bot (referred to as a Type 1 device) and an origin (referred to as a Type 2 device) for detecting and monitoring vital signs. Each device comprises a transceiver, a processor, and memory.

[0179] The disclosed system is applicable in many cases. For example, a Type 1 device (transmitter) may be a small WiFi-enabled device on a table. It may also be a WiFi-enabled television (TV), set-top box (STB), smart speaker (e.g., Amazon Echo), smart refrigerator, smart microwave, mesh network router, mesh network satellite, smartphone, computer, tablet, smart plug, etc. For example, a Type 2 (receiver) may be a WiFi-enabled device on a table. It may also be a WiFi-enabled television (TV), set-top box (STB), smart speaker (e.g., Amazon Echo), smart refrigerator, smart microwave, mesh network router, mesh network satellite, smartphone, computer, tablet, smart plug, etc. Type 1 and Type 2 devices may be placed in / near a conference room to count people. Type 1 and Type 2 devices may also be in a health monitoring system for the elderly to monitor daily activities and any signs of symptoms (e.g., dementia, Alzheimer's disease). Type 1 and Type 2 devices may be used in an infant monitor to monitor the vital signs (respiration) of a living infant. Type 1 and Type 2 devices can be placed in bedrooms to monitor sleep quality and any sleep apnea. Type 1 and Type 2 devices can be placed in vehicles to monitor the health of passengers and drivers, detect driver sleep, and detect infants remaining in the vehicle. Type 1 and Type 2 devices can be used in logistics to prevent human trafficking by monitoring people hidden in trucks and containers. Type 1 and Type 2 devices may be deployed by emergency services in disaster areas to locate victims trapped in debris. Type 1 and Type 2 devices can be deployed in an area to detect the breathing of any intruder. There are many applications for wireless respiratory monitoring without wearables.

[0180] Hardware modules may be constructed to include Type 1 transceivers and / or Type 2 transceivers. Hardware modules may be sold / used under variable brands to design, build and sell final commercial products. Products used in the disclosed systems and / or methods may include home / office security products, WiFi products, STBs, entertainment products, TVs, entertainment products, HiFi, speakers, home appliances, ovens, tables, chairs, beds, tools, torches, vacuum cleaners, sofas, fans, doors, windows, door handles, locks, smoke detectors, car accessories, computing devices, office devices, air conditioners, heaters, connectors, monitoring cameras, access points, mobile devices, LTE devices, 3G / 4G / 6G devices, UMTS devices, GSM devices, EDGE devices, TDMA devices, CDMA devices, WCDMA devices, TD-SCDMA devices, gaming devices, eyeglasses, VR goggles, necklaces, watches, waistbands, belts, wallets, pens, hats, wearables, embedded devices, tags, parking tickets, smartphones, etc.

[0181] The summary may include analysis, output response, selected time window, subsampling, transformation, and / or projection. Presentation may include presenting at least one of the following: month / week / day view, simplified / detailed view, section view, small / large form factor view, color-coded view, comparison view, summary view, animation, web view, audio announcement, and another presentation related to the periodic / repetitive characteristics of the repetitive behavior.

[0182] Type 1 / Type 2 devices may include: antennas, devices with antennas, devices with enclosures (for radios, antennas, data signal processing devices, wireless ICs, circuits, etc.), devices with interfaces for mounting / connecting / linking antennas, devices that interface / mount / connect / link with other devices / systems / computers / telephones / networks / data aggregators, devices with user interfaces (UI) / graphical UI / displays, devices with wireless transceivers, devices with wireless transmitters, devices with wireless receivers, IoT devices, devices with wireless networks, devices with wired and wireless network capabilities, devices with wireless integrated circuits (ICs), Wi-Fi devices, devices with Wi-Fi chips (e.g., compliant with 802.11a / b / g / n / ac / ax standards), Wi-Fi access points (APs), Wi-Fi clients, Wi-Fi routers, Wi-Fi -Fi repeaters, Wi-Fi hubs, wireless mesh network routers / hubs / APs, ad-hoc network routers, wireless mesh network devices, mobile devices (e.g., 2G / 2.5G / 3G / 3.5G / 4G / LTE / 5G / 6G / 7G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA), mobile terminals, base stations, mobile network base stations, mobile network hubs, mobile network compatible terminals, LTE terminals, terminals equipped with LTE modules, mobile modules (e.g., boards equipped with mobile enable chips (ICs) such as Wi-Fi chips, LTE chips, BLE chips, etc.), Wi-Fi chips (ICs), LTE chips, BLE chips, devices equipped with mobile modules, smartphones, smartphone companion devices (dongles, attachments, plug-ins, etc.), dedicated devices, plug-in devices, AC powered devices, battery-powered devices, devices with processors / memory / instruction sets, smart devices / gadgets / items.Clocks, stationery, pens, user interfaces, paper, mats, cameras, televisions, set-top boxes, microphones, speakers, refrigerators, ovens, machines, telephones, wallets, furniture, doors, windows, ceilings, floors, walls, tables, chairs, beds, nightstands, air conditioners, heaters, pipes, ducts, cables, carpets, ornaments. Gadgets, USB devices, plugs, dongles, lamps / lights, tiles, ornaments, bottles, vehicles, automobiles, automated guided vehicles, robots, laptops, tablets, computers, hard drives, network cards, musical instruments, rackets, balls, shoes, wearables, clothing, eyeglasses, hats, necklaces, food, pills, small devices that move inside the body of a living organism (e.g., intravascular, lymphatic, digestive tract), and / or other devices. Type 1 devices and / or Type 2 devices may be communicably connected to the Internet, another device with access to the Internet (e.g., a smartphone), a cloud server (e.g., a hub device), an edge server, a local server, and / or storage. Type 1 devices and / or Type 2 devices may operate under local control, be controlled by another device via a wired / wireless connection, operate automatically, or be controlled by a remote (e.g., remote) centralized system.

[0183] In one embodiment, a Type B device can be a transceiver capable of functioning as both an Origin (Type 2 device, Rx device) and a Bot (Type 1 device, Tx device), i.e., a Type B device can be both a Type 1 (Tx) and a Type 2 (Rx) device (e.g., simultaneously or alternatively), e.g., a mesh device, a mesh router, etc. In one embodiment, a Type A device can be a transceiver capable of functioning only as a Bot (Tx device), i.e., only as a Type 1 device, or only as a Tx, e.g., a simple IoT device. It may have the capabilities of an Origin (Type 2 device, Rx device), but in an embodiment it functions only as a Bot in some way. All Type A and Type B devices form a tree structure. The root may be a Type B device with network (e.g., Internet) access. For example, it may be connected to broadcast services via a wired connection (e.g., Ethernet, cable modem, ADSL / HDSL modem) or a wireless connection (e.g., LTE, 3G / 4G / 5G, WiFi, Bluetooth, microwave link, satellite link, etc.). In one embodiment, all Type A devices are leaf nodes. Each Type B device can be a root node, a non-leaf node, or a leaf node.

[0184] Type 1 devices (transmitters, or Tx) and Type 2 devices (receivers, or Rx) may reside on the same device (e.g., an RF chip / IC), or simply on the same device. The devices may operate in high-frequency bands such as 28 GHz, 60 GHz, and 77 GHz. The RF chip may have dedicated Tx antennas (e.g., 32 antennas) and dedicated Rx antennas (e.g., another 32 antennas).

[0185] A single Tx antenna can transmit a radio signal (for example, a sequence of probe signals, perhaps at 100 Hz). Alternatively, all Tx antennas may be used to transmit radio signals using beamforming (in Tx), resulting in the radio signal being focused in a certain direction (for example, for energy efficiency, or to boost the signal-to-noise ratio in that direction, or for low-power operation when "scanning" in that direction, or for low-power operation when an object is known to be in that direction).

[0186] Radio signals can strike objects within a venue (e.g., a room) (e.g., a living person lying on a bed four feet away from the Tx / Rx antenna, breathing, and heartbeat). The movement of objects (e.g., lung movement in response to respiratory rate, or blood vessel movement in response to heartbeat) can affect / modulate the radio signal. All Rx antennas can be used to receive radio signals.

[0187] Beamforming (in Rx and / or Tx) can be applied (digitally) to "scan" different directions. Many directions can be scanned or monitored simultaneously. In beamforming, "sectors" (e.g., direction, orientation, bearing, zone, region, segment) can be defined in relation to a Type 2 device (e.g., relative to the center position of an antenna array). For each probe signal (e.g., pulse, ACK, control packet, etc.), channel information or CI (e.g., channel impulse response / CIR, CSI, CFR) is acquired / calculated sector by sector (e.g., from an RF chip). In spray detection, CIR can be collected in a sliding window (e.g., 30 seconds; at a 100 Hz sounding / probing rate, there may be 3000 CIRs exceeding 30 seconds).

[0188] A CIR can have many taps (e.g., N1 component / tap). Each tap can be associated with a time lag, or time of flight (e.g., the time it takes for a person to hit their back from 4 feet away). When a person is breathing in a certain direction at a certain distance (e.g., 4 ft), the CIR for "that direction" can be searched. Then, the tap corresponding to "that distance" can be searched. Then, the respiratory rate and heart rate can be calculated from the taps of that CIR.

[0189] Each tap within the sliding window (e.g., the 30-hour window in the "Component Time Series") can be considered a time function (e.g., "Tap Function," "Component Time Series"). When exploring strong periodic behavior (e.g., perhaps corresponding to respiration in the range of 10 bpm to 40 bpm), each tap function can be examined.

[0190] Type 1 devices and / or Type 2 devices may have external connections / links and / or internal connections / links. External connections (e.g., connection 1110) may be associated with 2G / 2.5G / 3G / 3.5G / 4G / LTE / 5G / 6G / 7G / NBIoT, UWB, WiMAX, Zigbee, 802.16, etc. Internal connections (e.g., 1114A and 1114B, 1118, 11120) can be associated with WiFi, IEEE 802.11 standards, 802.11a / b / g / n / ac / ag / af / ah / ai / aj / ax / ay, Bluetooth, Bluetooth 1.0 / 1.1 / 1.2 / 2.0 / 2.1 / 3.0 / 4.0 / 4.0 / 4.1 / 4.2 / 5, BLE, mesh networks, IEEE 802.16 / 1 / 1a / 1b / 2 / 2a / b / b / c / d / e / f / g / h / i / j / k / l / m / n / o / p standards, etc.

[0191] Type 1 devices and / or Type 2 devices are powered by batteries (e.g., AA batteries, AAA batteries, coin cell batteries, button batteries, miniature batteries, battery banks, power banks, automotive batteries, hybrid batteries, container batteries, non-rechargeable batteries, secondary batteries, NiCd batteries, NiMH batteries, lithium-ion batteries, zinc-carbon batteries, zinc chloride batteries, lead-acid batteries, alkaline batteries, batteries with wireless chargers, smart batteries, solar cells, boat batteries, plain batteries, other batteries, temporary energy storage devices, capacitors, flywheels).

[0192] Any device may be powered by DC or direct current (for example, from a battery, generator, power converter, solar panel, rectifier, DC-DC converter, etc., having various voltages such as 1.2V, 1.5V, 3V, 5V, 6V, 9V, 12V, 24V, 40V, 42V, 48V, 110V, 220V, 380V, etc., as described above), and therefore may have a DC connector or a connector having at least one DC power pin.

[0193] Any device may be powered by AC or alternating current (e.g., household wall outlets, transformers, inverters, showers, etc., with various voltages such as 100V, 110V, 120V, 100-127V, 200V, 220V, 230V, 240V, 220-240V, 100-240V, 250V, 380V, 50Hz, 60Hz, etc.), and therefore may have an AC connector or a connector having at least one pin for AC power. Type 1 devices and / or Type 2 devices may be located inside or outside the venue (e.g., installed, positioned, moved).

[0194] For example, in a vehicle (e.g., automobile, truck, lorry, bus, special vehicle, tractor, excavator, shovel, teleporter, bulldozer, crane, forklift, electric trolley, AGV, emergency vehicle, cargo, freight car, trailer, container, boat, ferry, ship, submarine, aircraft, lift, monorail, train, railway vehicle, etc.), Type 1 devices and / or Type 2 devices may be embedded devices embedded in the vehicle, or add-on devices (e.g., aftermarket devices) plugged into a port on the vehicle (e.g., OBD port / socket, USB port / socket, accessory port / socket, 12V auxiliary power outlet, and / or 12V cigarette lighter port / socket).

[0195] For example, one device (e.g., a Type 2 device) may be plugged into a 12V cigarette lighter / accessory port, an OBD port, or a USB port (e.g., in a car / truck / vehicle), and the other device (e.g., a Type 1 device) may be plugged into a 12V cigarette lighter / accessory port, an OBD port, or a USB port. The OBD port and / or USB port may provide power, signal, and / or networking (to the car / truck / vehicle). The two devices may jointly monitor passengers, including children / babies, in the vehicle. They may be used to count passengers, recognize the driver, and detect the presence of passengers in specific seats / positions within the vehicle.

[0196] In another example, one device may be plugged into a 12V cigarette lighter / accessory port, an OBD port, or a USB port in a car / truck / vehicle, while the other device may be plugged into a 12V cigarette lighter / accessory port, an OBD port, or something else.

[0197] In another example, many heterogeneous vehicles / portable devices / smart gadgets (e.g., automated guided vehicles / AGVs, shopping / luggage / mobile carts, parking tickets, golf carts, bicycles, smartphones, tablets, cameras, recording devices, smartwatches, roller skates, shoes, jackets, goggles, hats, eyewear, wearables, Segways, scooters, luggage tags, cleaning machines, vacuum cleaners, pet tags / colors / wearables / implants) may have many Type A (e.g., Type 1 or Type 2) devices, each connected to the vehicle's 12V accessory port / OBD port / USB port or built into the vehicle. One or more other Type B (e.g., if A is Type 2, B is Type 1; if A is Type 1, B is Type 2) devices may be installed in scattered locations to cover larger areas such as gas stations, streetlights, street corners, tunnels, multi-story parking garages, factories / stadiums / stations / shopping malls / construction sites. Type A devices may be located, tracked, and monitored based on TSCI.

[0198] Areas / venues may not have local connectivity, such as broadband service or Wi-Fi. Type 1 and / or Type 2 devices may be portable. Type 1 and / or Type 2 devices may support plug-and-play.

[0199] A pairwise radio link can be established between many pairs of devices forming a tree structure. In each pair (and associated link), the device (second device) may be a non-leaf (Type B). Other devices (first devices) may be a leaf (Type A or Type B) or a non-leaf (Type B). In the link, the first device functions as a bot (Type 1 device or Tx device) for transmitting radio signals (e.g., probe signals) to the second device via a radio multipath channel. The second device may function as an origin (Type 2 device or Rx device) for receiving radio signals, acquiring TSCI, and calculating "linkwise analytics" based on TSCI.

[0200] In some embodiments, this teaching discloses systems and methods for wireless sensing. In one embodiment, a Type 1 heterogeneous wireless device or a Type 2 heterogeneous wireless device is one of a number of heterogeneous wireless devices or stations (STAs) in space.

[0201] Roles in wireless sensing: A Type 1 device, a Type 2 device, or another STA can function as a sensing initiator. A sensing initiator is an STA that initiates a wireless sensing procedure (or a sensing procedure using WiFi, WLAN, 5G, UWB, millimeter wave, WiMAX, WiGig, Bluetooth, or another wireless system). At least one STA (e.g., a Type 1 device, a Type 2 device, a sensing initiator, a sensing transmitter, a sensing receiver, or another STA) can function as a sensing responder. A sensing responder may be an STA that participates in a sensing procedure initiated by a sensing initiator. At least one STA (e.g., a Type 1 device, a Type 2 device, a sensing initiator, a sensing responder, a sensing receiver, or another STA) can function as a sensing transmitter. A sensing transmitter may be an STA that transmits radio signals used for sensing measurements in a wireless sensing procedure (e.g., Physical Layer Protocol Data Units (PPDUs), Data Packet Frames (NDPs), NDP Announcement (NDPA) frames in WiFi, or any sounding signals). At least one STA (e.g., a Type 1 device, a Type 2 device, a sensing initiator, a sensing responder, a sensing transmitter, or another STA) can function as a sensing receiver. A sensing receiver may be an STA that receives radio signals transmitted by a sensing transmitter (e.g., PPDUs, NDPAs, NDPs, or any sounding signals in WiFi) and performs sensing measurements in a WLAN sensing procedure.

[0202] An STA can assume one or more possible roles in one (or more) sensing procedures (e.g., sensing initiator, sensing receiver, sensing transceiver, sensing receiver, sensing contributor, SBP requesting STA). In a sensing procedure, a sensing initiator may be a sensing transmitter, a sensing receiver, both, or neither. In a sensing procedure, a sensing responder may be a sensing transmitter, a sensing receiver, or both.

[0203] Sensing Procedure: The sensing procedure enables the STA to perform sensing and acquire measurement results. The sensing procedure may consist of one or more of the following: sensing session setup, sensing measurement setup, sensing measurement instance, sensing measurement setup completion, and sensing session completion. The sensing procedure may also consist of one or more sensing measurement instances.

[0204] Sensing Session: A sensing session can be an agreement between a sensing initiator and a sensing responder to participate in a sensing procedure. A sensing procedure can consist of zero or at least one sensing measurement instance. Several examples of sensing procedures are shown in Figures 1 and 2.

[0205] The Measurement Setup ID can be used to identify the attributes of a sensing measurement instance. The Measurement Instance ID can be used to identify a sensing measurement instance that uses the same attributes as the Measurement Setup ID. The Dialog Token field may be used to include both the Measurement Setup ID and the Measurement Instance ID. At least one type of sensing measurement result can be defined. The sensing transmitter and sensing receiver roles of the STA corresponding to the Measurement Setup ID may be fixed, immutable, changed, modified, or adjusted as determined during the sensing measurement setup until the sensing measurement setup is completed.

[0206] In the sensing session setup of the sensing procedure, a sensing session may be established, and operating parameters related to the sensing session may be determined and exchanged between STAs. Sensing sessions may be pairwise and may be identified by MAC address, associated AID / UID, session ID, or another ID. A sensing initiator may maintain multiple sensing sessions. An STA may be a sensing initiator in one session and a sensing responder in another.

[0207] An optional negotiation process in a sensing measurement setup may be defined to allow the sensing initiator and sensing responder to exchange and agree on operational attributes related to the sensing measurement instance. These operational attributes may include the roles of the initiator and responder, the measurement reporting type (for non-local reporting, local reporting, or both), and other operational parameters.

[0208] In a sensing measurement instance of a sensing procedure, sensing measurements may be performed to obtain sensing measurement results. Multiple sensing responders may participate in a sensing measurement instance. There are at least two types of sensing measurement instances: (a) trigger-based (TB) sensing measurement instances and (b) non-TB sensing measurement instances.

[0209] TB Sensing Measurement: A TB sensing measurement instance may consist of a polling phase, an NDPA sounding phase, a trigger frame (TF) sounding phase, a reporting phase, and / or an LTF security update phase. The order of the sounding phases may vary, with NDPA sounding preceding TF sounding, or vice versa. The order may change over time.

[0210] Figure 3 shows some examples of possible TB sensing measurement instances. As shown in Figure 3, Examples 3 and 4 show two sounding sequences. The reporting phase in Example 5 may be temporally separated from the sounding phase. This may be delayed reporting. Polling in the reporting phase in Example 5 may be directed to responders other than those involved in the sounding.

[0211] Polling Phase: During the polling phase, APs (WiFi access points, 3G / 4G / 5G / 6G base stations, hubs, etc.) may send trigger frames to check the availability of the STA. If the STA is available, it may respond with a CTS-to-self.

[0212] NDPA Sounding: The NDPA sounding phase may be present in a TB sensing measurement instance if at least one STA, which is a sensing receiver, responds during the polling phase. The NDPA sounding phase may consist of (a) transmission of a sensing NDP Announcement (NDPA) frame by the AP, and (b) transmission of an NDP by the AP after the transmission of the sensing NDPA frame. NDPs may be used for channel measurements between a sensing transceiver and a sensing receiver (e.g., CI, CSI, CIR, CFR, etc.) (e.g., sub-7GHz band).

[0213] TF Sounding: If at least one STA, which is a sensing transmitter, responds during the polling phase, a trigger frame (TF) sounding phase may exist in the TB sensing measurement instance. The TF sounding phase may consist of (a) the AP sending a trigger frame (TF) to request an NDP transmission from the STA, and (b) the STA sending an NDP after receiving the trigger frame. The NDP is used for channel measurements (e.g., CI, CSI, CIR, CFR, etc.) between the sensing transmitter and the sensing receiver (e.g., in the sub-7GHz band).

[0214] Local / Non-Local Reporting: In the reporting phase of a sensing measurement instance, sensing measurement results may be reported. Measurement results performed in the sensing procedure may be reported and / or obtained locally, non-locally, both (i.e., both local and non-local), or not reported (i.e., not reported, e.g., if the variation in CI is below a threshold that suggests the CI is essentially the same as the previous CI).

[0215] When reported locally, sensing measurement results may be reported locally at the sensing receiver or at the location where the sensing measurement results can be measured. Such local reporting may be implemented via some software interface, such as a Media Access Control (MAC) sublayer management entity (MLME) primitive or a firmware application program interface (or API). Some applications or high-level software may use a software interface (e.g., using software interrupts) to acquire or read sensing measurement results.

[0216] When reported non-locally, measurement results may be reported non-locally to other devices or STAs (e.g., sensing initiators, sensing responders, sensing transmitters, sensing receivers, Type 1 devices, Type 2 devices, adjacent STAs, other STAs, or at least one of any local or cloud servers).

[0217] Measurement results may be reported both locally and non-locally (e.g., simultaneously, simultaneously, alternately, selectively, adaptively, and / or on-demand / scheduled / planned methods). Within a measurement "span" (period), measurement result reporting may consist of a combination of local and non-local reports (e.g., simultaneous local / non-local reporting, alternate local / non-local / both / none reporting, local / non-local / both / none reporting selected by a specific mechanism, adaptively determined local / non-local / both / none reporting, on-demand local / non-local / both / none reporting, scheduled local / non-local / both / none reporting, local / non-local / both / none reporting in response to several planned (e.g., threshold-based) situations / states / events).

[0218] Settings (for example, a combination of local and non-local reporting, any setup parameters, any session setup parameters, or one of the measurement setup parameters) may be applied to a "measurement span." A measurement span may include multiple sensing initiators, or the identities of sensing initiators, or sensing initiator IDs, or multiple sessions associated with one (or more) sensing initiators, or sessions, or session identities, or session IDs, or multiple measurement setups within a session, or measurement setups, or measurement setup IDs, or measurement setup IDs, or multiple measurement instances associated with a measurement setup (or measurement setup ID), or measurement instances associated with a measurement instance number.

[0219] In simultaneous reporting, both local and non-local reporting may be performed simultaneously or concurrently. In alternating reporting, measurement instances are divided into groups of consecutive sensing results, with the first group reported by the first method, the second group by the second method, the third group by the third method, and so on, where each of the first, second, third, fourth, fifth, etc. methods can be (a) local only, (b) non-local only, (c) both local and non-local, or (d) none (not reported).

[0220] The type, accuracy, processing, and other specifications of measurement results reported non-locally in a sensing procedure / session / measurement instance may be determined (non-locally) by the sensing initiator. The type, accuracy, processing, and other specifications of measurement results reported locally in a sensing procedure may be determined (locally) via the software interface (e.g., MLME primitive) of the sensing receiver or the location where the results are measured. Local / non-local reporting can be enabled / disabled (non-locally) by the sensing initiator, or by negotiation between the sensing initiator and the sensing responder. Switching between local and non-local reporting may be entirely controlled by the sensing initiator, or partially controlled by the sensing initiator and partially controlled via the sensing responder's software interface.

[0221] Sensing Measurement Report Frame: A Sensing Measurement Report frame may be defined that enables a sensing receiver to report sensing measurements non-locally. This frame may include at least two fields: (a) a measurement report control field containing information necessary to interpret the measurement report field, and (b) a measurement report field that carries the sensing measurement result obtained by the sensing receiver (e.g., channel information, CI, CSI, CIR, CFR, RSSI, or some variation).

[0222] Local and non-local reporting can be initiated by their respective MLMEs. The transmission of Sensing Measurement Report frames can be initiated by MLME primitives. Both immediate and delayed reporting may be performed.

[0223] At the end of a sensing session, the STA stops executing measurements and terminates the sensing session.

[0224] Threshold-Based Reporting: Optional threshold-based measurement and reporting procedures may be performed. The difference between the currently measured CI (e.g., CSI) and the previously measured CI (e.g., CSI) may be quantified. This difference may be called CI variation. Thresholds used by the sensing receiver in threshold-based procedures may be defined (e.g., by the sensing initiator, sensing responder, sensing transmitter, sensing receiver, another STA, and / or some server). By comparing the CI variation to the threshold, the sensing receiver may report the measurement result (e.g., original or converted, uncompressed or compressed measurement result) if the CI variation is likely to be large (e.g., larger than a certain threshold, or falling into the "large" category).

[0225] Options for CI variation: CI variation may consist of multiple quantities / measurements / options (for example, two CI variation measurements may be selected from seven options). As an example, options for CI variation may include any of the following: difference (the difference between the current CI and the previous CI), difference in moving averages (the difference between the moving average of the current CI and the moving average of the previous CI), difference in magnitude (or L1-norm, i.e., the difference between the magnitude of the current CI and the magnitude of the previous CI), difference in power (the difference between the power of the current CI and the power of the previous CI), difference between the current value and the moving average (the difference between the current CI and the moving average of the CI), high-pass or band-pass filter output, dot product (the dot product of the current CI and the previous CI, both vectors), dot product of moving averages, dot product of magnitudes, dot product of powers, autocorrelation function (of the CI), autocorrelation function of the magnitude of the CI, autocorrelation of the power of the CI, autocorrelation of the function of the CI, autocovariance, etc.

[0226] Selection: The same, similar, or different threshold-based measurement and reporting procedures may be applied to local and non-local reports, (a) the quantities of CI variation measures used for local and non-local reports may be the same or different, (b) the choices of CI variation measures used for local and non-local reports may be the same or different, and / or (c) the thresholds used for local and non-local reports may be the same and / or different.

[0227] In the case of non-local reporting, the enable / disablement of threshold-based reporting, the amount and options for CI change, and the corresponding threshold can be determined by the sensing initiator. It may also be determined by at least one of the sensing responder, sensing transmitter, Type 1 device, another STA, and / or server. It may also be determined locally by the sensing receiver or Type 2 device. Some precision reduction measures (quantization, approximation, etc.) may be applied to the measurement results before they are reported non-locally.

[0228] In the case of local reporting, the enable / disablement of threshold-based reporting, the amount and options of CI variation, and the corresponding thresholds can be determined locally by the sensing receiver or Type 2 device. Alternatively, they may be determined non-locally by at least one of the sensing initiator, sensing responder, sensing transmitter, Type 1 device, another STA, and / or some server. In the case of local reporting, precision reduction measures applied to the measurement results prior to non-local reporting may or may not be applied (i.e., may or may not be skipped). In the case of local reporting, the measurement results may be reported with the highest precision supported by the sensing receiver hardware.

[0229] Buffering Timeout: During a sensing session, measurement results associated with a measurement instance and its corresponding measurement setup ID are buffered and may remain available for local / non-local reporting on the sensing receiver (or Type 2 device) for a period comparable to the sounding period associated with the measurement setup ID (e.g., a percentage of the sounding period). The sounding period associated with the measurement setup ID is the target time between two consecutive measurement instances, negotiated in the sensing measurement setup.

[0230] CSI:CI as a measurement result (e.g., CSI, CIR, CFR, RSSI, or the channel measured during the training symbol of the received PPDU) may be a type of sensing measurement result (e.g., for sub-7GHz WiFi / WLAN). To enable sensing, a parameter (e.g., RXVECTOR parameter CI_ESTIMATE) may be defined that includes the channel measured during the training symbol of the received radio signal (e.g., WiFi / WLAN PPDU). The format of the parameter (e.g., CI_ESTIMATE) may be the same as that used in the measurement report field in the Sensing Measurement Report frame.

[0231] Centralized Computing: Some sensing networks composed of STAs form a centralized sensing system where most high-level sensing computation tasks based on sensing measurement results (or "consumption" of sensing results, e.g., motion detection, respiration detection / monitoring, fall detection, etc.) are centrally performed by a centralized device (which may be an STA and a sensing initiator, or a device that requests the STA to function as a sensing initiator). On the other hand, the vast majority of STAs (sensing responders, sensing transmitters, sensing receivers, Type 1 devices, Type 2 devices, etc.) do not participate in high-level sensing tasks. If sensing measurement results are generated in a centralized device, the centralized device may use only local reports, and centralized computation of high-level sensing computation tasks may be performed, meaning that sensing measurement results do not need to be sent from most STAs to the centralized device (this requires many network resources, communication time, bandwidth, and hardware / software resources, and involves considerable time delays). If sensing measurements are generated across the majority of the STA, non-local-only reporting is used across the majority of the STA, sending all measurement results to a centralized device, which then performs centralized computing for high-level sensing computation tasks. However, such non-local-only reporting can consume a considerable amount of network resources, communication time, bandwidth, and hardware / software resources, potentially resulting in significant time delays.

[0232] Distributed Computing: Several sensing networks form a distributed sensing system in which most high-level sensing computation tasks based on sensing measurement results are distributed or shared among most STAs, and the results of each high-level task are sent to a centralized device (e.g., STAs and sensing initiators, or devices that request STAs to act as sensing initiators) for fusion and / or further processing. If sensing measurement results are generated by most STAs, local-only reporting may be performed, and distributed computing of high-level sensing tasks may be performed. If sensing measurement results are generated by a centralized device, the centralized device may need to send each result to each STA for distributed computing.

[0233] For example, there may be a base device that functions as a sensing initiator (e.g., a WiFi access point / AP, or a 3G / 4G / 5G / 6G / 7G / 8G base station, or hub), and a large number of client devices (e.g., WiFi IoT devices, mobile phones, or 3G / 4G / 5G / 6G / 7G / 8G client devices).

[0234] Case 1: The base device uses trigger-based (TB) sensing measurements by sending trigger frames (TFs) to request NDP from the client device, and the measurement results are generated on the base device. In this method, only local reporting is performed on the base device, and central computing for high-level tasks is performed on the base device.

[0235] Case 2: The base device can use non-TB sensing measurements by sending NDPA and NDP to the client device so that the client device generates the measurement results. In this way, the client device performs only local reporting, and the client device performs distributed computing for high-level tasks. The client can send the results of the high-level tasks to the base device for fusion and further processing.

[0236] Example (Proxy): In another example, an initiating device (e.g., STA as a real sensing initiator) can request a base device to act as a sensing initiator (proxy sensing initiator) to establish a sensing network with IoT devices. In Case 1, the base device sends all measurement results to the initiating device, and the initiating device can perform centralized computation of high-level tasks. In Case 2, the client device sends the results of high-level tasks to the base device, and the base device sends them to the initiating device for fusion and further processing (at the initiating device).

[0237] Sharing of Measurement Instances: In one embodiment, a measurement instance may be associated with a single measurement setup. In another embodiment, sharing of measurement instances within a session may be disclosed. A measurement instance may be shared by multiple measurement setups within a sensing session (by being associated with multiple measurement setup IDs, the same initiator-responder pair), and measurements are performed using a “shared” or “combined” measurement setup, which may be a superset encompassing multiple measurement setups (for example, if one setup is 300 Hz with two antennas and another setup is 200 Hz with three antennas, the combined setup may be 300 Hz with three antennas). The combined sounding frequency may be greater than or equal to the sounding frequencies of the multiple measurement setups, and less than or equal to their least common multiple (LCM) (for example, the LCM for 200 and 300 is 600). For example, the combined sounding frequency may be the maximum value of the sounding frequencies (for example, the maximum value for 200 and 300 is 300), or it may be the LCM. Sharing such measurement instances can be useful when the large sampling times of multiple measurement setups coincide or are very close to each other. The number of coupled antennas may be the maximum number of antennas across the multiple measurement setups.

[0238] For example, sharing measurement instances is useful for two measurement setups where only the sounding frequency differs, with one sounding frequency being the factor for the other (e.g., 100Hz vs. 200Hz, with all other settings being the same). By sharing measurement instances, all slower (lower sounding frequency) measurement instances are absorbed into the faster ones. Sharing measurement instances can save 100 instances (from 300 instances per second previously to 200 instances per second now). This results in a significant reduction in network resources (communication time, bandwidth, hardware / software usage).

[0239] As another example, sharing measurement instances is useful in two measurement setups that differ only in their sounding frequencies, where the two sounding frequencies have a sufficiently large GCF (Greatest Common Divisor), e.g., 200Hz vs. 300Hz, GCF=100, and all other settings are identical. By sharing measurement instances, 10 measurement instances can be saved (from 500 total measurement instances per second previously to 400 per second now). Generally, if GCF=N, N measurement instances can be saved per second. Two measurement instances can be merged or shared if their GCF is greater than a threshold.

[0240] In another embodiment, the sharing of measurement instances across multiple sessions may be disclosed. There may be multiple sessions, each associated with a unique session ID. The measurement instance may be shared by multiple measurement setups across multiple sensing sessions (multiple sessions corresponding to multiple initiator-responder pairs, by being associated with multiple measurement setup IDs and multiple session IDs), and measurements are performed using a “shared” or “combined” measurement setup, which may be a superset encompassing the multiple measurement setups. Such sharing of measurement instances is useful for eliminating or avoiding “redundant” measurement instances when different initiator-responder pairs select similar or identical sets of measurement setup parameters.

[0241] For example, a smart TV can establish a first sensing session with an AP, where the AP is the sensing initiator. A smart thermostat can establish a second sensing session with the AP, with the AP acting as the sensing initiator. Both sensing sessions may have identical or very similar sets of measurement setup parameters (for example, "identical" means both have 100Hz, and "similar" means 100Hz vs. 200Hz, with all other settings being identical). For example, a clever professor might publish a paper sharing a very good set of measurement setup parameters (e.g., 100Hz). In the "identical" case, both the TV and the thermostat may have identical settings (e.g., both 100Hz) because they are designed based on the published results. In the "similar" case, one of the devices may have adjusted its sounding frequency from 100Hz to 200Hz to achieve the required performance, resulting in "similar" settings (100Hz vs. 200Hz). As explained earlier, by enabling the sharing of measurement instances across multiple sessions, we can achieve a saving of 100 instances per second.

[0242] Generally, two measurement instances (within the same session or across multiple sessions) related to two different measurement setups may be "merged" or "shared" if the difference between their sampling times is likely to be below a threshold.

[0243] In some embodiments, the application can benefit from "local" reporting / consumption of sensing measurements (e.g., CSI) at the sensing receiver, instead of "non-local" reporting (sent to the sensing initiator using sensing measurement reporting frames) / consumption (by the sensing initiator). For example, the sensing initiator and sensing receiver may be designed / operated by the same company to jointly perform sensing tasks. The sensing initiator is designed to set up a WLAN sensing network, and the sensing receiver is designed to perform most of the sensing calculations (e.g., motion / breathing detection) locally based on locally reported sensing measurements. The locally calculated sensing results (much simpler than the raw sensing measurements) may be sent to the sensing initiator for fusion / further processing.

[0244] This significantly reduces the heavy network resources (signaling, bandwidth, communication time, latency) required for non-local reporting, as the sensing measurements (e.g., CSI) are large in scale. Local consumption distributes sensing computations across many sensing receivers, resulting in relatively low computation / memory requirements for each. In contrast, non-local consumption concentrates all sensing computations on the sensing initiator, resulting in high computation / memory requirements. Thus, according to some embodiments of this teaching, sensing measurements are reported locally at the sensing receivers via MLME primitives.

[0245] In some embodiments, the threshold-based procedure is extended from non-local reporting to local reporting of sensing measurements. For local reporting, an optional “threshold-based local reporting” may be defined. In some embodiments, the optional “threshold-based local reporting” (and associated thresholds) may be selected / deselected by an MLME primitive. If sensing measurements can be reported locally at the sensing receiver, “threshold-based local reporting” may apply to local reporting at the sensing receiver. Threshold-based local reporting means that sensing measurements are reported locally if the “variation in sensing measurements” is greater than a threshold.

[0246] In various embodiments, "threshold-based local reporting" can be applied in the sensing receiver in an optional or mandatory manner. In some embodiments, the threshold used in "threshold-based local reporting" can be set via MLME in the sensing receiver. In some embodiments, at least one "sensing measurement variation" (SMV) is available for "threshold-based local reporting". In some embodiments, one of the at least one SMV is selected via MLME in the sensing receiver.

[0247] In some embodiments, a wireless device may reduce the accuracy of a sensing measurement by performing some quantization on the sensing measurement (e.g., CSI) before transmitting it in a sensing measurement report frame. This helps to reduce computational complexity, lower hardware costs, and achieve increased / sustained throughput. However, for local consumption by the sensing receiver (or local reporting of the sensing measurement), the sensing measurement can / should be reported with the highest possible accuracy.

[0248] In some embodiments, accuracy reduction means applied to the sensing measurement can be skipped for the purpose of non-local reporting at the sensing receiver. In some embodiments, the sensing measurement may be reported locally at the sensing receiver via MLME at the highest accuracy supported by the sensing receiver hardware.

[0249] In some embodiments, sensing measurement results may require a considerable amount of memory to be stored / buffered in the sensing receiver. Because the memory allocated to the sensing receiver hardware is limited, buffering new sensing measurement results can be expensive, if not impossible, if older sensing measurement results are not "cleared." "Cleared" means either being sent for non-local reporting or read for local reporting.

[0250] In some embodiments, older sensing measurement results may be overwritten by newer ones. Therefore, sensing measurement results associated with a measurement instance having a measurement setup ID should be buffered for a period equivalent to the sounding period associated with the measurement setup ID (e.g., a percentage of the sounding period, such as 50%) and made available for local / non-local reporting. This allows higher-level applications to know when sensing measurement results should be retrieved using MLME primitives.

[0251] The sounding duration associated with the measurement setup ID is the target duration between two consecutive measurement instances, negotiated in the corresponding sensing measurement setup. In some embodiments, a statement is added to the SPF that sounding measurements associated with a measurement instance having a measurement setup ID should be buffered for a duration comparable to the sounding duration associated with the measurement setup ID and available for local / non-local reporting.

[0252] In various embodiments, there are different methods for reporting sensing measurement results. Firstly, sensing measurement results can be reported non-locally only, without local reporting, which is suitable for centralized sensing systems. Secondly, sensing measurement results can be reported locally only, without non-local reporting, which is suitable for fully distributed sensing systems. Thirdly, sensing measurement results can be reported both locally and non-locally, which is suitable for hybrid sensing systems. Fourthly, there may be no reporting of sensing measurement results, or reporting may be suspended / stopped. For example, a sensing measurement / session may be suspended for privacy protection. Suspension can be achieved by ending the measurement setup and resuming it later by starting a new measurement setup.

[0253] Following the first method, only non-local reporting of sensing measurement results is performed using sensing measurement reporting frames, and no local reporting is performed. In this method, all sensing measurement results are sent elsewhere and consumed non-locally. This is useful in centralized sensing systems where the sensing responder does not participate in the consumption of sensing measurements. In other words, the sensing responder does not perform high levels of WLAN sensing computation (motion detection / monitoring, breathing, falls, etc.). This increases network traffic, communication time, or resources spent on transmitting raw sensing measurements (e.g., CSI) and causes considerable time delays (for the sensing initiator to collect all sensing measurements). Furthermore, this requires centralized computing and imposes high computation / storage requirements on the sensing initiator. This method is suitable for "cooperative" sensing receivers that only cooperate in generating and sending back sensing measurements.

[0254] Following the second method, only local reporting of sensing measurement results is performed, and no non-local reporting is performed. In this method, all sensing measurement results are consumed locally at the upper layer of the sensing responder and are not transmitted to the sensing initiator. This is useful in distributed cases where each sensing receiver performs high-level WLAN sensing computations (motion detection / monitoring, breathing, falls, etc.) related to (local) sensing measurements. The sensing initiator plays the role of setting up the sensing responders to form a WLAN sensing network. The sensing initiator can function as either a sensing transmitter or a sensing receiver. In some embodiments, the sensing receiver can share the computed sensing results (which require little to no network bandwidth, communication time, or resources) with the sensing initiator for fusion / further processing. This only induces local computing and has lower computing / storage requirements than the first method. There are no network resources used to transmit demanding raw sensing measurements, making it suitable for “partner” sensing receivers that help perform some of the high-level computing.

[0255] Following the third method, both local and non-local reporting are performed, and sensing measurement results are consumed both locally and non-locally. This is useful in hybrid cases where both the sensing responder and sensing initiator participate in the consumption of sensing measurements. In other words, the sensing responder shares some high-level WLAN sensing computations with the sensing initiator. Similar to the first method, the third method involves greater network traffic, communication time, or resources spent on transmitting raw sensing measurements (such as CSI). Because the third method performs both centralized and distributed computing, the sensing initiator has similar computation / memory requirements to the first method, and the sensing receiver has similar requirements to the second method.

[0256] A hybrid case refers to a combination of centralized and distributed computing. For example, suppose the AP acts as a sensing initiator, with many IoT devices acting as sensing responders. (Alternatively, the AP may be required to act as a proxy.)

[0257] An exemplary "recipe" for centralized computing is as follows: The AP uses TF to perform TB sensing measurements, requests NDP from IoT devices, and has the measurement results generated at the AP. For centralized computing, only local reporting is sufficient. Measurement results are not transmitted wirelessly.

[0258] An exemplary "recipe" for distributed computing is as follows: The AP can use non-TB sensing measurements by sending NDPA+NDP to IoT devices so that the measurement results are generated on the IoT devices. Distributed computing on IoT devices only requires local reporting; measurement results are never transmitted wirelessly.

[0259] In some embodiments, the choice between local and non-local reporting remains the same for measurement instances with the same measurement setup ID. Local or non-local reporting can be selected at different levels or granularities, including session setup level (e.g., local / non-local reporting selection that applies to all measurement setups in a session), measurement setup level (e.g., local / non-local reporting selection that applies to one measurement setup), or both (e.g., one bit of the session level indicating session level or measurement setup level, followed by selection at the corresponding level).

[0260] In some embodiments, one measurement instance is associated with one measurement setup. However, in measurements performed using “shared” measurement setups, it may be useful to associate one measurement instance with multiple measurement setups within a sensing session. In the first example, the multiple shared measurement setups may be two measurement setups where a large number of measurement instances match each other. In the second example, the two or more shared measurement setups may be two measurement setups that differ only in their sounding frequencies, where F1 is the factor of F2 (e.g., 10Hz vs. 20Hz, all other settings are identical, and 30 instances / second becomes 20 instances / second). In the third example, the multiple shared measurement setups are two measurement setups that differ only in their sounding frequencies and have a large greatest common denominator (GCF) (e.g., 20Hz vs. 30Hz, all other settings are identical, GCF=10, and 20+30=50 instances / second becomes 40 instances / second. If GCF=N, the instance savings are N). In the fourth example, multiple shared measurement setups may be two measurement setups with different sounding frequencies and antenna counts (e.g., 10Hz / 3 antennas vs. 20Hz / 2 antennas, "shared" measurement setup = 3 antennas). Sharing sensing measurements can reduce the total number of measurement instances, meaning less communication time / bandwidth / network resources for sensing, less buffering memory, lower power consumption, longer battery life, etc.

[0261] In some embodiments, it may be useful to share measurement instances between different sensing sessions (e.g., different initiator-responder pairs) where measurements are performed using a “shared” measurement setup. When the initiator is an AP and the responders are IoT devices, several “common” and “good” settings are likely to be used by many IoTs, causing their measurement setup parameters to be very similar. In one example, a shared measurement setup in TB-based sensing would be two measurement setups that differ only in their sounding frequency, one being the other (e.g., a smart TV wants 10Hz, while a smart speaker wants 20Hz, and all other settings are identical). In another example, a shared measurement setup in TB-based sensing would be two measurement setups that differ in their sounding frequency and number of antennas (e.g., a smart TV wants 10Hz with three antennas, while a smart speaker wants 20Hz with two antennas, and the “shared” measurement setup would be three antennas). Sharing sensing measurements can reduce the total number of measurement instances, which means reduced communication time / bandwidth / network resources for sensing, reduced buffer memory, lower power consumption, and extended battery life.

[0262] In some embodiments, the sounding transmitter can associate a measurement instance with up to N measurement setup IDs, where N is an integer. Of the N measurement setup IDs, local / non-local reporting is performed in a predefined order (e.g., incrementing) of the measurement setup IDs.

[0263] In some embodiments, “measurement instance sharing” is permitted within a sensing session. In some embodiments, “measurement instance sharing” allows instantaneous (individual) measurement instances to be associated with N sensing measurement setup IDs, and enables the associated sensing measurements to be performed using an instantaneous “common” setup that allows the sensing measurements to satisfy all requirements / specifications of the N sensing measurement setup IDs, where N is an integer greater than or equal to 1.

[0264] In some embodiments, "measurement instance sharing" is optional in a sensing session and allows instantaneous (individual) measurement instances to be associated with N sensing measurement setup IDs, enabling the associated sensing measurements to be performed using an instantaneous "common" setup that satisfies all requirements / specifications of the N sensing measurement setup IDs, where N is an integer greater than or equal to 1.

[0265] In some embodiments, access to the CSI is controlled for privacy protection. In WLAN sensing, there are different functional roles. A sensing initiator initiates a sensing procedure and has access to the CSI. A sensing responder participates in a session procedure and, if a receiver, has access to the CSI. A sensing transmitter sends a sensing PPDU. A sensing receiver performs sensing measurements, reports the sensing measurements, and has access to the CSI. There may also be an SBP request STA that requests an SBP procedure and has access to the CSI.

[0266] In some embodiments, the sensing system can maintain a classification of STAs to manage CSI access. In one example, the system may allow the most trusted class of STAs to perform all roles (with full access to the CSI), including 1, 2, 3, 4, and 5 (e.g., the user's IoT device from a trusted source). In another example, the system may allow a class of STAs to perform several roles (with limited access to the CSI), such as {1, 2, 3, 4}, {2, 3, 4}, or {4} (e.g., the user's IoT device from a less trusted source, neighboring device). In yet another example, the system may allow a class of STAs to perform several roles (without access to the CSI), such as a responder that is not a receiver, or {3} (e.g., an unknown device). In yet another example, the system may allow a class of STAs to perform no roles (e.g., adversarial device, compromised device) (without access to the CSI).

[0267] In some embodiments, an optional proxy sensing (SBP) procedure may be defined as follows: First, an “SBP request” involves a non-AP STA sending an SBP Request frame to an SBP-enabled AP STA. The STA that sends an SBP Request frame to initiate SBP (and consequently WLAN sensing) is referred to as the “SBP requesting STA.” The format and content of the SBP Request frame are determined. Second, an AP STA that receives an SBP Request may accept or reject the request by sending an SBP Response frame to the SBP requesting STA. The format and content of the SBP Response frame are determined. Next, an AP STA that accepts an SBP request may use operational parameters derived from the operational parameters indicated in the SBP Request to initiate a WLAN sensing procedure with one or more non-AP STAs. The measurement results obtained in the WLAN sensing procedure resulting from the SBP request may be reported to the SBP requesting STA.

[0268] In some embodiments, the Proxy-Based Sensing with Local Reporting (SBP-LR) procedure may be defined as follows: An "SBP-LR request" involves a non-AP STA sending an SBP-LR Request frame to an AP STA capable of SBP-LR. The STA that sends an SBP-LR Request frame to initiate SBP-LR (and consequently WLAN sensing) is referred to as the "SBP-LR requesting STA". The format and content of the SBP Request frame are determined. The AP STA that receives the SBP-LR Request may accept or reject the request by sending an SBP-LR Response frame to the SBP-LR requesting STA. If accepted, the AP may promise to perform SBP-LR for a certain period. Once the period ends, SBP-LR may stop. However, the STA requesting SBP-LR (or another STA) may send another "continuation request" with an SBP-LR setup ID. The format and content of the SBP-LR Response frame are determined. An AP STA accepting an SBP-LR request can initiate a WLAN sensing procedure with one or more non-AP STAs using operational parameters derived from the operational parameters indicated in the SBP-LR Request frame. The measurement results may be reported locally only (i.e., locally to the sensing receiver), remotely only (to the AP STA forwarding to the SBP-LR / SBP request STA), or both locally and remotely.

[0269] In some embodiments, the SBP-LR setup ID may be associated with SBP-LR setup and / or operational parameters. Each measurement instance may be associated with one or more SBP-LR setup IDs. The AP STA may store / buffer / process / transfer / redirect / reroute / distribute / utilize / execute / deliver the sensing measurement results received from the sensing receiver.

[0270] In some embodiments, the AP STA may perform non-TB sensing measurements with the non-AP STA using NDPs transmitted to the non-AP STA so that sensing measurements are performed at each of the non-AP STAs and the measurement results are reported locally at the non-AP STA. The AP STA may perform non-TB sensing measurements with the non-AP STA using NDPs transmitted from the non-AP STA so that sensing measurements are performed at the AP STA and the measurement results are reported locally at the AP STA. The AP STA can perform non-TB sensing measurements with the non-AP STA by having some NDPs transmitted to the non-AP STA and some NDPs transmitted from the non-AP STA. In this way, the sensing measurement results may not be wirelessly transmitted from the sensing receiver to the AP STA. In some embodiments, the AP STA may perform TB sensing measurements with the non-AP STA so that sensing measurements are performed at the AP STA and the measurement results are reported locally at the AP STA.

[0271] In the local report of the sensing receiver, unclaimed / unconsumed measurement results may be retained until the timeout period. Beyond the timeout period, the measurement results may be persisted (e.g., persisted until overwritten, persisted until the timeout period), discarded, overwritten, or retained. One or more high-level application processes in a non-AP STA can use MLME primitives to request the non-AP STA to participate in SBP-LR at the corresponding sounding frequency. The sensing measurement results may be read out via MLME primitives within the timeout period. The AP STA may perform wireless (e.g., WLAN, 4G / 5G / 6G / 7G / 8G, Bluetooth, WiMax, Wi-Fi, etc.) sensing procedures for multiple SBP-LR requests / SBP requests and / or multiple SBP-LR request STAs / SBP request STAs. The AP STA can perform WLAN sensing procedures as a service for multiple SBP-LR request STAs / SBP request STAs. As long as there is one SBP-LR / SBP request, the AT STA may initiate the execution of the WLAN sensing procedure. If all SBP-LR / SBP requests are satisfied and completed and there are no further requests, the service may be suspended.

[0272] If there are two or more SBP-LR / SBP requests with different sensing parameters (e.g., one sounding at 20Hz and another at 10Hz, or one at 20Hz and another at 30Hz), the AP STA may execute a radio sensing procedure having a "supersset" of sensing parameters so that all SBP-LR / SBP requests are satisfied simultaneously. Alternatively, the AP STA may execute multiple radio sensing procedures so that each SBP-LR / SBP request is satisfied by multiple radio sensing procedures. For example, the AP STA may execute multiple radio sensing procedures, e.g., A, B, and C, each having its own sensing parameters. The first SBP-LR / SBP request may be satisfied by A (or part of A). The second request may be satisfied by a combination of A and B. The third request may be satisfied by A and C. The fourth request may be satisfied by A, B, and C, and so on.

[0273] In some embodiments, multiple SBP-LR / SBP request STAs may be selected or authorized STAs for sending SBP-LR / SBP requests. AP STAs may reject / reject SBP-PR / SBP requests from unselected / unauthorized STAs.

[0274] In some embodiments, the AP STA can provide privacy protection / access control for a wireless sensing system formed by the AP STA and non-AP STAs. The AP STA belongs to a user and may be installed at the user's home / office / facility. The user uses some authentication protocol, some pairing procedure, some identification procedure, some password, etc., to send SBP-LR / SBP requests to enable sensing as a "permitted" or "selected" STA, and can specify / designate / select some user devices (e.g., the user's device, some permitted users' devices, the user's trusted devices, the user's recognized devices, the user's permitted devices). The user can also specify / designate / select some WiFi devices "visible" from the AP STA (e.g., neighboring WiFi devices, public devices, commercial devices, unknown devices, devices not trusted by the user, or devices owned by unpermitted users such as tenants or residents or visitors of the user's home / office / facility) as "unpermitted" or "unselected" or "denied" devices.

[0275] In some embodiments, in a mesh network where multiple AP STAs cooperate to form a mesh network, some or all of the AP STAs may perform SBP or SBP-LR. Each AP STA may perform radio sensing independently of its respective set of “client” STAs. A client STA may perform radio sensing with one or more AP STAs. Alternatively, some or all of the AP STAs may perform radio sensing jointly or in coordination. The sounding of two AP STAs may be synchronous, nearly synchronous, simultaneous, or phase-shifted with a constant phase difference, or not phase-shifted. Suppose there are three APs: AP1, AP2, and AP3. Each of the three APs can perform one or more radio sensing procedures (e.g., AP1 has A, B, C, AP2 has D, E, F) with its respective sensing parameters. D and A may be related. D and A may have the same sensing parameters. Similarly, E and B may be related, similar, or identical. F and C may be related, similar, or identical. AP1 and AP2 may perform A and D simultaneously, simultaneously, synchronously (with or without phase delay), or asynchronously. AP1 and AP2 may perform A and B alternately (for example, A=D, B=E, so that AP1 performs A while AP2 performs B, and AP1 performs B while AP2 performs A) or incrementally (for example, AP1 performs A, AP2 performs D while AP1 performs B, AP2 performs E while AP1 performs C, and AP2 performs F while AP1 performs A, with AP2 being one step behind AP1). AP3 can perform wireless sensing in a manner relative to AP1 alone, AP2 alone, or both AP1 and AP2.

[0276] In some embodiments, a "recipe" for distributed computing is implemented. The AP can use non-TB sensing measurements by sending NDPA+NDP to the IoT device so that the measurement results are generated on the IoT device. Distributed computing on the IoT device only requires local reporting; measurement results are not transmitted wirelessly.

[0277] In some embodiments, sensing via a proxy (SBP-LR) procedure with local reporting for multiple SBP-LR requesting STAs (e.g., one request for 10Hz / 20MHz / 1 antenna, one request for 30Hz / 40MHz / 4 antennas, one request for 15Hz / 40MHz / 3 antennas) can be defined as follows: When multiple SBP-LR requesting STAs send SBP-LR requests to an SBP-LR-enabled AP STA, the AP can assign an SBP-LR setup ID to each set of requested parameters. Those with identical request parameters can have the same SBP-LR setup ID, which means sharing of the SBP-LR setup ID. When the AP establishes a session with a non-AP STA, the AP obtains the maximum set of parameters supported by the non-AP STA. Thus, the AP knows which non-AP STAs can participate in each SBP-LR setup. The AP performs a measurement setup with each non-AP STA using a measurement setup based on the SBP-LR setup. Some measurement setup IDs may be reserved for SBP-LR setups.

[0278] In some embodiments, selective SBP may be applied. A proxy initiator (e.g., an SBP initiator) may send a request to a radio access point (AP), which is a proxy responder (e.g., an SBP responder), so that non-selective radio sensing (e.g., SBP) is performed between the AP (acting as a sensing initiator on behalf of the proxy initiator) and any available sensing responders in the AP's radio network (e.g., a non-AP STA / device, another AP, a mesh AP). Each of the available sensing responders may be assigned / associated with an identity (ID, e.g., MAC address). The proxy initiator (e.g., an SBP initiator) may send another request to the AP to perform selective radio sensing (e.g., selective SBP) with a selected group of sensing responders in the AP's radio network. Each selected sensing responder may be identified by its respective ID. The same or different sensing configurations may be used for different sensing responders. The same or different sensing settings may be used for different target tasks (in the case of multiple target tasks) or different proxy initiators (in the case of multiple proxy initiators) for a sensing responder.

[0279] A proxy initiator can request the AP to provide a list of sensing-enabled devices in the AP's network that support / are capable of wireless sensing (e.g., 802.11bf compatible), along with relevant device information (e.g., device name, hostname, vendor class ID, device product name). Based on the list and the relevant device information, the proxy initiator can select the desired sensing responders.

[0280] A proxy initiator can use a two-stage approach to perform selective radio sensing for a target task. In Stage 1, the proxy initiator can request / perform / use non-selective radio sensing (i.e., sensing by all available sensing responders) to run trial / test / training tasks with all sensing responders and select sensing responders based on the sensing results and several criteria. The trial / test / training task may also be a motion detection task. In the trial / test / training task, the location (or mapping to a target physical device) of each sensing responder within the venue can be estimated, and selection can be made based on the estimated location (or mapping) of the sensing responders. The proxy selector can also select some devices from a list of sensingable devices that did not participate in Stage 1.

[0281] Next, in Stage 2, the proxy initiator can request / perform selective radio sensing for the target task using the selected sensing responders. The trial / test / training task may be related to the target task in some way. The trial / test / training task may have low sensing requirements so that all sensing-capable radio responders can meet the requirements and participate in non-selective radio sensing. The trial / test / training task may have sensing results that are useful for selecting the sensing responders.

[0282] A proxy initiator can use a two-stage approach to perform selective radio sensing for two target tasks. For each target task, a Stage 1 may be performed followed by a Stage 2. Alternatively, a common Stage 1 may be performed in which a first group of selected sensing responders is selected for the first target task and a second group is selected for the second target task. The first group may or may not overlap with the second group. Then, based on each group of selected sensing responders, separate Stage 2s may be performed for the two target tasks (e.g., sequentially, simultaneously, or concurrently). If the first and second groups overlap with at least one common sensing responder appearing in both groups, the sensing results associated with the common sensing responder may be shared by both target tasks.

[0283] Two different proxy initiators can use a two-stage approach to perform selective radio sensing for their respective target tasks. For each target task of each proxy initiator, their respective Stage 1 may be performed, followed by their respective Stage 2. Alternatively, for the first proxy initiator, a first common Stage 1 may be performed (to select a group of sensing responders selected for each of its target tasks), followed by a separate Stage 2 (to perform selective radio sensing for each of its target tasks). Similarly, a second common Stage 1 may be performed for the second proxy initiator, followed by a separate Stage 2 for each of its target tasks. Alternatively, a third common Stage 1 may be performed for both proxy initiators, followed by a separate Stage 2 for each target task. If a common sensing responder is selected for multiple target tasks, the sensing results associated with the common sensing responder may be shared by the multiple target tasks.

[0284] A proxy initiator may be an "authorized" or "trusted" device that allows / authorizes / authenticates an AP to initiate either a non-selective SBP or both. A first test / configuration / procedure may be performed for the SBP initiator to be authorized by the AP to initiate a non-selective SBP (first authorization). A second test / configuration / procedure may be performed for the SBP initiator to be authorized by the AP to initiate a selective SBP (second authorization). The SBP initiator may have either the first authorization or the second authorization, or both. One of the first or second authorizations may imply the other.

[0285] The proxy initiator may connect to the AP via a wireless connection (e.g., the AP's wireless network, WiFi, WiMAX, 4G / 5G / 6G / 7G / 8G, Bluetooth, UWB, mmWave, etc.) or via a wired connection (e.g., Ethernet, USB, fiber optic, etc.).

[0286] A sensing responder may or may not support non-selective proxy sensing (such as SBP), selective proxy sensing, or both. When sending sensing results to an AP for onward transmission to a proxy initiator, the sensing responder may encrypt / process the sensing results so that they may not be decrypted / interpreted / consumed / sensed by an AP (which does not have a decryption key), but may be decrypted / interpreted / consumed / sensed by a proxy initiator (which has a decryption key).

[0287] In some embodiments, an SBP initiator may request an SBP responder (e.g., during SBP setup, or in an SBP setup request frame, or during SBP setup protocol / exchange / signaling) to restrict the sensing procedure in SBP to a list of non-AP STAs selected as sensing responders, and an SBP responder may restrict the sensing procedure in SBP to a list of non-AP STAs selected as sensing responders. Each selected non-AP STA may be specified by its MAC address. If requested, an SBP responder may exclude non-AP STAs not selected as sensing responders from the sensing procedure in SBP. An SBP initiator may include itself as one of the sensing responders.

[0288] In some embodiments, a bit pattern within the SBP setup request frame may be used to indicate the presence or absence of such a request. If the bit pattern indicates the presence of a request, a field indicating the number / count / quantity of selected non-AP STAs may be present in the SBP setup request frame. The MAC addresses of the list of selected non-AP STAs may be sent in or after the SBP setup request frame.

[0289] In some embodiments, wireless sensing has different use cases. A first use case of wireless sensing is shown in Figure 4. In this case, the AP is both a sensing initiator and a sensing transmitter for wireless sensing. An 802.11bf-compatible STA may be a sensing responder and a sensing receiver for wireless sensing. In this case, the sensing measurement result (e.g., CSI) may be fed back to the sensing initiator. Several sensing-based results (e.g., tasks and applications such as respiration detection and fall detection) may be calculated by the sensing initiator based on the sensing measurement result.

[0290] A second use case for wireless sensing is shown in Figure 5. In this case, the AP is both a sensing initiator and a sensing transmitter for wireless sensing. An 802.11bf-compatible STA may be both a sensing responder and a sensing receiver for wireless sensing. In this case, there is no feedback of sensing measurement results (e.g., CSI) to the sensing initiator. Some sensing-based results may be calculated by the sensing responder based on the sensing measurement results. The sensing-based results may be used by the sensing responder or transmitted elsewhere.

[0291] A third use case for wireless sensing is shown in Figure 6. In this case, the AP is both a sensing responder and a sensing receiver for wireless sensing. An 802.11bf-compatible STA may be a sensing initiator and a sensing transmitter for wireless sensing. In this case, the sensing measurement results (e.g., CSI) may be fed back to the sensing initiator. Several sensing-based results (e.g., tasks and applications such as respiration detection and fall detection) may be calculated by the sensing initiator based on the sensing measurement results.

[0292] A fourth use case for wireless sensing is shown in Figure 7. In this case, the AP is both a sensing responder and a sensing receiver for wireless sensing. An 802.11bf-compatible STA may be a sensing initiator and a sensing transmitter for wireless sensing. In this case, there is no feedback of sensing measurement results (e.g., CSI) to the sensing initiator. Some sensing-based results may be calculated by the sensing responder (AP) based on the sensing measurement results. Sensing-based results may be used by the sensing responder or transmitted elsewhere.

[0293] The fifth use case of wireless sensing is shown in FIG. 8. In this case, the AP is both a sensing initiator and a sensing receiver for wireless sensing. The 802.11bf compatible STA may be a sensing responder and a sensing transmitter for wireless sensing. In this case, the sensing measurement results (e.g., CSI) may be obtained by the sensing initiator. Some sensing-based results (e.g., tasks and applications such as respiration detection, fall detection) may be calculated by the sensing initiator based on the sensing measurement results.

[0294] The sixth use case of wireless sensing is shown in FIG. 9. In this case, the AP is both a sensing responder and a sensing transmitter for wireless sensing. The 802.11bf compatible STA may be a sensing initiator and a sensing receiver for wireless sensing. In this case, the sensing measurement results (e.g., CSI) may be obtained by the sensing initiator. Some sensing-based results (e.g., tasks and applications such as respiration detection, fall detection) may be calculated by the sensing initiator based on the sensing measurement results.

[0295] The seventh use case of wireless sensing is the case of proxy-based sensing (SBP) shown in FIG. 10. In this case, the AP is both a sensing initiator and a sensing transmitter for wireless sensing. The 802.11bf compatible STA may be a sensing responder and a sensing receiver for wireless sensing. In this case, the sensing measurement results (e.g., CSI) may be fed back to the SBP request STA. Some sensing-based results (e.g., tasks and applications such as respiration detection, fall detection, etc.) may be calculated by the SBP request STA based on the sensing measurement results.

[0296] An eighth use case for wireless sensing is the proxy-based sensing (SBP) case shown in Figure 11. In this case, the AP is both a sensing initiator and a sensing receiver for wireless sensing. An 802.11bf-compatible STA may be a sensing responder and a sensing transmitter for wireless sensing. In this case, the sensing measurement results (e.g., CSI) may be fed back to the SBP requesting STA. Based on the sensing measurement results, the SBP requesting STA may calculate the sensing-based results (e.g., tasks and applications such as breathing detection and fall detection).

[0297] The ninth use case for wireless sensing is the case of Sensing by Proxy (SBP), shown in Figure 12. In this case, the AP is both the sensing initiator and the sensing transmitter for wireless sensing. An 802.11bf-compatible STA may be both the sensing responder and the sensing receiver for wireless sensing. In this case, there is no feedback of sensing measurement results (e.g., CSI) to the SBP requesting STA. Some sensing-based results (e.g., tasks and applications such as breathing detection and fall detection) may be calculated by the sensing responder based on the sensing measurement results.

[0298] The tenth use case for wireless sensing is the proxy-based sensing (SBP) case shown in Figure 13. In this case, the AP is both the sensing initiator and sensing transmitter for wireless sensing. An 802.11bf-compatible STA may be both the sensing responder and sensing receiver for wireless sensing. In this case, the PPDU is broadcast from the AP to one or more sensing responders with the same sensing measurement setup, and there is no feedback of sensing measurement results (e.g., CSI) to the SBP requesting STA. Some sensing-based results (e.g., tasks and applications such as respiration detection and fall detection) may be calculated by the sensing responders based on the sensing measurement results.

[0299] In some embodiments, this teaching discloses systems and methods for wireless sensing. In some embodiments, when sensing results (e.g., CSI) are reported locally at a sensing receiver, a timestamp may be included in the report. The timestamp includes the time when a wireless signal (i.e., a sensing physical layer protocol data unit (PPDU) transmitted from a type 1 device (which may be a sensing transmitter) was received by a type 2 device (sensing receiver). In some embodiments, the timestamp may be used for time base correction (e.g., in respiratory detection / monitoring).

[0300] In some embodiments, each measurement instance can be associated with multiple sessions, but only with one measurement setup.

[0301] However, associating a single measurement instance with multiple measurement setups is useful. This means reduced communication time / bandwidth / network resources for sensing, reduced buffering memory, lower power consumption, and extended battery life. In some embodiments, measurement instances are shared within a session. Figure 14 shows an example of associating a measurement instance with multiple measurement setup IDs.

[0302] In some embodiments, measurement instances can be shared between sessions. By default, one measurement instance can be associated with multiple sessions, so a measurement instance associated with multiple measurement setups can be associated with multiple sets of {measurement setup ID, session ID}. Figure 15 shows an example of a measurement instance with multiple {measurement setup ID, session ID}.

[0303] Thus, according to some embodiments, 802.11bf may or may not permit a measurement instance to be associated with one or more measurement setup IDs. According to some embodiments, 802.11bf may or may not permit a measurement instance to be associated with one or more {measurement setup ID, session ID}.

[0304] Figure 16 is an exemplary block diagram of a first wireless device, e.g., Bot 1600, of a system for wireless sensing according to some embodiments of the present disclosure. Bot 1600 is an example of a device that can be configured to implement various methods described herein. As shown in Figure 16, Bot 1600 includes a transceiver 1610 comprising a processor 1602, memory 1604, a transmitter 1612 and a receiver 1614, a synchronization controller 1606, a power module 1608, an optional carrier configurator 1620, and a housing 1640 including a wireless signal generator 1622.

[0305] In this embodiment, the processor 1602 controls the general operation of the bot 1600 and may include one or more processing circuits or modules such as a central processing unit (CPU) and / or a general-purpose microprocessor, microcontroller, digital signal processor (DSP), field-programmable gate array (FPGA), programmable logic device (PLD), controller, state machine, gate logic, discrete hardware component, dedicated hardware finite state machine, or any other suitable circuit, device, and / or structure capable of performing calculations or other operations on data.

[0306] Memory 1604 may include both read-only memory (ROM) and random access memory (RAM) and may provide instructions and data to processor 1602. Part of memory 1604 may also include non-volatile random access memory (NVRAM). Processor 1602 typically performs logical and arithmetic operations based on program instructions stored in memory 1604. Instructions stored in memory 1604 (also known as software) can be executed by processor 1602 and perform the methods described herein. Together, processor 1602 and memory 1604 form a processing system for storing and executing software. As used herein, “software” means any type of instruction, whether or not it is called software, firmware, middleware, microcode, etc., that can configure a machine or device to perform one or more desired functions or processes. Instructions may include code (e.g., source code, binary code, executable code, or any other suitable form of code). When executed by one or more processors, instructions cause the processing system to perform the various functions described herein.

[0307] A transceiver 1610, including a transmitter 1612 and a receiver 1614, enables the bot 1600 to send and receive data to and from a remote device (e.g., Origin or other bots). An antenna 1650 is typically mounted on a housing 1640 and electrically coupled to the transceiver 1610. In various embodiments, the bot 1600 includes multiple transmitters, multiple receivers, and multiple transceivers (not shown). In one embodiment, the antenna 1650 is replaced by a multi-antenna array 1650, which can form multiple beams, each pointing in a distinct direction. The transmitter 1612 can be configured to wirelessly transmit signals of different types or functions, such signals being generated by a processor 1602. Similarly, the receiver 1614 is configured to receive wireless signals of different types or functions, and the processor 1602 is configured to process multiple different types of signals.

[0308] In this example, bot 1600 can function as a bot or Type 1 device or sensing transmitter for wireless sensing. The synchronization controller 1606 in this example is configured to control the operation of bot 1600 to be synchronized or asynchronous with another device, such as Origin or another bot. In one embodiment, the synchronization controller 1606 can control bot 1600 to be synchronized with Origin, which receives the wireless signal transmitted by bot 1600. In another embodiment, the synchronization controller 1606 may control bot 1600 to transmit a wireless signal asynchronously with other bots. In another embodiment, each of bot 1600 and the other bots can transmit wireless signals individually and asynchronously.

[0309] The carrier configurator 1620 is an optional component of Bot 1600 for configuring transmission resources, such as time and carrier, for transmitting the radio signal generated by the radio signal generator 1622. In one embodiment, each CI in the time series has one or more components, each corresponding to a carrier or subcarrier of the radio signal transmission. The radio sounding sensing may be based on any one or any combination of these components.

[0310] The power module 1608 may include one or more power sources, such as batteries, and a power regulator to supply regulated power to each of the modules described above in Figure 16. In some embodiments, if the Bot 1600 is coupled to a dedicated external power source (e.g., a wall outlet), the power module 1608 may include a transformer and a power regulator.

[0311] The various modules described above are coupled together by the bus system 1630. In addition to the data bus, the bus system 1630 may include, for example, a power bus, a control signal bus, and / or a status signal bus. It is understood that the modules of Bot 1600 can be coupled together operably using any suitable technology and medium.

[0312] Figure 16 illustrates numerous separate modules or components, but those skilled in the art will understand that one or more of these modules can be combined or implemented in common. For example, the processor 1602 can implement not only the functionality described above for the processor 1602, but also the functionality described above for the radio signal generator 1622. Conversely, each of the modules illustrated in Figure 16 can be implemented using multiple separate components or elements.

[0313] Figure 17 is an exemplary block diagram of a second wireless device, e.g., Origin 1700, for a wireless sensing system according to one embodiment of this teaching. Origin 1700 is an example of a device that can be configured to implement various methods described herein. In this example, Origin 1700 can function as an Origin or Type 2 device or sensing receiver for wireless sensing. As shown in Figure 17, Origin 1700 includes a housing 1740 containing a processor 1702, memory 1704, a transceiver 1710 consisting of a transmitter 1712 and a receiver 1714, a power module 1708, a synchronization controller 1706, a channel information extractor 1720, and an optional motion detector 1722.

[0314] In this embodiment, the processor 1702, memory 1704, transceiver 1710, and power module 1708 operate similarly to the processor 1602, memory 1604, transceiver 1610, and power module 1608 of the Bot 1600. The antenna 1750 or multi-antenna array 1750 is typically mounted on the housing 1740 and electrically coupled to the transceiver 1710.

[0315] Origin1700 may be a second wireless device having a different type from the first wireless device (e.g., Bot1600). In particular, the channel information extractor 1720 within Origin1700 is configured to receive a wireless signal via a wireless channel and to obtain time-series channel information (CI) of the wireless channel based on the wireless signal. The channel information extractor 1720 can transmit the extracted CI to an optional motion detector 1722 or an external motion detector of Origin1700 to perform wireless sounding sensing within the venue.

[0316] The motion detector 1722 is an optional component of the Origin 1700. In one embodiment, it is located within the Origin 1700, as shown in Figure 17. In another embodiment, it is located outside the Origin 1700 and in another device such as a bot, another Origin, a cloud server, a fog server, a local server, or an edge server. The optional motion detector 1722 may be configured to detect sound information from vibrating objects or sources within the venue based on motion information. The motion information may be calculated based on a time series of CIs from the motion detector 1722 or another motion detector outside the Origin 1700.

[0317] In this example, the synchronization controller 1706 is configured to control the operation of Origin 1700 to be synchronized or asynchronous with other devices, such as a bot, another Origin, or an independent motion detector. In one embodiment, the synchronization controller 1706 controls Origin 1700 to be synchronized with a bot that transmits radio signals. In another embodiment, the synchronization controller 1706 controls Origin 1700 to receive radio signals asynchronously with another Origin. In yet another embodiment, Origin 1700 and each of the other Origins may receive radio signals individually and asynchronously. In one embodiment, an optional motion detector 1722 or an external motion detector of Origin 1700 is configured to asynchronously calculate its respective heterogeneous motion information based on the respective time series of CIs.

[0318] The various modules described above are coupled together by the bus system 1730. In addition to the data bus, the bus system 1730 may include, for example, a power bus, a control signal bus, and / or a status signal bus. It is understood that the modules of the Origin 1700 can be coupled together operably using any suitable technology and medium.

[0319] Figure 17 illustrates numerous separate modules or components, but those skilled in the art will understand that one or more modules can be combined or implemented in common. For example, the processor 1702 can implement not only the functionality described above for the processor 1702, but also the functionality described above for the channel information extractor 1720. Conversely, each module illustrated in Figure 17 can be implemented using multiple separate components or elements.

[0320] Figure 18 shows a flowchart of an exemplary method 1800 for wireless sensing according to several embodiments of the present disclosure. In various embodiments, method 1800 may be performed by the system disclosed above. In operation 1802, a time series of wireless sounding signals (WSS) is transmitted by a transmitter in a wireless data communication network based on a wireless protocol associated with the communication network. The wireless data communication network may consist of multiple layers, including a physical (PHY) layer, a medium access control (MAC) layer, and at least one higher layer. In operation 1804, the time series of WSS (TSWSS) is received by a receiver in the wireless data communication network via a wireless channel of the venue based on a wireless protocol. In operation 1806, a number of wireless sensing measurements are performed by the receiver based on the received TSWSS to obtain sensing measurement results. In operation 1808, the sensing measurement results are reported by the receiver's PHY layer (or MAC layer) to at least one higher layer of the receiver. In operation 1810, a sensing-based task is performed by at least one upper layer of the receiver based on the sensing measurement results. The sequence of operations in Figure 18 can be modified according to various embodiments of this teaching.

[0321] In some embodiments, this teaching discloses methods for bidirectional P2P sensing. In one example of bidirectional P2P sensing, the AP may be the sensing initiator, and both the first and second non-AP STAs may be sensing responders. In another example, the non-AP STA may be a proxy sensing (SBP) initiator that requests the AP (SBP responder) to perform bidirectional P2P sensing, and in the bidirectional P2P sensing, the AP may be the sensing initiator, and both the first and second non-AP STAs may be sensing responders.

[0322] In both embodiments, the AP can individually configure / negotiate / arrange with two non-AP STAs so that the two non-AP STAs can identify each other (each having at least one corresponding ID, e.g., identifiable network address, identifiable radio network address / ID, AP-assigned ID, initiator-assigned ID, user-defined ID, MAC address), and the two non-AP STAs transmit NDP to each other as sounding signals so that sensing measurement results are acquired / generated at both non-AP STAs. The AP can transmit a second P2P sensing trigger frame to the pair of non-AP STAs. The second P2P sensing trigger frame may be an NDPA frame, a trigger frame, a special NDPA-trigger frame (as described above), a first P2P sensing trigger frame, or any other frame. Separate first P2P sensing trigger frames may be transmitted to each pair of non-AP STAs, or a common / shared first P2P sensing trigger frame may be transmitted to multiple (e.g., some or all) available STAs. Next, the first non-AP STA sends an NDP to the second non-AP STA to generate sensing measurement results in the second non-AP STA, and the second non-AP STA sends an NDP to the first non-AP STA to generate sensing measurement results in the first non-AP STA. The sensing measurement results may be used / needed in the second non-AP STA, or the sensing results may optionally be sent from the second non-AP STA (sensing responder) to the AP (sensing initiator). In the SBP example, the AP (SBP responder) can further report the sensing results to the SBP initiator.

[0323] In another example, the first and second non-AP STAs can perform unidirectional P2P sensing or bidirectional P2P sensing without signaling from the AP. The two non-AP STAs can identify each other and configure / negotiate / coordinate with each other. In unidirectional P2P sensing, the NDP may be transmitted unidirectionally from the first non-AP STA to the second non-AP STA to generate sensing results in the second non-AP STA. The second non-AP STA may optionally transmit its sensing results to the first non-AP STA. In bidirectional P2P sensing, the NDP may be transmitted bidirectionally between the two non-AP STAs without signaling from the AP.

[0324] In AP-initiated sensing procedures, inter-responder sounding may be optionally permitted for bidirectional inter-responder sensing. For example, an AP-initiated sensing procedure may optionally permit bidirectional sounding between two responders, with an NDP from responder 1 (R1) to responder 2 (R2) and another NDP from responder 2 (R2) to responder 1 (R1). This is useful when there are N responders forming a daisy-chain, scan order, or configuration.

[0325] In the N=3 example, the three responders R1, R2, and R3 can form a closed daisy-chain or closed loop. R2 can acquire CSIs between R1 and R2, and between R2 and R3. R2 can then perform useful WLAN sensing calculations based on the two CSIs. Optionally, the CSIs are reported to the AP.

[0326] Figure 19 shows an example of bidirectional responder sensing. In this example, four sensing responders R1, R2, R3, and R4 are configured to form a network (e.g., daisy-chain). Some "links" can perform bidirectional sensing (e.g., R1-R2, or R2-R3) such that each linked pair sends NDPs to each other in tandem (e.g., NDP from R1 to R2 and NDP from R2 to R1).

[0327] Some links perform unidirectional sensing (e.g., R3-R4, R4-R1) where NDP is transmitted only in one direction. R2 has two CSIs. R2 has two CSIs: one between R1 and R2, and one between R2 and R3 (it may have more CSIs if R2 is further linked to additional responders such as R4). In some embodiments, reporting of sensing measurements is optional.

[0328] According to some embodiments, in optional inter-responder sensing, the first sensing responder should be permitted to perform unidirectional or bidirectional sensing with the second sensing responder. In unidirectional sensing, an NDP is transmitted from the first responder to the second responder. In bidirectional sensing, an NDP is transmitted from the first responder to the second responder, and then another NDP is transmitted from the second responder to the first responder.

[0329] In some embodiments, this teaching also discloses the termination or suspension of session setup / measurement setup related to a sensing responder. The AP may determine that the sensing measurement results related to a particular sensing responder (e.g., CSI, CIR, CFR, RSSI) are unusable, unhelpful, and / or most unhelpful for the task (e.g., too noisy, too unstable, too chaotic, too interfering, unreliable, defective, or the user “pauses” or “stops” the sensing related to a particular responder, or the user “pauses” or “stops” the sensing related to all sensing responders), and in the case of proxy sensing (SBP), may receive a determination from the SBP initiator. The determination may be based on (i) tests on the sensing measurement results (e.g., tests / measurements on noise, stability, variability, randomness / chaos, interference, reliability, failure, error and / or miss), and / or (ii) the state / conditions / tests of the system (e.g., whether the transmission / storage / related processing / sensing calculations of the sensing measurement results consume too much bandwidth / memory / processing power / time, or generate too much power, or whether another task with a higher priority may require the resources currently allocated to the sensing measurement results). It may be determined that sensing measurement results associated with another sensing responder are useful, not wasted, and / or potentially more useful for the task.

[0330] As a result, the AP may terminate the setup of a sensing session associated with a particular sensing responder, or, in the case of an SBP, may receive such a request from an SBP initiator. The AP may wait for a certain period of time (for example, until any interference / noise / instability / unreliability / adverse conditions have ended, or until the user "unpauses" or "unstops" sensing), and then start another sensing session (by performing a sensing session setup) with a particular sensing responder and the same or similar, or modified, sensing session setup settings as the terminated sensing session setup, or, in the case of an SBP, may receive such a request from an SBP initiator. The determination of the time period may be based on several criteria.

[0331] Alternatively, instead of ending a sensing session setup, the AP may end a specific sensing measurement setup associated with a particular sensing responder, or, in the case of an SBP, may receive a request from an SBP initiator. The AP may wait for a certain period of time with the same or similar configuration as the terminated specific sensing measurement setup, and then start another sensing measurement setup with a particular sensing responder, or, in the case of an SBP, may receive a request from an SBP initiator.

[0332] Alternatively, the AP may be requested to temporarily suspend a sensing session with a particular sensing responder (i.e., setting up the sensing session) for a certain period and then resume the sensing session after that period, or, in the case of an SBP, to receive a request from the SBP initiator.

[0333] Alternatively, the AP may temporarily suspend a specific sensing measurement session with a specific sensing responder for a certain period of time and then resume the session after that period, or, in the case of an SBP, it may receive a request from the SBP initiator.

[0334] In some embodiments, the sounding signal may be multicast or broadcast from the AP to multiple sensing responders within the SBP. The AP may be both a sensing initiator and a sensing transmitter (e.g., in a sensing session or in an SBP). The AP may individually transmit a sounding signal (e.g., an NDP) to each of the numerous sensing responders (i.e., point-to-point sounding). Alternatively, the sounding signal (e.g., an NDP) may be transmitted to multiple sensing responders using multicast or broadcast, generating sensing measurement results simultaneously or concurrently at the multiple sensing responders. The sensing measurement results may be optionally reported to the AP or not. In the case of an SBP, the AP may optionally report the sensing measurement results to the SBP initiator.

[0335] In some embodiments, this teaching discloses systems and methods for ad hoc network sensing, peer-to-peer mode sensing, and non-infrastructure mode (NIM) sensing, such as wireless sensing in wireless ad hoc networks or distributed wireless networks (e.g., WiFi, Bluetooth, or other devices in non-infrastructure mode or peer-to-peer mode, WiFi Direct, Mobile Ad Hoc Network (MANET), Vehicle Ad Hoc Network (VANET), SPAN, wireless mesh network). In some embodiments, ad hoc networks are suitable for applications that cannot rely on a centralized node because they do not depend on existing infrastructure (e.g., ad hoc networks do not have access points / APs). Ad hoc networks tend to require minimal configuration and can be deployed quickly, making them suitable for emergencies, natural disasters, temporary / special events, or robotics.

[0336] Wireless sensing procedures can be used in infrastructure mode (non-ad-hoc networks) where an access point (AP) establishes / organizes / manages a wireless network. An STA (AP STA or non-AP STA) can act as a sensing initiator, starting a sensing procedure (in infrastructure mode). One or more other STAs can act as sensing responders by joining the sensing procedure. An STA can start multiple wireless sensing procedures, each with its own set of sensing responders and each with its own set of sensing parameters / settings. There may be multiple STAs, each starting its own sensing procedure.

[0337] An AP is either a sensing initiator or a sensing responder. Sounding signals may be transmitted from one STA (sensing transmitter) to another STA (sensing responder). These signals may be transmitted from the AP to a non-AP STA, from a non-AP STA to the AP, both, or not at all. When the AP is the sensing initiator, trigger-based (TB) sensing may be used. The trigger for transmitting a sounding signal (e.g., a non-data packet (NDP), an NDP variant) may be achieved by the AP transmitting an NDP announcement frame (NDPA) or a trigger frame variant (TF). During a short period after triggering (e.g., interframe space (IFS), short IFS (SIFS), reduced IFS (RIFS), PCF IFS (PIFS), DCF IFS (DIFS), arbitrarary IFS (AIFS), extended IFS (EIFS), etc.), NDPs can be transmitted from an AP (sensing initiator) to a non-AP STA (sensing responder), or from a non-AP STA to an AP, or both, or from one non-AP STA to another non-AP STA to generate sensing results. If the non-AP STA is the sensing initiator, non-TB sensing may be used, where the non-AP STA sends an NDPA to the AP, followed by pairs of initiator-to-responder (I2R) NDPs and responder-to-initiator (R2I) NDPs. Any NDP can be used to generate sensing results at a sensing receiver. Reporting sensing results to the sensing initiator is optional.

[0338] In some embodiments, this teaching discloses non-infrastructure mode (NIM) radio sensing based on protocols (e.g., 802.11, 802.11bf) for a radio ad-hoc network consisting of multiple peer-to-peer radio devices or radio stations (STAs) in non-infrastructure mode. Non-infrastructure mode radio sensing (including frames, exchanges, timing, specifications, etc.) may follow / be based on protocols or standards (e.g., 802.11, 802.11bf). Similar to infrastructure mode, a non-infrastructure mode STA may function as a sensing initiator by initiating a non-infrastructure mode sensing procedure or sensing session (e.g., by sending a request to a neighboring station in the ad-hoc network). A non-infrastructure mode sensing procedure may be similar to an infrastructure mode sensing procedure, except that an AP is replaced by a non-infrastructure mode STA. Similar to infrastructure mode, other STAs in non-infrastructure mode can function as sensing responders by participating in non-infrastructure mode sensing procedures (for example, by responding to a request from a sensing initiator with an indication of their willingness to participate). A non-infrastructure mode STA can initiate multiple non-infrastructure mode sensing procedures or sensing sessions, each with a set of sensing responders (who are also non-infrastructure mode STAs). Multiple STAs in non-infrastructure mode of an ad-hoc network may function as sensing initiators, each having its own non-infrastructure mode sensing procedure or sensing session.

[0339] In non-infrastructure mode sensing procedures, the sensing initiator may be a sensing transmitter, a sensing receiver, both, or none at all. The sensing responder may be a transmitter, a receiver, or both. The sounding signal may be transmitted from the sensing transmitter to the sensing receiver. If bidirectional sensing is supported, the sounding signal is also transmitted in the reverse direction. The sounding signal is transmitted from the sensing initiator to the sensing responder, or from the sensing responder to the sensing initiator, or both, or from the first sensing responder to the second responder.

[0340] Non-infrastructure mode trigger-based (TB) sensing may be performed similarly to infrastructure mode TB sensing, except that the infrastructure mode AP is replaced by a non-infrastructure mode sensing initiator. Triggering the transmission of a sounding signal (e.g., NDP, NDP variant) may be achieved by the sensing initiator transmitting an NDPA, or an NDPA-like frame, or a TF, or a TF-like frame. Within a short time after the trigger (e.g., IFS, SIFS, RIFS, PIFS, DIFS, AIFS, EIFS), an NDP may be transmitted from the sensing initiator to the sensing responder, or from the sensing responder to the sensing initiator, or both, or from a first sensing responder to a second sensing responder, generating a sensing result (at the sensing receiver).

[0341] Non-TB sensing in non-infrastructure mode may be performed in the same way as non-TB sensing in infrastructure mode, in which case the sensing initiator may send an NDPA to the sensing responder, followed by a pair of initiator-to-responder (I2R) NDPs and responder-to-initiator (R2I) NDPs. In non-TB sensing in non-infrastructure mode, the sensing responder may send an NDPA to the sensing initiator, followed by a pair of initiator-to-responder (I2R) NDPs and responder-to-initiator (R2I) NDPs.

[0342] Any NDP may be used to generate sensing results in the sensing receiver. Reporting sensing results to the sensing initiator is optional.

[0343] In some embodiments, for non-infrastructure modes (e.g., the polling phase in TB sensing), a frame (public or protected) similar to a trigger frame variant (TF) may be defined in a protocol or standard (e.g., 802.11, 802.11bf) that allows an STA in non-infrastructure mode (e.g., a sensing initiator) to request an NDP transmission from another STA in non-infrastructure mode to obtain sensing measurements. If an STA in non-infrastructure mode is available, the STA can respond with a CTS-to-self.

[0344] In some embodiments, the protocol or standard TF may be defined / improved / modified / changed to allow an STA in non-infrastructure mode to request an NDP transmission from another STA in non-infrastructure mode to obtain sensing measurements. When the TF is transmitted by an AP (infrastructure mode), the AP can request an NDP transmission from an STA to obtain sensing measurements. When the TF is transmitted by an STA in non-infrastructure mode, it allows an STA to request an NDP transmission from another STA in non-infrastructure mode to obtain sensing measurements.

[0345] The non-infrastructure mode NDPA sounding phase may consist of the transmission of an NDPA or NDPA-like frame by a non-infrastructure mode STA (e.g., sensing initiator or sensing responder) and the transmission of an NDP by a non-infrastructure mode STA (e.g., sensing initiator or sensing responder or sensing transmitter) a short time after the NDPA transmission (e.g., IFS, SIFS, RIFS, PIFS, DIFS, AIFS). For example, NDPA sounding may be used by 802.11 high efficiency (HE), ultra-high throughput (EHT), or pre-HE STAs.

[0346] The TF sounding phase in non-infrastructure mode may consist of a non-infrastructure mode STA (e.g., a sensing initiator or sensing responder) sending a TF or TF-like frame to request NDP transmissions from other STAs, and a short time after the reception of the TF or TF-like frame (e.g., IFS, SIFS, RIFS, PIFS, DIFS, AIFS, EIFS, etc.) the transmission of NDPs by other STAs (e.g., from one STA to that STA, from that STA to another STA, or from yet another STA to yet another STA).

[0347] Non-infrastructure mode non-TB sensing measurement instances can be performed as follows: When a non-infrastructure mode STA (e.g., sensing transmitter, sensing initiator, sensing responder) acquires a transmit opportunity (TXOP), it initiates a non-infrastructure mode non-TB sensing measurement instance by sending an NDPA to the sensing receiver, followed by a pair of I2R NDPs (sensing initiator to sensing responder) and R2I NDPs (sensing responder to sensing initiator). If the sensing initiator is only a sensing transmitter, the NDPA frame is configured to send the R2I NDP with a minimum length of 1LTF symbol. If the sensing responder is only a sensing transmitter, the NDPA frame is configured to send the I2R NDP with a minimum length of 1LTF symbol.

[0348] Proxy-based sensing (SBP) procedures are primarily used in infrastructure mode. In infrastructure mode SBP, a non-AP STA (acting as the SBP initiator) sends an SBP request (using an SBP Request frame or "infrastructure mode SBP Request frame") to an AP (acting as the SBP responder), and the AP accepts the SBP request by sending an SBP response (using an SBP response frame). The AP then performs sensing procedures with a number of sensing responders (where the SP acts as the sensing initiator) and optionally reports sensing measurement results (such as CSI).

[0349] In some embodiments, for infrastructure mode SBP, an infrastructure mode SBP responder (and sensing initiator as well) may be executing an existing sensing procedure before receiving an SBP request from an SBP initiator. This sensing procedure may or may not be associated with another SBP procedure initiated by another SBP initiator. The existing sensing procedure may consist of a number of existing sensing responders and may be associated with a set of sensing measurement settings / parameters.

[0350] In one case, the sensing measurement settings / parameters of an existing sensing procedure may be acceptable to the SBP initiator (for the requested SBP). The SBP responder can simply associate the existing sensing procedure with the requested SBP and send / share the sensing measurements from the existing sensing procedure to the SBP initiator.

[0351] In another case, the sensing measurement settings / parameters of an existing sensing procedure may not be acceptable to the SBP initiator (for the requested SBP). The SBP responder / sensing initiator can work with the sensing responder to adjust / modify one or more sensing measurement settings / parameters of the existing sensing procedure so that the adjusted / modified sensing procedure with the adjusted / modified sensing measurement settings / parameters is acceptable to the SBP initiator. The SBP responder can then associate the existing sensing procedure with the requested SBP and send / share the sensing measurements from the existing sensing procedure to the SBP initiator.

[0352] For example, you can adjust / change one of the following settings / parameters of an existing sensing procedure: sounding frequency / timing (e.g., 0.1 / 1 / 10 / 100 / 1000 / 10000Hz), carrier frequency (e.g., channel number, 2.4GHz, 5GHz, 6GHz, etc.), bandwidth (e.g., 20 / 40 / 80 / 160 / 320 / 640MHz, or part / shared bandwidth), unicast / multicast / broadcast, sensing transmitter / receiver settings, trigger (TB sensing, using NDPA / TF, non-TB sensing, etc.), antenna amount, optional reporting of sensing measurements, type of sensing measurements to be reported, and set of sensing responders.

[0353] In another case, the sensing measurement settings / parameters of an existing sensing procedure may be partially acceptable to an SBP initiator (for the requested SBP). This may become acceptable if they are augmented by another sensing procedure. For example, an SBP initiator may request a sounding frequency of 100 Hz, but the existing sensing procedure has a sounding frequency of 50 Hz. The SBP responder can initiate a complementary / auxiliary sensing procedure with a sounding frequency of 50 Hz and synchronize the timing of the sounding of the complementary / auxiliary sensing procedure so that the two sensing procedures combined can produce the 100 Hz sounding frequency requested by the SBP initiator.

[0354] In another example, an SBP initiator might request a sounding frequency of 100 Hz, but the existing sensing procedure has a sounding frequency of 25 Hz (uniform timing / sampling). The SBP responder can initiate a complementary / auxiliary sounding procedure with a sounding frequency of 75 Hz (non-uniform timing / sampling), and time the sounding of the complementary / auxiliary sounding procedure so that the two sounding procedures combined can produce the 100 Hz (uniform sounding / sampling) sounding frequency requested by the SBP initiator. For example, the SBP responder might take one sensing measurement from the existing sensing procedure, then three sensing measurements from the auxiliary sensing procedure, then another sensing measurement from the existing sensing procedure, then three more sensing measurements from the auxiliary sensing procedure, and so on.

[0355] In another example, an SBP initiator might request an 80 MHz bandwidth, but the existing sensing procedure has a 40 MHz bandwidth. The SBP responder can initiate a complementary / auxiliary sensing procedure with a 40 MHz bandwidth, and combine the two sensing procedures to generate the 80 MHz bandwidth requested by the SBP initiator.

[0356] The SBP request frame may contain a field (e.g., a bit or bit pattern) indicating whether multiple sensing procedures may be used in the SBP procedure (for one / several / any / all sensing responders). Alternatively, it may contain a field (e.g., a bit) indicating whether "mix-and-match" of sensing procedures is permitted in the SBP procedure.

[0357] In some embodiments, the SBP request frame or associated frame may include a specification / description / list of permitted sensing responders (or permitted / preferred sensing responders). The SBP initiator may restrict the SBP procedures performed / initiated by the SBP responder / sensing initiator to allow only permitted sensing responders to participate in the sensing procedure (or the SBP initiator may want / prefer to receive sensing measurement results only from permitted sensing responders, and the SBP responder may only provide the SBP initiator with sensing measurement results from permitted sensing responders). Other sensing responders not on the list of permitted sensing responders may be “disallowed” and may not be permitted to participate in the sensing procedure. To identify permitted sensing responders, an SBP initiator may provide a unique identifier (ID) for each permitted sensing responder in the SBP request frame or associated frame (e.g., a user ID (UID), association (AID), universally unique ID (UUID), globally unique ID (GUID), MAC address, or Internet Protocol (IP) address, or an internal ID within the system).

[0358] In some embodiments, an SBP initiator may send an SBP update frame to an SBP responder at any time during the sensing procedure of an SBP procedure to update / change / modify at least one setting / parameter of the SBP procedure. For example, an SBP initiator may request an SBP responder / sensing initiator to terminate / stop a particular sensing responder (e.g., using an SBP update frame) (for example, because the sensing measurements / TSCI associated with a particular sensing responder may be noisy, problematic, unstable, defective, unreliable, etc., and may be wasting valuable network resources such as TXOP, data bandwidth, memory, computing power, and / or energy to process / transmit), or at least request to stop transmitting sensing measurements / TSCI associated with a particular sensing responder. An SBP initiator may request an SBP responder / sensing initiator to pause, resume, or add a particular sensing responder. The SBP may provide a unique identifier for a particular sensing responder.

[0359] In some embodiments, a non-infrastructure mode (NIM) SBP procedure is defined that is similar to infrastructure mode SBP, except that the AP in infrastructure mode SBP is replaced by a non-infrastructure mode STA. A frame similar to the SBP request frame (public or protected) is defined in the protocol or standard (802.11, 802.11bf), and this frame can be used by a non-infrastructure mode STA (acting as an SBP responder or NIM SBP responder, similar to the AP in infrastructure mode SBP) to send a NIM SBP request to another non-infrastructure mode STA (acting as an SBP responder or NIM SBP responder, similar to the AP in infrastructure mode SBP) which will send a NIM SBP response (e.g., an SBP response frame or similar frame) to accept the SBP request. The SBP Request frame may have bits / fields that indicate / specify whether the SBP responder is a sensing transmitter, a sensing receiver, or both, or neither, in a NIM sensing procedure initiated by the SBP responder. Next, another STA (which is both an SBP responder and a sensing initiator) can execute / start a NIM sensing procedure with multiple sensing responders (STAs in non-infrastructure mode). Measurement results obtained in the non-infrastructure mode sensing procedure may optionally be reported from sensing responders to sensing initiators and from SBP responders (sensing initiators) to SBP initiators. Another STA (i.e., an SBP responder and a sensing initiator) may assign a Measurement Setup ID in its SBP response.

[0360] In some embodiments, the infrastructure mode (IM) SBP Request frame may be defined / improved / modified / changed to allow a non-infrastructure mode STA (SBP initiator) to send a non-infrastructure mode SBP request to another non-infrastructure mode STA.

[0361] A NIM SBP response sent by another STA in non-infrastructure mode may be a frame similar to an IM SBP Response frame, or it may be an IM SBP Response frame itself, as defined / modified by the protocol or standard to allow another STA in non-infrastructure mode to accept or reject a NIM SBP request.

[0362] In some embodiments, an SBP initiator may specify / describe / list / provide a number of allowed sensing responders. The SBP initiator may restrict the NIM SBP procedure or NIM sensing procedure performed / initiated by the SBP responder / sensing initiator so that the SBP responder / sensing initiator allows only a number of allowed sensing responders to participate in the NIM sensing procedure (or the SBP initiator may prefer to receive / receive only sensing measurement results from allowed sensing responders, and the SBP responder may provide the SBP initiator only with sensing measurement results from allowed sensing responders). Other sensing responders not on the list of allowed sensing responders are "not allowed" and may not be permitted to participate in the NIM sensing procedure. To specify the number of allowed sensing responders, the SBP initiator may provide a unique identifier (ID) for each allowed sensing responder (e.g., a User ID (UID), Association ID (AID), Universal Unique ID (UUID), Global Unique ID (GUID), MAC address, or Internet Protocol (IP) address, System Internal ID, etc.).

[0363] In some embodiments, an SBP initiator may send a NIM SBP update frame to an SBP responder at any time during a NIM sensing procedure of a NIM SBP procedure to update / change / modify at least one setting / parameter of the NIM SBP procedure. For example, an SBP initiator may request an SBP responder / sensing initiator to terminate / stop a particular sensing responder (e.g., using a NIM SBP update frame) (for example, perhaps because the sensing measurements / TSCI associated with a particular sensing responder are noisy, problematic, unstable, defective, unreliable, etc., and may be wasting valuable network resources such as TXOP, data bandwidth, memory, computing power, and / or energy to process / transmit), or at least request to stop transmitting the sensing measurements / TSCI associated with a particular sensing responder. An SBP initiator may request an SBP responder / sensing initiator to pause, resume, or add a particular sensing responder. SBP can provide a unique identifier for a specific sensing responder.

[0364] In infrastructure mode, the sensing procedure initiated by AP STA is optionally extended to allow NDP measurements between sensing responders.

[0365] In some embodiments, a non-infrastructure mode sensing procedure initiated by a non-infrastructure mode STA may be optionally extended to enable NDP measurements from sensing responder to sensing responder. A first sensing responder and a second sensing responder, both of which are non-infrastructure mode STAs, may be configured to transmit NDP from the first responder to the second responder, or from the second responder to the first responder, or both.

[0366] In some embodiments, a non-infrastructure mode STA in an ad-hoc network acts as a sensing initiator, initiating a non-infrastructure mode sensing procedure based on a protocol (e.g., 802.11, 802.11bf). At least one other STA in the non-infrastructure mode ad-hoc network participates in the sensing procedure as a sensing responder, based on the protocol. The sensing initiator and sensing responder negotiate to set up the sensing procedure / session and associated sensing measurement parameters.

[0367] In some embodiments, one device is set up as a sensing transmitter (Type 1 device). The other device is set up as a sensing receiver (Type 2 device). A radio sounding signal (e.g., NDP, time series of NDP) is transmitted from the sensing transmitter to the sensing receiver, where it generates a sensing measurement (e.g., TSCI). The NDP can be I2R or R2I depending on which device is the sensing transmitter.

[0368] The sensing measurements may be made available locally to applications (e.g., software, firmware) in the sensing receiver. Optionally, the sensing measurements may be wirelessly transmitted from the sensing responder to the sensing initiator or from the sensing receiver to the sensing transmitter (e.g., using a protocol-based sensing measurement report frame) for use by applications (e.g., software, firmware) in the sensing initiator.

[0369] In some embodiments, a mesh network may have multiple wireless mesh routers, e.g., R1, R2, R3, ..., R_k, for some k (e.g., k may be 3, 4, 6, or 10, or 100). Some mesh routers may be dual-band, tri-band, or quad-band devices. Some back channels may allow mesh routers to transmit / receive / forward / channel / exchange digital data to and from Internet / broadband services (e.g., via some broadband router / service provider). Mesh routers may be interconnected via non-infrastructure mode. Individual wireless client devices (e.g., IoT devices) may be connected to any one of the multiple mesh routers (e.g., in infrastructure mode or non-infrastructure mode). Wireless client devices and / or mesh routers may form a wireless sensing network, some of which are wireless transmitters (e.g., sensing transmitters) and some are wireless receivers (e.g., sensing receivers). Some (e.g., sensing initiators) may initiate wireless sensing procedures / sessions. Some may respond to join a wireless sensing procedure / session.

[0370] In some embodiments, each mesh router is available for client devices to connect to, but if possible, it may be better to "encourage," "move," "reconnect," "reconnect," or "centralize" most or all client devices to one (or a very small number) mesh routers. By having all client devices connect to the same mesh router, the sensing network can better cover the venue and / or the sensing network's functions / logic / algorithms can function better. For example, the system could centralize all client devices to one or more specific mesh routers (e.g., R1). However, in large areas, multiple mesh routers may be needed to cover the whole. In that case, two or more mesh routers can be identified as a specific mesh router.

[0371] In some embodiments, several signaling (protocol, control data / frame exchange) may be performed to suggest / instruct / request / instruct a client device to disconnect from its current mesh router and connect to a specific mesh router (or one of several specific mesh routers). Each specific mesh router may be identifiable (SSID, name, MAC address, etc.).

[0372] Figure 20 shows numerous STAs in non-infrastructure mode forming an ad-hoc network. This ad-hoc network does not contain any access points (APs). Figures 21–25 illustrate various use cases for non-infrastructure mode sensing.

[0373] Figure 21 shows Use Case 1, where the sensing initiator is a sensing transmitter. Initially, an STA (as a sensing initiator) starts a sensing session. Some STAs join the sensing session (as sensing responders). Some STAs do not join the sensing session. The sensing initiator is a sensing transmitter (Tx). In some embodiments, TB-like sensing may be performed using NDPA(I2R) and NDP(I2R). In some embodiments, non-TB-like sensing may be performed using NDPA(I2R), NDP(I2R), and NDP(R2I). Bidirectional sensing may be supported (i.e., both Tx and Rx), and sensing measurement reporting may be optional.

[0374] Figure 22 shows Use Case 2, where the sensing initiator is the sensing receiver. Initially, an STA (as the sensing initiator) starts a sensing session. Some STAs join the sensing session (as sensing responders). Some STAs do not join the sensing session. The sensing initiator is the sensing receiver (Rx). In some embodiments, TB-like sensing may be performed using TF(I2R) and NDP(R2I). In some embodiments, non-TB-like sensing may be performed using: NDPA(I2R) and NDP(I2R) and NDP(R2I). Bidirectional sensing may be supported (i.e., both Tx and Rx), and sensing measurement reporting may be optional.

[0375] Figure 23 shows Use Case 3 in which responder-to-responder (R2R) sensing is performed. First, an STA (Sensing Initiator) starts a sensing session. Some STAs join the sensing session (as sensing responders). Some STAs do not join the sensing session. The first responder (Tx) may be configured to send a sounding signal to the second responder (Rx). Bidirectional sensing may be supported (i.e., both Tx and Rx), and sensing measurement reporting may be optional.

[0376] Figure 24 shows Use Case 4, where proxy sensing (SBP) is performed and the sensing initiator is a sensing transmitter. First, an STA (SBP initiator) requests another STA (SBP responder, sensing initiator) to start a sensing session. The sensing initiator is a sensing transmitter (Tx). In some embodiments, TB-like sensing may be performed using NDPA(I2R) and NDP(I2R). In some embodiments, non-TB-like sensing may be performed using: NDPA(I2R) and NDP(I2R) and NDP(R2I). Bidirectional sensing may be supported (i.e., both Tx and Rx), and sensing measurement reporting may be optional.

[0377] Figure 25 illustrates Use Case 5, where proxy sensing (SBP) is performed and the sensing initiator is the sensing receiver. First, one STA (SBP initiator) requests another STA (SBP responder, sensing initiator) to start a sensing session. The sensing initiator is the sensing receiver (Rx). In some embodiments, TB-like sensing may be performed using TF (I2R) and NDP (R2I). In some embodiments, non-TB-like sensing may be performed using NDPA (I2R) and NDP (I2R) and NDP (R2I). Bidirectional sensing may be supported (i.e., both Tx and Rx), and sensing measurement reporting may be optional.

[0378] According to some embodiments, WLAN sensing is supported in non-infrastructure mode. In the case of a WLAN sensing procedure in non-infrastructure mode, the WLAN sensing step may be equivalent to at least one of the following sensing steps: trigger-based (TB) sensing, non-TB sensing, or other WLAN sensing steps.

[0379] In some embodiments, a first mode, flag, setting, bit combination and / or field may exist in the sensing session / procedure setup frame and / or SBP setup frame to indicate that both the minimum bandwidth (BW) requirement and the minimum spatial stream count (SS) requirement apply (e.g., from a sensing initiator / SBP responder to a sensing responder). A second mode, flag, setting, bit combination and / or field may exist to indicate that the minimum BW and minimum SS do not apply. Instead, the minimum "effective bandwidth" or the minimum "product of BW and SS" applies (e.g., from sensing initiator / SBP responder to sensing responder). For example, suppose {BW = 40 MHz, spatial stream count = 4}. In the first mode / flag / setting / bit combination / field, BW must be at least 40 MHz and SS must be at least 4. In the second mode / flag / setting / bit combination / field, the system may allow {BW=80MHz, spatial streams=2} which would otherwise fail the SS requirement, or {BW=20MHz, spatial streams=8} or {BW=20MHz, spatial streams=9} which would otherwise fail the BW requirement. Alternatively, in the case of {BW=40MHz,SS=3}, the system may allow {BW=20MHz,SS=6} or {BW=80MHz,SS=2} which would otherwise fail the BW or SS requirement.

[0380] In some embodiments, there may be modes / flags / settings / bit combinations / fields in the sensing session / procedure setup frame and / or SBP setup frame to indicate that maintaining the sounding frequency takes precedence over the bandwidth (BW). For example, an STA can typically transmit a sounding signal at a specific bandwidth (e.g., 80 MHz) and a specific sounding frequency (e.g., 100 Hz). However, under certain circumstances (e.g., data traffic congestion or high interference), it may not be possible to maintain both the sounding frequency and the bandwidth simultaneously. In such cases, maintaining the sounding frequency takes precedence over the bandwidth. This priority may be applied by the sensing transmitter when transmitting the sounding signal to the sensing receiver. There may be other modes / flags / settings / bit combinations / fields in the sensing session / procedure setup frame and / or SBP setup frame to indicate that maintaining the bandwidth takes precedence over the sounding frequency. In general, there may be modes / flags / settings / bit combinations / fields to indicate that maintaining a particular parameter of the sensing procedure / session or SBP takes precedence over other parameters and / or other parameters. There may be modes / flags / settings / bit combinations / fields in a sensing procedure / session or SBP that indicate a first priority for maintaining a first parameter over other parameters and / or other parameters, and a second priority for maintaining a second parameter (i.e., multiple parameters with corresponding priorities).

[0381] In some embodiments, after the SBP procedure is established / configured, the SBP configuration may be updated once or multiple times during the SBP procedure using the configuration fields in the configuration frame. Possible updates may include adding / configuring / stopping / pausing / restarting new sensing responders, adding / configuring / pausing / restarting new sensing transmitters, adding / configuring / stopping / pausing / restarting new sensing receivers, stopping / pausing / restarting / terminating sensing responders, adjusting local / non-local reporting (or reporting settings such as CSI processing, accuracy, immediate / delayed CSI reporting), adjusting sounding frequencies, adjusting channel settings (bandwidth, carrier frequency), and adjusting threshold-based reporting. The SBP configuration may also be updated by an SBP initiator (a specific client device).

[0382] At some point in the SBP, the SBP initiator may discover that there are problems with the sensing measurements associated with a particular sensing responder (e.g., the CSI is noisy, unstable, unreliable, or defective). To avoid wasting resources (e.g., TXOP usage or data bandwidth for transmitting sensing results), the particular sensing responder should be shut down.

[0383] Problematic situations may be temporary. Instead of shutting down, the specific sensing responder may be temporarily suspended and restarted later. Newly introduced devices may also be added.

[0384] In some embodiments, an SBP initiator may request an SBP responder (AP) to do the following, and the AP may do the following: stop a sensing procedure with a particular sensing responder, pause a sensing procedure with a particular sensing responder, resume a sensing procedure with a paused sensing responder, or add a sensing procedure with a particular sensing responder.

[0385] Figure 26 illustrates a first use case for updating SBP setup and procedures, where the sensing initiator is a sensing transmitter and SBP responder. In this case, the non-AP STA is the sensing responder and receiver. In some embodiments, certain responders (e.g., noisy, unstable, unreliable, or faulty responders) are stopped / paused / restarted / added.

[0386] Figure 27 illustrates a second use case for updating SBP setup and procedures, where the sensing initiator is a sensing receiver and SBP responder. In this case, the non-AP STA is a sensing responder and transmitter. In some embodiments, certain responders (e.g., noisy, unstable, unreliable, or faulty responders) are stopped / paused / restarted / added.

[0387] In some embodiments, the SBP initiator may request that the sensing procedure with a particular sensing responder be stopped, and the SBP responder (AP) should be able to stop. This does not stop the SBP.

[0388] In some embodiments, an SBP initiator may request to pause a sensing procedure with a particular sensing responder and to resume the sensing procedure with the paused sensing responder, and an SBP responder (AP) should be able to pause a sensing procedure with a particular sensing responder and to resume the sensing procedure with the paused sensing responder.

[0389] In some embodiments, an SBP initiator may request the addition of a sensing procedure with a specific sensing responder, and the SBP responder (AP) should be able to do so.

[0390] In some embodiments, this teaching discloses a system for local reporting of CSIs in wireless sensing measurements based on the 802.11bf standard. This means “local CSI reporting” or “CSI not reported non-locally” and may affect many elements of 802.11bf, including configuration, setup, sensing measurements, and reporting.

[0391] Th...

Claims

1. A system in a wireless data communication network for wireless sensing, A transmitter configured to transmit a time-series radio sounding signal (WSS) based on a radio protocol associated with the radio data communication network, wherein the radio data communication network comprises the transmitter and a physical (PHY) layer, a media access control (MAC) layer, and at least one higher layer. It is a receiver, Based on the aforementioned wireless protocol, the time series (TSWSS) of the WSS is received via the venue's wireless channel. To obtain sensing measurement results, multiple wireless sensing measurements are performed based on the received TSWSS. The PHY layer or MAC layer of the receiver reports the sensing measurement results to the at least one higher layer of the receiver. The at least one upper layer of the receiver performs a sensing-based task based on the sensing measurement results. The receiver configured as follows includes System.

2. The wireless protocol is at least one of the following: wireless LAN (WLAN) protocol, mobile communication protocol, WLAN standard, Wi-Fi standard, IEEE 802 standard, IEEE 802.11 standard, or IEEE 802.11bf standard. The system according to claim 1.

3. The sensing measurement results include a time series of channel information (CI) of the wireless channel. Each CI (TSCI) in the aforementioned time series is acquired by the receiver based on its respective WSS. Each CI includes at least one of the following: channel state information (CSI), channel impulse response (CIR), or channel frequency response (CFR). The system according to claim 1.

4. The sensing measurement results are configured to be reported to at least one upper layer of the receiver based on the setting field of the setting frame. The configuration frame is acquired by the receiver during the setup procedure related to the wireless sensing measurement in accordance with the wireless protocol. The system according to claim 1.

5. The sensing measurement results are configured to be reported to at least one upper layer of the receiver, instead of to any device other than the receiver. The system according to claim 4.

6. The sensing measurement results are configured not to be reported to any device other than the receiver. The system according to claim 4.

7. The aforementioned configuration frame is communicated based on the wireless protocol during the setup procedure. The setup procedure described above is performed before any of the wireless sensing measurements. The system according to claim 4.

8. The sensing measurement results are configured to be reported locally by the receiver by configuring the receiver using the configuration field of the configuration frame communicated during the setup procedure based on the wireless protocol. The system according to claim 4.

9. The receiver is configured to, during the setup procedure, report the sensing measurement results locally within the receiver based on the wireless protocol, by the system's sensing initiator device. The system according to claim 8.

10. The configuration frame is communicated during the negotiation process between the sensing initiator device and the receiver based on the wireless protocol. The system according to claim 9.

11. Each of the transmitter and receiver is individually configured by the system's sensing initiator device to perform a plurality of the wireless sensing measurements in coordination. The transmitter is configured by the sensing initiator device to function as a sensing transmitter device for transmitting the TSWSS to the receiver. The TSWSS comprises at least one setting related to the sensing-based task, The receiver is configured by the sensing initiator device to function as a sensing receiver for receiving the TSWSS from the transmitter and acquiring the TSCI based on the received TSWSS. The receiver is configured to report the TSCI locally to at least one higher layer of the receiver, or to report it non-locally to another device in the system, based on the wireless protocol. At least one of the transmitter or the receiver functions as a sensing responder device associated with the sensing initiator device. The system according to claim 3.

12. The receiver, based on the wireless protocol, is controlled by the sensing initiator device. The TSCI is processed in a first manner, which includes a first precision reduction for reporting the TSCI locally. The system is configured to process the TSCI in a second way, which includes a second precision reduction different from the first precision reduction for reporting the TSCI non-locally. The system according to claim 11.

13. When reporting the aforementioned TSCI non-locally, The receiver wirelessly transmits each CI to the sensing initiator device based on the wireless protocol. The reported TSCI is available non-locally in the sensing initiator device for the sensing-based task. The system according to claim 12.

14. The non-locally reported TSCI can be used for centralized computing of the sensing-based tasks in the sensing initiator device. The system according to claim 13.

15. When reporting the aforementioned TSCI locally, The receiver makes each CI locally available at the at least one higher layer of the receiver for the sensing-based task. The system according to claim 12.

16. The locally reported TSCI can be used in the receiver for distributed computing of sensing-based tasks. The system according to claim 15.

17. The first CI of the TSCI is reported by the receiver both locally and non-locally. The system according to claim 11.

18. The second CI of the TSCI is not reported by the receiver, either locally or non-locally. The system according to claim 11.

19. The sensing initiator device is an access point (AP) device of the wireless data communication network, The transmitter is a client device of the wireless data communication network and functions as a sensing responder device. The receiving side is the AP, The receiver reports the TSCI locally. The locally reported TSCI is available to the AP for the sensing-based task. The system according to claim 11.

20. The wireless sensing measurement is trigger-based (TB), using null data packet (NDP) frames and trigger frames (TF) based on the wireless protocol. The system according to claim 19.

21. The venue further includes at least one additional transmitter, individually configured by the sensing initiator device based on the wireless protocol to function as a sensing transmitter device, Each of the additional transmitters is configured to transmit each TSWSS to the receiver based on the wireless protocol, The aforementioned receiver is Based on the aforementioned wireless protocol, each of the TSWSS is received through the wireless channel. Based on each of the received TSWSSs, the respective TSCIs are obtained. Each of the TSCIs is reported locally to the at least one upper layer in the receiver. The system is configured to perform the aforementioned sensing-based task in a centralized manner based on multiple locally reported TSCIs. The system according to claim 19.

22. The sensing initiator device is an access point device (AP) of the wireless data communication network, The aforementioned transmitter is an AP, The receiver is a client device of the wireless data communication network and functions as a sensing responder device. The receiver reports the TSCI locally. The locally reported TSCI is available on the client device for the sensing-based task. The system according to claim 11.

23. The TSWSS is broadcast from the transmitter to the receiver. The system according to claim 22.

24. The wireless sensing measurement is trigger-based (TB) using null data packet (NDP) frames and NDP announcement (NDPA) frames based on the wireless protocol. The system according to claim 22.

25. The venue further includes at least one additional receiving device, individually configured by the sensing initiator device based on the radio protocol to function as a sensing receiver device, The transmitter is configured to transmit each TSWSS to each additional receiver based on the wireless protocol. Each additional receiver is Based on the aforementioned wireless protocol, each of the TSWSS is received through the wireless channel. Based on each of the received TSWSSs, each TSCI is obtained, Each of the aforementioned TSCIs is reported locally to each of the aforementioned at least one upper layer of the additional receivers, The system is configured to perform the sensing-based task in a decentralized manner based on multiple locally reported TSCIs, by locally executing each of the parts of the sensing-based task based on each of the locally reported TSCIs. The system according to claim 22.

26. The sensing initiator device is a client device of the wireless data communication network. The sensing responder device is an access point device (AP) of the wireless data communication network, The transmitter is the client device, The aforementioned receiver is the AP, The receiver reports the TSCI locally, The locally reported TSCI is available to the AP for the sensing-based task. The system according to claim 11.

27. The sensing initiator device is a client device of the wireless data communication network. The sensing responder device is an access point device (AP) of the wireless data communication network, The transmitter is the AP, The receiver is the client device, The receiver reports the TSCI locally, The locally reported TSCI is available on the client device for the sensing-based task. The system according to claim 11.

28. The sensing initiator device is an access point device (AP) of the wireless data communication network, The first client device of the aforementioned wireless data communication network is a first sensing responder device, The second client device of the aforementioned wireless data communication network is a second sensing responder device, The transmitter is the first client device, The receiver is the second client device, The receiver reports the TSCI locally, The locally reported TSCI is available on the second client device for the sensing-based task. The system according to claim 11.

29. A wireless device in a wireless data communication network for wireless sensing, Processor and A memory connected to the aforementioned processor in a communicative manner, The processor includes a receiver that is communicatively coupled to the processor, Additional wireless devices within the wireless data communication network are configured to transmit time-series wireless sounding signals (WSS) based on the wireless protocol associated with the wireless data communication network. The aforementioned wireless data communication network comprises a physical (PHY) layer, a media access control (MAC) layer, and at least one higher layer. The aforementioned receiver is The time series (TSWSS) of the WSS is received via the venue's wireless channel based on the wireless protocol. To obtain sensing measurement results, multiple wireless sensing measurements are performed based on the received TSWSS. The PHY layer or MAC layer of the receiver reports the sensing measurement results to at least one higher layer of the receiver. The at least one upper layer of the receiver performs a sensing-based task based on the sensing measurement results. It is configured to Wireless device.

30. A method for wireless sensing, A wireless data communication network comprising a physical (PHY) layer, a media access control (MAC) layer, and at least one higher layer, wherein a transmitter within the wireless data communication network transmits a time-series wireless sounding signal (WSS) based on a wireless protocol associated with the wireless data communication network. The receiver in the aforementioned wireless data communication network receives the time series (TSWSS) of the WSS via the venue's wireless channel based on the wireless protocol, The receiver performs multiple wireless sensing measurements based on the received TSWSS in order to acquire sensing measurement results, The PHY layer or MAC layer of the receiver reports the sensing measurement results to at least one higher layer of the receiver. The receiver includes performing a sensing-based task based on the sensing measurement results by at least one upper layer of the receiver. method.

Images