Method, Apparatus and System for Wireless Tracking, Scanning and Surveillance - Patent application

Abstract

To provide methods, apparatus, and systems for wireless tracking with graph-based particle filtering.SOLUTION: A wireless monitoring system comprises: a transmitter transmitting a series of probe signals; a receiver; and a processor. The receiver is configured to: receive the series of probe signals modulated by a wireless multipath channel and an object moving in a place; and obtain a time-series channel information (TSCI) of the wireless multipath channel from the series of probe signals. The processor is configured to: monitor a motion of the object relative to a map on the basis of the TSCI; determine an incremental distance travelled by the object in an incremental time period on the basis of the TSCI; and compute a next location of the object at a next time on the basis of at least one of a current location of the object at a current time, and a direction of the motion during the incremental time period.SELECTED DRAWING: None

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS The entire disclosure and priority claim for each of the following cases is incorporated by reference into this application: (a) U.S. Provisional Patent Application No. 62 / 846,686, filed May 12, 2019, entitled "Method, Apparatus, and System for Wireless Inertial Measurements"; (b) U.S. Provisional Patent Application No. 62 / 980,206, filed February 22, 2020, entitled "Method, Apparatus, and System for Wireless Sensing"; (c) U.S. Provisional Patent Application No. 62 / 849,853, filed May 18, 2019, entitled "Method, Apparatus and System for Wireless Artificial Intelligence in Smart Cars"; (d) U.S. Provisional Patent Application No. 62 / 846,688, filed May 12, 2019, entitled "Method, Apparatus, and System for Processing and Preprocessing Lifelogs Based on Wireless Signals"; (e) U.S. Provisional Patent Application No. 62 / 868,782, filed June 28, 2019, entitled "Method, Device, and System for Monitoring Vital Signs Using High Frequency Radio Signals"; (f) U.S. patent application Ser. No. 16 / 798,337, filed February 22, 2020, entitled "Method, Apparatus, and System for Wireless Object Scanning"; (g) U.S. patent application Ser. No. 16 / 798,343, filed February 22, 2020, entitled "Method, Apparatus, and System for Wireless Object Tracking"; (h) U.S. patent application Ser. No. 16 / 870,996, filed May 10, 2020, entitled "Method, Apparatus, and System for Wireless Inertial Measurements"; (i) U.S. Patent Application No. 16 / 871,000, filed May 10, 2020, entitled "Method, Apparatus, and System for Wireless Tracking with Graph-Based Particle Filtering"; (j) U.S. Patent Application No. 16 / 871,004, filed May 10, 2020, entitled "Method, Apparatus, and System for Person Counting and Recognition Based on Rhythmic Movement Monitoring"; (k) U.S. Patent Application No. 16 / 871,006, filed May 10, 2020, entitled “Method, Device, and System for Monitoring Vital Signs Using High Frequency Wireless Signals.”

[0002] The present disclosure relates generally to wireless object tracking and scanning, and wireless vital signs monitoring. More specifically, the present disclosure relates to tracking movement, orientation, direction, rotation angle, scanning objects, respiratory rate monitoring, people counting, and person identification with high accuracy based on wireless channel information in a highly cluttered environment. [Background technology]

[0003] Motion measurement is an essential input for a variety of applications, including robot navigation, indoor tracking, and mobile gaming, and has been widely used in robots, drones, automobiles, unmanned vehicles, various home appliances, and many other moving objects. The mainstream technology has been using inertial measurement units (IMUs) for motion tracking. The increasing demand for accurate and robust motion tracking and the increasing production of smart devices are driving the IMU market, which is expected to grow from $15.71 billion in 2016 to $21.74 billion by 2022. Improvements in motion measurement have a significant impact on many systems and applications.

[0004] Accurate and robust motion measurement is not trivial. Typical IMUs, implemented with sensors such as accelerometers that measure linear acceleration, gyroscopes that calculate angular velocity, and magnetometers that report orientation, are known to suffer from significant errors and drift. For example, accelerometers have difficulty measuring distance traveled due to noisy readings, while magnetometers do not report facing direction and are easily distorted by the surrounding environment. Meanwhile, gyroscopes experience significant drift introduced by integration, especially over the long term. These limitations hinder many applications requiring accurate motion processing, such as indoor tracking, virtual reality, and motion-sensing games.

[0005] In recent years, wireless signals have been used to locate and track targets. However, these systems can only track location while suffering from significant general limitations that prevent ubiquitous, accurate inertial measurements. First, all of these systems require one or more precisely installed access points (APs) and accurate information about their location and / or orientation. Small errors in the AP's geometric information lead to large position errors. Second, these systems can only determine one or two of multiple motion parameters from successive position estimates. They do not directly measure multiple motion parameters and cannot track angular motion in place. Third, these systems face accuracy limitations affected by frequency bandwidth, number of antennas, and synchronization errors on commercially available, off-the-shelf WiFi, and may degrade or even fail in complex non-line-of-sight (NLOS) scenarios.

[0006] Wireless imaging based on radio frequency (RF) signals is a long-standing and challenging problem in the evolving world of wireless sensing. Given the ubiquity of Wi-Fi (WiFi), it is particularly interesting to enable imaging using WiFi reflected signals, i.e., to create images of humans and objects without attaching a wireless source to the target. This would enable widespread sensing of humans and objects with rich context, including not only position but also silhouette / shape, size, and pose, for emerging applications such as interactive games, pose estimation, motion estimation, and human recognition, all in a privacy-preserving, lightweight, and cost-effective manner.

[0007] Despite progress in repurposing WiFi devices for wireless sensing, such as activity sensing, gesture recognition, and vital sign monitoring, RF imaging faces significant challenges and remains unsolved. Previous research related to high-frequency imaging over the WiFi band has been able to track human movement and activity, map large obstacles, and detect malicious objects. However, these approaches either require specialized hardware with large antennas unavailable in commodity radios or suffer from poor image quality. For example, when using WiFi radios with multiple antennas for object imaging, existing systems are inherently limited by WiFi signals at 2.4 GHz / 5 GHz and / or require multiple specialized frequency-modulated continuous-wave (FMCW) radars with relatively large arrays. In general, the imaging capabilities of 2.4 GHz / 5 GHz WiFi are fundamentally limited by their narrow bandwidth, limited number of antennas, and large wavelengths. Current millimeter-wave (mmWave) systems can perform imaging using large-lens radars or dedicated circuitry, but these are specialized radars and are not suitable for ubiquitous applications.

[0008] Over the past decade, indoor localization systems have transitioned from active device-based themes to passive, device-free methods—methods that localize and track users without requiring them to carry or wear a device. Passive indoor localization can enable a wide range of applications requiring ubiquitous tracking of a user's location, while allowing or disallowing users to carry or wear a device. For example, a smart television (TV) can determine a user's location and intelligently rotate its viewing angle or power on / off as the user arrives or leaves. Energy-efficient buildings will also be able to determine occupancy locations, count people, and adjust heating and lighting accordingly. This can be used in smart homes to control Internet of Things (IoT) devices based on the user's location. All of these applications will become more practical and attractive in natural indoor environments when multiple people can be located.

[0009] Previous research has focused on single-target tracking and fails in the presence of multiple people, primarily because the reflected signals from all targets, as well as other objects in the environment, are superimposed together. Furthermore, these efforts primarily focus on tracking moving targets and require tedious calibration to localize static objects. To achieve multi-person localization, many previous proposals rely on dense deployment and / or prior training of many devices. Alternatively, specialized hardware is built with many antennas in very large form factors. One existing system requires a large number of receivers and presumes knowledge of the number of people present. For all of these systems, the resolution to resolve weak signals reflected by multiple targets is fundamentally limited by the number of antennas and system bandwidth.

[0010] Continuous monitoring of respiration as well as heart rate is important for the early detection and prevention of potentially fatal diseases. Current solutions typically require users to wear dedicated devices such as wrist-worn sensors or chest straps that require contact with the human body during monitoring, making them inconvenient and uncomfortable for users. With the rapid development of the Internet of Things (IoT), wireless sensing has attracted increasing attention in recent years due to the ubiquitous deployment of wireless devices. It has been proven that human presence affects wireless signal propagation, enabling the capability to wirelessly monitor human subjects by analyzing electromagnetic waves (EM).

[0011] Recent research into wireless human vital sign detection leverages existing communication infrastructure (e.g., Wi-Fi) to provide widespread, user-friendly, and affordable solutions for health monitoring. Over the past decade, significant efforts have been devoted to designing and testing various architectures for robust vital sign monitoring using off-the-shelf Wi-Fi devices. However, due to the relatively low carrier frequency of Wi-Fi systems, the number of antennas in 2.4 / 5 GHz Wi-Fi is low, resulting in poor spatial resolution. Furthermore, the narrow bandwidth of Wi-Fi systems results in coarse range resolution (7.5 meters for a 20 MHz bandwidth). Therefore, in the presence of multiple users, the received radio frequency (RF) signal is reflected by multiple users, making it difficult to extract vital signs for each. Therefore, most studies assume that only one person is present or that various users have different breathing rates. Furthermore, because RF signals are reflected by multiple scattering, the embedded heartbeat signal has an extremely low signal-to-noise ratio (SNR). Thus, estimating heart rate using commodity Wi-Fi is extremely challenging.

[0012] Human-centric sensing via radio frequency (RF) is gaining increasing interest in various Internet of Things (IoT) applications. The demand for accurate and passive awareness of the environment is rapidly increasing in many applications. For example, smart homes can adjust lighting and ventilation systems based on occupancy rates to improve energy efficiency. Person recognition in smart homes enables user authentication for home security and privacy protection. In addition to environmental awareness, monitoring the state of humans in the environment has also attracted attention, and respiration / respiratory rate, a significant vital sign, has been a hot topic for RF sensing.

[0013] Compared to traditional methods that use dedicated sensors to monitor respiration rate, RF sensing offers a contactless solution. RF-based solutions can be divided into two categories: radar-based and WiFi-based. For radar-based methods, previous research has demonstrated the feasibility of using millimeter wave (mmWave) or ultra-wideband (UWB) to monitor respiration and heart rate. While these systems can achieve high accuracy, dedicated devices hinder their deployment. Conversely, WiFi-based methods can be easily deployed and have been studied over the past decade. Received signal strength (RSS) measured by WiFi devices has been used to measure chest movement during respiration. However, the accuracy of respiration rate estimation decreases when the subject does not hold the device. Fine-grained channel state information (CSI) is more sensitive to environmental changes, but it has been utilized to capture minute movements caused by respiration. However, due to the omnidirectional propagation and narrow bandwidth of commonly used 2.4 / 5 GHz WiFi, the received signal can reflect from multiple people in an indoor space. This makes it difficult to extract vital signs of multiple people from the reflected signals. Most existing studies assume that there is only one person in the observation area, or that different individuals have different breathing rates and the number of people is known in advance. Summary of the Invention

[0014] The present disclosure generally relates to wireless tracking systems. More specifically, the present disclosure relates to accurately tracking distance, direction, and rotation angle based on wireless channel information in a rich scattering environment. The present disclosure also relates to measuring multiple parameters of an object's motion, i.e., distance, direction, and rotation angle, in a rich scattering environment, such as an indoor environment or an urban metropolitan area, an enclosed environment, or an underground environment. The present disclosure also relates to accurately tracking human respiration / respiratory rate and heart rate variability based on wireless channel information in a rich scattering environment.

[0015] In one embodiment, a wireless monitoring system is described. The wireless monitoring system includes a transmitter, a receiver, and a processor. The transmitter is configured to transmit a series of probe signals within a venue via a wireless multipath channel. The receiver is configured to receive, through the wireless multipath channel between the transmitter and the receiver, the series of probe signals modulated by the wireless multipath channel and an object moving within the venue, and to derive a time series of channel information (TSCI) for the wireless multipath channel from the series of probe signals modulated by the wireless multipath channel and the object. The processor is configured to monitor movement of the object relative to a map based on the TSCI, determine incremental distances traveled by the object during incremental time periods based on the TSCI, and calculate a next position of the object within the map at a next time based on at least one of the object's current position at a current time, the incremental distance, and the direction of movement during the incremental time periods. According to various embodiments, the processor is physically coupled to at least one of the transmitter and the receiver.

[0016] In another embodiment, a method executed by a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory to be executed by the processor are described, the method including obtaining time-series channel information (TSCI) of a wireless multipath channel within a location, the TSCI being extracted from a series of probe signals transmitted from a transmitter to a receiver over the wireless multipath channel, the series of probe signals being modulated by an object moving within the wireless multipath channel and the location, monitoring movement of the object relative to a map based on the TSCI, determining incremental distances traveled by the object within incremental time periods based on the TSCI, and calculating a next position of the object at a next time in the map based on at least one of a current position of the object at a current time, the incremental distances, and a direction of movement during the incremental time periods.

[0017] In another embodiment, a wireless monitoring system is described. The wireless monitoring system includes a transmitter, a plurality of heterogeneous receivers, and a processor. The transmitter is configured to transmit a series of probe signals within a location via a wireless multipath channel. Each of the plurality of heterogeneous receivers is configured to receive, via the wireless multipath channel, a sequence of probe signals modulated by the wireless multipath channel and a plurality of objects moving within the location, and to obtain time-series channel information (TSCI) of the wireless multipath channel from the wireless multipath channel and the sequence of probe signals modulated by the plurality of objects. The processor is configured to monitor movement of the plurality of objects relative to a map based on the TSCI obtained by the plurality of heterogeneous receivers, determine respective incremental distances traveled by each object in each incremental time period based on the respective TSCIs, and calculate respective next positions of the objects in the map at respective next times based on at least one of the respective current positions of the objects at respective current times, the respective incremental distances, and respective directions of movement of each object during each incremental time period. According to various embodiments, the processor is physically coupled to the transmitter and at least one of the plurality of heterogeneous receivers.

[0018] In one embodiment, an object tracking system is described. The object tracking system includes a transmitter, a receiver, and a processor. The transmitter is configured to transmit a wireless signal through a wireless multipath channel. The receiver is configured to receive the wireless signal through the wireless multipath channel and extract a plurality of time-series channel information (TSCI) of the wireless multipath channel from the wireless signal. Each of the plurality of TSCIs is associated with an antenna of the transmitter and an antenna of the receiver. One of the transmitter and the receiver is a stationary device. The other of the transmitter and the receiver is a mobile device that moves with the object. The processor is configured to determine that a first channel information (CI) at a first time of a first TSCI associated with a first antenna of the mobile device matches a second CI at a second time of a second TSCI associated with a second antenna of the mobile device, both the first TSCI and the second TSCI associated with a specific antenna of the stationary device; calculate at least one movement parameter of the mobile device based on at least one of the plurality of TSCIs, a configuration of the antenna of the mobile device, a configuration of at least one antenna of the stationary device, a time difference between the first time and the second time, and a distance between the first antenna and the second antenna; and calculate spatiotemporal information (STI) of the mobile device based on at least one of the at least one movement parameter, the first time, the second time, the configuration of the antenna of the mobile device, the configuration of the at least one antenna of the stationary device, and a past STI, where at least one of the movement parameter and the STI is related to a current movement of the mobile device; and track the object and the mobile device based on the STI.

[0019] In another embodiment, a method executed by a processor is described, the method including a memory communicatively coupled to the processor and a set of instructions stored in the memory to be executed by the processor, the method including: acquiring a plurality of time-series channel information (TSCIs) of a wireless multipath channel, the plurality of TSCIs being extracted from a wireless signal transmitted from a first wireless device to a second wireless device over the wireless multipath channel, each of the plurality of TSCIs being associated with an antenna of the first wireless device and an antenna of the second wireless device, one of the first wireless device and the second wireless device being a stationary device and the other of the first wireless device and the second wireless device being a mobile device moving with an object; and determining that a first channel information (CI) at a first time of the first TSCI associated with the first antenna of the mobile device matches a second CI at a second time of a second TSCI associated with the second antenna of the mobile device, the first TSCI and the second TSCI being matched. determining that the plurality of TSCIs are associated with the same antenna of the stationary device; calculating at least one movement parameter of the mobile device based on at least one of the plurality of TSCIs, the antenna configuration of the mobile device, the configuration of at least one antenna of the stationary device, a time difference between the first time and the second time, and a distance between the first antenna and the second antenna; calculating spatiotemporal information (STI) of the mobile device based on at least one of the at least one movement parameter, the first time, the second time, the antenna configuration of the mobile device, the configuration of the at least one antenna of the stationary device, past STI, and past movement parameters, wherein at least one of the movement parameters and the STI are related to a current movement of the mobile device; and tracking at least one of the object and the mobile device based on the STI.

[0020] In another embodiment, an object tracking system is described. The object tracking system includes a transmitter, a receiver including a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory for execution by the processor. The transmitter is configured to transmit a wireless signal over a wireless multipath channel. The receiver receives the wireless signal through the wireless multipath channel and extracts from the wireless signal a plurality of time-series channel information (TSCI) of the wireless multipath channel, where each of the plurality of TSCIs is associated with an antenna of the transmitter and an antenna of the receiver, one of the transmitter and receiver being stationary and one of the transmitter and receiver being a mobile device moving with the object. The receiver receives the wireless signal over the wireless multipath channel and extracts from the wireless signal a plurality of time-series channel information (TSCI) of the wireless multipath channel, where each of the plurality of TSCIs is associated with an antenna of the transmitter and an antenna of the receiver, where one of the transmitter and receiver is stationary and one of the transmitter and receiver is a mobile device moving with the object. The receiver receives the plurality of TSCIs and determines that a first channel information (CI) at a first time instant of a first TSCI associated with a first antenna of the mobile device matches a second channel information (CI) at a second time instant of a second TSCI associated with a second antenna of the mobile device, where both the first TSCI and the second TSCI are associated with a particular antenna of the stationary device. The receiver then extracts the plurality of TSCIs. Calculate at least one movement parameter of the moving device based on at least one of the CI, the antenna configuration of the moving device, the configuration of at least one antenna of the stationary device, the difference between the first time and the second time, and the distance between the first antenna and the second antenna; calculate spatial temporal information (STI) of the moving device based on at least one of the at least one movement parameter, the first time, the second time, the antenna configuration of the moving device, the configuration of at least one antenna of the stationary device, the past STI, and the past movement parameter, where at least one of the movement parameters and the STI is related to the current movement of the moving device; and track the at least one object and the moving device based on the STI.

[0021] In one embodiment, this disclosure relates to a super-resolution imaging system that functions as a millimeter-wave camera on a commodity 60 GHz WiFi device. We propose a new super-resolution imaging algorithm based on multiple signal classification (MUSIC) with joint transmitter smoothing. Experiments show that the disclosed system achieves remarkable imaging performance for both humans and everyday objects, comparable to that of dedicated depth sensors such as Kinect, and can image a person behind thin drywall.

[0022] In one embodiment, a wireless scanning system is described. The wireless scanning system includes a transmitter, a receiver, and a processor. The transmitter is configured to transmit a first wireless signal using multiple transmit antennas toward an object within the location through a wireless multipath channel of the location. The receiver is configured to receive a second wireless signal using multiple receive antennas through the wireless multipath channel between the transmitter and the receiver. The second wireless signal differs from the first wireless signal due to modulation of the first wireless signal by the wireless multipath channel and the object. The processor is configured to obtain a set of channel information (CI) for the wireless multipath channel based on the second wireless signal received by the receiver and to calculate an image of the object based on the set of CIs. Each CI in the set is associated with a respective one of the multiple transmit antennas and a respective one of the multiple receive antennas. According to various embodiments, the processor is physically coupled to at least one of the transmitter and the receiver.

[0023] In another embodiment, a described apparatus for wireless scanning is located within a location where a transmitter and a receiver are disposed. The described apparatus includes a processor and at least one of the transmitter and the receiver. The transmitter is configured to transmit a first wireless signal using multiple transmit antennas toward an object within the location through a wireless multipath channel of the location. The receiver is configured to receive a second wireless signal using multiple receive antennas via the wireless multipath channel between the transmitter and the receiver. The second wireless signal differs from the first wireless signal due to modulation of the first wireless signal by the wireless multipath channel and the object. The processor is configured to obtain a set of channel information (CI) for the wireless multipath channel based on the second wireless signal, where each CI in the set is associated with a respective one of the multiple transmit antennas and a respective one of the multiple receive antennas, and to calculate an image of the object based on the set of CIs.

[0024] In one embodiment, the device includes a receiver but does not include a transmitter. The receiver receives the second wireless signal and extracts CI, e.g., channel state information (CSI), to perform the object scan. In another embodiment, the device includes a transmitter but does not include a receiver. The CSI is extracted by the receiver and obtained by a processor for the object scan. In yet another embodiment, the device includes a transmitter but does not include a receiver. The CSI is extracted in the receiver, which transmits the CSI to the transmitter. The object scan is performed in the transmitter.

[0025] In another embodiment, a method executed by a processor, a memory communicatively coupled to the processor, and a collection of instructions stored in the memory to be executed by the processor are described. The method includes obtaining a set of channel information (CI) for a wireless multipath channel of a location, wherein a transmitter transmits a first wireless signal using multiple transmit antennas through the wireless multipath channel of the location toward an object in the location, and a receiver receives a second wireless signal using multiple receive antennas through the wireless multipath channel, calculating a set of CIs for the wireless multipath channel based on the second wireless signal, the second wireless signal being different from the first wireless signal due to modulation of the first wireless signal by the wireless multipath channel and the object, and calculating an imaging of the object based on the set of CIs.

[0026] In one embodiment, the present disclosure relates to a multiple-person location and tracking system using a single 802.11ad device. High spatial resolution is achieved by implementing digital beamforming on the antenna array of a 60 GHz chipset. Multiple object detection algorithms and robust clustering methods are used to locate and count multiple users. The disclosed system also recovers movement trajectories through a continuous tracking algorithm. According to one embodiment, experiments on a commodity off-the-shelf (COTS) 802.11ad radio demonstrate that the disclosed system achieves a median location error of 9.9 cm and 19.7 cm for dynamic and static use, respectively, with a people counting error of less than 1 in 97.8% of cases.

[0027] In one embodiment, a wireless tracking system is described. The wireless tracking system includes a transmitter, a receiver, and a processor. The transmitter is configured to transmit a first wireless signal via a wireless multipath channel of the location using multiple transmit antennas toward at least one object within the location. The receiver is configured to receive a second wireless signal via the wireless multipath channel between the transmitter and the receiver using multiple receive antennas. The second wireless signal differs from the first wireless signal due to modulation of the first wireless signal by the wireless multipath channel and the at least one object. The processor is configured to obtain a set of channel information (CI) for the wireless multipath channel based on the second wireless signal received by the receiver, and to simultaneously track at least one object based on the set of CIs. Each CI in the set is associated with a respective one of the multiple transmit antennas and a respective one of the multiple receive antennas. According to various embodiments, the processor is physically coupled to at least one of the transmitter and the receiver.

[0028] In another embodiment, the described wireless tracking apparatus is located within a location where a transmitter and a receiver are disposed. The described apparatus includes a processor and at least one of the transmitter and the receiver. The transmitter is configured to transmit a first wireless signal using multiple transmit antennas toward at least one object within the location over a wireless multipath channel of the location. The receiver is configured to receive a second wireless signal using multiple receive antennas over the wireless multipath channel between the transmitter and the receiver. The second wireless signal differs from the first wireless signal due to modulation of the first wireless signal by the wireless multipath channel and the at least one object. The processor is configured to obtain a set of channel information (CI) for the wireless multipath channel based on the second wireless signal, each CI being associated with a respective one of the multiple transmit antennas and a respective one of the multiple receive antennas, and to simultaneously track at least one object based on the set of CIs.

[0029] In one embodiment, the device includes a receiver but does not include a transmitter. The receiver receives the second wireless signal and extracts CI, e.g., channel state information, to perform object tracking. In another embodiment, the device includes a transmitter but does not include a receiver. CSI is extracted by the receiver and obtained by a processor for object tracking. In yet another embodiment, the device includes a transmitter but does not include a receiver. CSI is extracted in the receiver, which transmits the CSI to the transmitter. Object tracking is performed in the transmitter.

[0030] In another embodiment, a method executed by a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory to be executed by the processor are described, the method including: acquiring a set of channel information (CI) for a wireless multipath channel of a location, wherein a transmitter transmits a first wireless signal using multiple transmit antennas through the wireless multipath channel of the location toward at least one object within the location, and a receiver receives a second wireless signal using multiple receive antennas through the wireless multipath channel, calculating a set of CIs for the wireless multipath channel based on the second wireless signal, the second wireless signal being different from the first wireless signal due to the wireless multipath channel and modulation of the first wireless signal by the at least one object; and simultaneously tracking the at least one object based on the set of CIs.

[0031] In one embodiment, a wireless monitoring system is described. The wireless monitoring system includes a transmitter, a receiver, and a processor. The transmitter is configured to transmit wireless signals through a wireless multipath channel of a location using N1 transmit antennas, while a first object within the location performs a first repetitive motion. The receiver is configured to receive the wireless signals through the wireless multipath channel using N2 receive antennas and extract a plurality of time-series channel information (TSCI) of the wireless multipath channel from the wireless signals. N1 and N2 are positive integers. Each of the plurality of TSCIs is associated with a transmit antenna of the transmitter and a receive antenna of the receiver. The processor is configured to calculate first information of the first repetitive motion based on the plurality of TSCIs and monitor the first repetitive motion of the first object based on the first information.

[0032] In another embodiment, a method executed by a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory to be executed by the processor are described, the method including: transmitting wireless signals over a wireless multipath channel of a location using N1 transmit antennas of a first heterogeneous wireless device while an object in the location has a repetitive motion, receiving the wireless signals over the wireless multipath channel using N2 receive antennas of a second heterogeneous wireless device, obtaining a plurality of time-series channel information (TSCIs) of the wireless multipath channel from the wireless signals, where N1 and N2 are positive integers, each of the plurality of TSCIs associated with a transmit antenna of the first heterogeneous wireless device and a receive antenna of the second heterogeneous wireless device, calculating timing information of the repetitive motion based on the plurality of TSCIs, and monitoring the repetitive motion of the object based on the timing information.

[0033] In another embodiment, a wireless monitoring system is described. The wireless monitoring system includes a plurality of pairs of transmitters and receivers within a location, and a processor. For each of the plurality of pairs, each transmitter of the pair is configured to asynchronously transmit a respective wireless signal over a wireless multipath channel, a first object within the location has a first repetitive motion, and each receiver of the pair is configured to asynchronously receive the respective wireless signal over the wireless multipath channel and extract a respective time-series channel information (TSCI) of the wireless multipath channel from the respective wireless signal. The transmitter and receiver of a first pair of the plurality of pairs are collocate at a same location within the location, and the transmitter and receiver of a second pair of the plurality of pairs are located at two different locations within the location. The plurality of TSCIs are obtained by the receivers of the plurality of pairs. The processor is configured to calculate first information of the first repetitive motion based on the plurality of TSCIs and monitor the first repetitive motion of the first object based on the first information.

[0034] The present disclosure also relates to monitoring respiration rates, counting people, and identifying people in environments rich in clutter, such as indoor environments or urban metropolitan areas, enclosed environments, automobiles, etc. In one embodiment, the present disclosure relates to driver identification and recognition, driver and passenger respiration rate monitoring, passenger counting, and detecting the presence of unattended children based on wireless channel information, for example, in indoor environments or urban metropolitan areas, enclosed environments, automobiles, etc.

[0035] In one embodiment, a system for rhythmic motion monitoring is described. The system includes a transmitter, a receiver, and a processor. The transmitter is configured to transmit a wireless signal over a wireless multipath channel affected by the rhythmic motion of an object within a location. The receiver is configured to receive the wireless signal over the wireless multipath channel and extract N1 time series of channel information (TSCI) of the wireless multipath channel from the wireless signal modulated by the wireless multipath channel affected by the rhythmic motion of the object, each of the N1 TSCIs being associated with a transmitter antenna and a receiver antenna. The processor is configured to decompose each of the N1 TSCIs into N2 time series of channel information components (TSCICs), each of the N2 TSCICs including a respective component of channel information (CI) for the TSCI at that time, monitor the rhythmic motion of the object based on at least one of the N1 * N2 TSCICs and the N1 TSCIs (N1 and N2 are positive integers), and trigger a response action based on the monitoring of the rhythmic motion of the object.

[0036] In another embodiment, a method executed by a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory to be executed by the processor are described, the method including: acquiring N1 time series of channel information (TSCI) of a wireless multipath channel affected by rhythmic motion of an object within a location, the N1 TSCI being extracted from a wireless signal transmitted from a transmitter to a receiver over the wireless multipath channel, each of the N1 TSCI being associated with a transmitter antenna and an antenna of the receiver; decomposing each of the N1 TSCI into N2 time series of channel information components (TSCICs), each channel information component (CIC) of the N2 TSCICs including a respective component of channel information (CI) for the TSCI at that time, where N1 and N2 are positive integers; monitoring rhythmic motion of the object based on at least one of the N1*N2 TSCICs and the N1 TSCIs; and triggering a response action based on the monitoring of the rhythmic motion of the object.

[0037] In another embodiment, an apparatus for rhythmic motion monitoring at a location where a transmitter and a receiver are located is disclosed. The apparatus includes at least one of a transmitter and a receiver, and a processor. The transmitter is configured to transmit a wireless signal through a wireless multipath channel affected by the rhythmic motion of an object within the location. The receiver is configured to receive the wireless signal through the wireless multipath channel and extract N1 time series channel information (TSCI) of the wireless multipath channel from the wireless signal modulated by the wireless multipath channel affected by the rhythmic motion of the object. Each of the N1 TSCIs is associated with a transmitter antenna and a receiver antenna. The processor is configured to decompose each of the N1 TSCIs into N2 time-series channel information components (TSCICs), where each channel information component (CIC) of the N2 TSCICs includes a respective component of the channel information (CI) of the TSCI at that time, monitor rhythmic movement of the object based on at least one of the N1*N2 TSCICs and the N1 TSCIs, where N1 and N2 are positive integers, and trigger a response action based on the monitoring of the rhythmic movement of the object.

[0038] Other concepts relate to software for implementing the present disclosure for wireless rhythmic motion monitoring, object tracking and scanning, respiratory rate monitoring, people counting, and person identification in rich cluttered environments. Additional novel features will be set forth in part in the description that follows, and in part will become apparent to those skilled in the art upon examination of the following drawings and the accompanying drawings, or may be learned by the manufacture or operation of the embodiments. The novel features of the present disclosure may be realized and attained by practice or use of various aspects of the methods, instrumentalities, and combinations described in the detailed embodiments set forth below. [Brief explanation of the drawings]

[0039] The methods, systems, and / or devices described herein will be further explained with reference to exemplary embodiments, which will be described in detail with reference to the drawings, which are non-limiting exemplary embodiments, and in which like reference numerals represent like structure throughout the several views of the drawings.

[0040] [Figure 1] 1 illustrates the overall architecture of an indoor tracking system according to some embodiments of the present invention.

[0041] [Figure 2] 1 illustrates an exemplary antenna alignment according to some embodiments of the present invention.

[0042] [Figure 3A] 1 illustrates an example for estimating time delay from a TRRS matrix, where, according to some embodiments of the present disclosure, the maximum TRRS occurs only at a single time point when one antenna is at the position where the other antenna was at Δt time ago in super-resolution.

[0043] [Figure 3B] FIG. 10 illustrates an example for estimating time delays from a TRRS matrix for a linear motion trajectory of a three-antenna array, in accordance with some embodiments of the present disclosure.

[0044] [Figure 3C] 10 shows an example for estimating time delay from a TRRS matrix, where, according to some embodiments of the present disclosure, the TRRS decreases when the device is not moving along the antenna array line; however, there are still clear peaks for deviation angles within 15 degrees, allowing velocity estimation.

[0045] [Figure 4] 4A-4B illustrate exemplary graph-based map representations according to some embodiments of the present invention.

[0046] [Figure 5] 1 illustrates an example of a DTL according to some embodiments of the present invention.

[0047] [Figure 6] 1 illustrates an exemplary comparison of two distance estimation methods according to some embodiments of the present invention.

[0048] [Figure 7] 10 illustrates exemplary performance of antenna alignment distance estimation methods for different antenna pairs, according to some embodiments of the present disclosure.

[0049] [Figure 8] 10 illustrates exemplary performance of antenna alignment distance estimation methods for various antenna spacings, according to some embodiments of the present invention.

[0050] [Figure 9] 10 illustrates the overall tracking accuracy performance of distance estimation methods according to some embodiments of the present disclosure.

[0051] [Figure 10] 1 illustrates a flow diagram of a map augmentation tracking method according to some embodiments of the present invention.

[0052] [Figure 11] The figures show different step patterns resulting from different subjects, velocities, and sensor placements, with each figure showing a four-step (two stride cycle) acceleration series according to some embodiments of the present disclosure.

[0053] [Figure 12] 1 illustrates an exemplary finite state machine (FSM) for step detection according to some embodiments of the present invention.

[0054] [Figure 13] 13A-13C show examples of inertial-based step counting and radio-based distance estimation according to some embodiments of the present disclosure.

[0055] [Figure 14] 1 illustrates virtual antenna alignment in accordance with some embodiments of the present invention.

[0056] [Figure 15] 1 illustrates an example setup of a six-element circular array that can be constructed by co-locating the antennas of two commodity WiFi radios for two-dimensional measurements, according to some embodiments of the present disclosure.

[0057] [Figure 16] 16A-16C show example antenna arrays where linear, triangular, and square arrays produce at most 2, 6, and 12 tractable directions, respectively, according to some embodiments of the present disclosure.

[0058] [Figure 17] 1 illustrates the spatial resolution of time-reversed resonance intensity (TRRS) according to some embodiments of the present disclosure.

[0059] [Figure 18] 10 illustrates exemplary experimental results for a squared trajectory alignment matrix according to some embodiments of the present invention.

[0060] [Figure 19] 10 illustrates an exemplary antenna alignment during deflection setback, according to some embodiments of the present disclosure.

[0061] [Figure 20] 10 illustrates exemplary experimental results for motion detection according to some embodiments of the present invention.

[0062] [Figure 21] 10A-10C illustrate exemplary experimental results of peak tracking, in which alignment peaks are accurately and robustly tracked despite measurement noise and imperfect retracking, in accordance with some embodiments of the present disclosure.

[0063] [Figure 22] 10 shows an example of experimental results of movement amount estimation accuracy according to some embodiments of the present invention.

[0064] [Figure 23] 10 illustrates exemplary experimental results of estimated heading errors for different directions according to some embodiments of the present invention.

[0065] [Figure 24] 10 illustrates exemplary experimental results of a cumulative distribution function of heading errors according to some embodiments of the present invention.

[0066] [Figure 25] 1 illustrates exemplary handwritten characters recognized using a RIM according to some embodiments of the present invention.

[0067] [Figure 26] 10 illustrates exemplary experimental results for gesture recognition according to some embodiments of the present invention.

[0068] [Figure 27] 1 shows a flow diagram for measuring multiple parameters of object motion according to some embodiments of the present invention.

[0069] [Figure 28] 1 illustrates an exemplary RF-based inertial measurement unit configuration with three antennas, according to some embodiments of the present disclosure.

[0070] [Figure 29] 1 illustrates an example of TRRS spatial attenuation according to some embodiments of the present invention.

[0071] [Figure 30] 1 illustrates an exemplary octagonal antenna array for azimuth measurements, according to some embodiments of the present invention.

[0072] [Figure 31] 1 illustrates an exemplary antenna moving trace according to some embodiments of the present invention.

[0073] [Figure 32] 10 illustrates another exemplary antenna moving trace according to some embodiments of the present invention.

[0074] [Figure 33] 1 illustrates an exemplary offset according to some embodiments of the present invention.

[0075] [Figure 34] 1 illustrates exemplary angular ranges according to some embodiments of the present invention.

[0076] [Figure 35] 1 illustrates an exemplary first case direction estimation according to some embodiments of the present invention.

[0077] [Figure 36] 10 illustrates an exemplary second case direction estimation according to some embodiments of the present invention.

[0078] [Figure 37] 1 illustrates an exemplary pre-position rotationally aligned antenna pair in which antenna i rotates to a position closest to the initial position of antenna j, in accordance with some embodiments of the present disclosure.

[0079] [Figure 38] 1 illustrates an exemplary coordinate system for wireless object scanning, according to some embodiments of the present disclosure.

[0080] [Figure 39] 1 illustrates spatial smoothing according to some embodiments of the present disclosure.

[0081] [Figure 40] 10 illustrates joint transmitter smoothing according to some embodiments of the present disclosure.

[0082] [Figure 41] FIG. 1 illustrates an example of channel impulse response (CIR) performance according to some embodiments of the present disclosure.

[0083] [Figure 42] FIG. 42 illustrates an example CIR obtained after background and noise cancellation according to some embodiments of the present disclosure.

[0084] [Figure 43] 1 illustrates an exemplary detection of a region of interest (RoI) according to some embodiments of the present disclosure.

[0085] [Figure 44] 10 illustrates examples of spatial spectra for different directions, according to some embodiments of the present disclosure.

[0086] [Figure 45] 1 illustrates exemplary system performance for imaging a human, according to some embodiments of the present disclosure.

[0087] [Figure 46] 1 illustrates exemplary system performance for different users, according to some embodiments of the present disclosure.

[0088] [Figure 47] 1 illustrates an exemplary system performance comparison between different spatial spectrum estimators, according to some embodiments of the present disclosure.

[0089] [Figure 48] 1 illustrates a flowchart of an example approach for wireless object scanning, consistent with some embodiments of the present disclosure.

[0090] [Figure 49] 1 illustrates an example system for wireless object tracking, according to some embodiments of the present disclosure.

[0091] [Figure 50] 1 illustrates an exemplary measured channel impulse response (CIR) in accordance with some embodiments of the present disclosure.

[0092] [Figure 51] 1 illustrates an exemplary coordinate system according to some embodiments of the present disclosure.

[0093] [Figure 52] 10 shows the results of measuring the effect of near-perspective effect and weighted resampling, according to some embodiments of the present disclosure.

[0094] [Figure 53] 1 illustrates an example metric for detecting a region of interest (RoI) with two subjects standing in front of a device, according to some embodiments of the present disclosure.

[0095] [Figure 54] 1 illustrates an exemplary completed RoI according to some embodiments of the present disclosure.

[0096] [Figure 55] FIG. 2 illustrates an example of a spatial spectrum according to some embodiments of the present disclosure.

[0097] [Figure 56] 1 illustrates exemplary three-dimensional information of two objects extracted from spatial spectra, according to some embodiments of the present disclosure.

[0098] [Figure 57] 1 illustrates an exemplary robust clustering according to some embodiments of the present disclosure.

[0099] [Figure 58] 1 illustrates an exemplary weighted bipartite graph matching according to some embodiments of the present disclosure.

[0100] [Figure 59] 1 illustrates exemplary position and height estimation according to some embodiments of the present disclosure.

[0101] [Figure 60] 10 illustrates an exemplary estimation of the number of users presented in a region of interest, according to some embodiments of the present disclosure.

[0102] [Figure 61] 1 illustrates exemplary multiple person tracking performance according to some embodiments of the present disclosure.

[0103] [Figure 62] 1 illustrates the advantages of robust clustering according to some embodiments of the present disclosure.

[0104] [Figure 63] 10 illustrates exemplary wireless tracking results according to some embodiments of the present disclosure.

[0105] [Figure 64] 1 illustrates a flowchart of an example method for wireless object tracking, according to some embodiments of the present disclosure.

[0106] [Figure 65] 1 illustrates a process flow of the disclosed system, according to some embodiments of the present invention.

[0107] [Figure 66A] 1 illustrates coordinates of the disclosed system, according to some embodiments of the present invention.

[0108] [Figure 66B] 1 illustrates an exemplary phase signal of a tap containing vital signs, according to some embodiments of the present invention.

[0109] [Figure 67A]1 illustrates a one-dimensional (1D) constant false alarm rate (CFAR) window used with range dimensions to reflect object detection, according to some embodiments of the present disclosure.

[0110] [Figure 67B] 1 illustrates a two-dimensional (2D) constant false alarm rate (CFAR) window used in angular dimensions to reflect object detection, according to some embodiments of the present disclosure.

[0111] [Figure 68] 1 illustrates a 1D-CFAR for reflecting object detection according to some embodiments of the present invention.

[0112] [Figure 69] 1 illustrates exemplary phases for target detection according to some embodiments of the present invention.

[0113] [Figure 70] 1 illustrates an example of cell merging according to some embodiments of the present disclosure.

[0114] [Figure 71] 10 illustrates an exemplary effect of respiratory disturbance removal according to some embodiments of the present invention.

[0115] [Figure 72] 10 illustrates estimation results from dynamic programming according to some embodiments of the present invention.

[0116] [Figure 73] 1 shows experimental setup and results for long-distance effects according to some embodiments of the present invention.

[0117] [Figure 74] 1 shows experimental setup and results for the influence of orientation, according to some embodiments of the present invention.

[0118] [Figure 75] 1 shows experimental setup and results for incident angle impingement according to some embodiments of the present invention.

[0119] [Figure 76] 1 shows experimental setup and results for the effect of occlusion, according to some embodiments of the present invention.

[0120] [Figure 77] 10 illustrates exemplary detection rates and estimation accuracies for three user cases according to some embodiments of the present invention.

[0121] [Figure 78] 1 illustrates an exemplary process flow for a system according to various embodiments of the present disclosure.

[0122] [Figure 79] 10 illustrates an exemplary power spectral density of CIR amplitudes for 6 links with a scenario of 3 people sitting in a car with breathing rates of [10, 14, 15] bpm, according to various embodiments of the present disclosure.

[0123] [Figure 80] 10 illustrates exemplary normalized power spectral densities of various links after subcarrier (SC) combining in a scenario where three people are sitting in a car with breathing rates of [10, 14, 15] bpm, according to various embodiments of the present disclosure.

[0124] [Figure 81] 10 shows exemplary power spectral densities after concatenation combination for three people sitting in a car with breathing rates of [10, 14, 15] bpm, according to various embodiments of the present disclosure.

[0125] [Figure 82] 1 shows an example spectrum after link combining for a scenario of three people sitting in a car breathing naturally and their average breathing rates are [10, 14, 15] bpm, according to various embodiments of the present disclosure.

[0126] [Figure 83] 1 illustrates an example procedure for iterative dynamic programming (IDP) according to various embodiments of the present disclosure.

[0127] [Figure 84] 10 shows exemplary traces found by an IDP in four adjacent time windows, according to various embodiments of the present disclosure.

[0128] [Figure 85] 84A-84D show an example of trace connection results for a window corresponding to the figure, according to various embodiments of the present disclosure.

[0129] [Figure 86] 10 illustrates an example effect of a pseudo bilateral filter on people counting, according to various embodiments of the present disclosure.

[0130] [Figure 87] 1 illustrates exemplary estimated probability density functions (PDFs) of four test subjects for person recognition, according to various embodiments of the present disclosure.

[0131] [Figure 88A] 1 illustrates an example confusion matrix of the disclosed system for laboratory people counting, in accordance with various embodiments of the present disclosure.

[0132] [Figure 88B] 10 shows an example confusion matrix for another system for laboratory people counting, according to various embodiments of the present disclosure.

[0133] [Figure 88C] 10 shows an example confusion matrix of the disclosed system without a pseudo-bilateral filter enabled for laboratory people counting, in accordance with various embodiments of the present disclosure.

[0134] [Figure 89A]1 illustrates an example confusion matrix of the disclosed system for counting people in a vehicle, in accordance with various embodiments of the present disclosure.

[0135] [Figure 89B] 10 illustrates an example confusion matrix for another system for counting people in a vehicle, according to various embodiments of the present disclosure.

[0136] [Figure 89C] 10 illustrates an example confusion matrix of the disclosed system without a pseudo-bilateral filter enabled for counting people in a car, in accordance with various embodiments of the present disclosure.

[0137] [Figure 90] 1 illustrates an example confusion matrix for person recognition in a smart home scenario, according to various embodiments of the present disclosure.

[0138] [Figure 91] 1 illustrates an experimental setup for a resolution study with three users sitting at different space intervals and different breathing frequency intervals, according to various embodiments of the present disclosure.

[0139] [Figure 92] 1 shows an exemplary set of test results according to various embodiments of the present disclosure.

[0140] [Figure 93] 1 illustrates an exemplary iterative dynamic programming algorithm, according to various embodiments of the present disclosure.

[0141] [Figure 94] 1 illustrates an exemplary trace concatenating algorithm in accordance with various embodiments of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0142] In the following detailed description, numerous specific details are set forth, by way of example, to provide a thorough understanding of the relevant disclosure. However, it should be apparent to those skilled in the art that the present disclosure may be practiced without such details. In other instances, well-known methods, procedures, components, and / or circuits have been described at a relatively high level, without detail, in order to avoid unnecessarily obscuring aspects of the present disclosure.

[0143] In the following detailed description, numerous specific details are set forth, by way of example, to provide a thorough understanding of the relevant disclosure. However, it should be apparent to those skilled in the art that the present disclosure may be practiced without such details. In other instances, well-known methods, procedures, components, and / or circuits have been described at a relatively high level, without detail, in order to avoid unnecessarily obscuring aspects of the present disclosure.

[0144] In one embodiment, the present disclosure 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 may be obtained (e.g., dynamically) using a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory. A time series of CI (TSCI) may be extracted from wireless signals (signals) transmitted between a type 1 heterogeneous wireless device (e.g., a wireless transmitter, TX) and a type 2 heterogeneous wireless device (e.g., a wireless receiver, RX) at a venue over the channel. The channel may be affected by the representations (e.g., motion, movement, representation, and / or position / pose / shape / representation changes) of objects at the venue. Objects and / or their motion characteristics and / or spatial-temporal information (STI, e.g., motion information) may be monitored based on the TSCI. Tasks may be performed based on the characteristics and / or STI. A presentation associated with the task may be generated in a user interface (UI) on the user's device. The TSCI may be a wireless signal stream. The TSCI or each CI may be pre-processed. The device may be a station (STA). The symbol "A / B" means "A and / or B" in this disclosure.

[0145] Expressions can include placement, placement of movable parts, location, position, orientation, identifiable location, area, spatial coordinates, presentation, state, expression, static representation, size, length, width, height, angle, scale, shape, curve, surface, area, volume, pose, posture, sign, body representation, dynamic representation, dynamic noun, movement, movement sequence, gesture, stretch, contraction, distortion, deformation, body representation (e.g., head, face, eyes, mouth, tongue, hair, voice, neck, limbs, arms, hands, legs, feet, muscles, moving parts), surface representation (e.g., shape, texture, material, color, electromagnetic (EM) properties, visual pattern, humidity, reflectivity, translucency, flexibility), material properties (e.g., living tissue, hair, fabric, metal, wood, leather, plastic, metallic, artificial material, solid, liquid, gas, temperature), movement, activity, behavior, change in expression, and / or any combination.

[0146] Wireless signals include transmit / receive signals, EM radiation, RF signals / transmissions, signals in licensed / unlicensed / ISM bands, band-limited signals, baseband signals, wireless / mobile / cellular communication signals, wireless / mobile / cellular network signals, mesh signals, optical signals / communications, downlink / uplink signals, unicast / multicast / broadcast signals, standard (e.g. WLAN, WWAN, WPAN, WBAN, international, national, industry, de facto, IEEE, IEEE 802, 802.11 / 15 / 16, WiFi, 802.11n / ac / ax / be, 3G / 4G / LTE / 5G / 6G / 7G / 8G, 3GPP, blue tooth, BLE, Zigbee, RFID, UWB, WiMax) compliant signals, protocol signals, standard frames, beacon / pilot / search / inquiry / acknowledge / handshake / synchronization signals, management / control / data signals, standardized wireless / cellular communication protocols, reference signals, source signals, motion probe / detection / The CI may include a sensing signal and / or a series of signals. The wireless signal may include line-of-sight (LOS) and / or non-LOS components (or paths / links). Each CI may be extracted / generated / calculated / detected at a layer of the Type 2 device (e.g., the PHY / MAC layer of the OSI model) and obtained by an application (e.g., software, firmware, driver, app, wireless monitoring software / system).

[0147] A wireless multipath channel can include: a communication channel, an analog frequency channel (e.g., analog carrier frequencies around 700 / 800 / 900 MHz, 1.8 / 1.8 / 2.4 / 3 / 5 / 6 / 27 / 60 GHz), a coded channel (e.g., CDMA), and / or a channel of a wireless network / system (e.g., WLAN, WiFi, mesh, LTE, 4G / 5G, Bluetooth, Zigbee, UWB, RFID, microwave). It can include two or more channels. The channels can be contiguous (e.g., adjacent / overlapping bands) or non-contiguous (e.g., non-overlapping WiFi channels, one at 2.4 GHz and one at 5 GHz).

[0148] The TSCI can be extracted from a wireless signal at a layer of a Type-2 device (e.g., a layer of the OSI reference model, a physical layer, a data link layer, a logical link control layer, a media access control (MAC) layer, a network layer, a transport layer, a session layer, a presentation layer, an application layer, a TCP / IP layer, an Internet layer, or a link layer). The TSCI may also be extracted from a derived signal (e.g., a baseband signal, a motion detection signal, or a motion sensing signal) derived from a wireless signal (e.g., an RF signal). It may be a (wireless) measurement detected by a communication protocol (e.g., a standardized protocol) using an existing mechanism (e.g., a wireless / cellular communication standard / network, 3G / LTE / 4G / 5G / 6G / 7G / 8G, WiFi, IEEE 802.11 / 15 / 16). The derived signal may include a packet having at least one of a preamble, a header, and a payload (e.g., for data / control / management in a wireless link / network). The TSCI may be extracted from a probe signal in a packet (e.g., a training sequence, STF, LTF, L-STF, L-LTF, L-SIG, HE-STF, HE-LTF, HE-SIG-A, HE-SIG-B, or CEF). The motion detection / sensing signal may be recognized / identified based on the probe signal. The packet may be a standard-compliance protocol frame, a management frame, a control frame, a data frame, a sounding frame, an excitation frame, an illumination frame, a null data frame, a beacon frame, a pilot frame, a probe frame, a request frame, a response frame, an association frame, a reassociation frame, a disassociation frame, an authentication frame, an action frame, a report frame, a poll frame, an announcement frame, an extension frame, an inquiry frame, an acknowledgement frame, an RTS frame, a CTS frame, a QoS frame, a CF-Poll frame, a CF-Ack frame, a block acknowledgement frame, a reference frame, a training frame, and / or a synchronization frame.

[0149] The packet may contain control data and / or motion detection probes. Data (e.g., Type 1 device IDs, parameters, characteristics, settings, control signals, commands, instructions, notifications, and broadcast-related information) may be obtained from the payload. The wireless signal may be transmitted by a Type 1 device and received by a Type 2 device. A database (e.g., in a local server, hub device, cloud server, or storage network) may be used to store TSCI, characteristics, STI, signatures, patterns, behaviors, trends, parameters, analysis, output responses, identification information, user information, device information, channel information, location (e.g., map, environment model, network, proximity device / network) information, task information, class / category information, presentation (e.g., UI) information, and / or other information.

[0150] A Type 1 / Type 2 device may include at least one of electronics, circuitry, transmitter (TX) / receiver (RX) / transceiver, RF interface, "origin satellite" / "tracker bot," unicast / multicast / broadcast device, wireless power device, power / destination device, wireless node, hub device, target device, motion detection device, sensor device, remote / wireless sensor device, wireless communication device, wireless enabled device, standard compliant device, and / or receiver. A Type 1 (or Type 2) device may be heterogeneous because, if multiple instances of a Type 1 (or Type 2) device exist, they may have different circuitry, enclosure, structure, purpose, auxiliary functionality, chips / ICs, processor, memory, software, firmware, network connectivity, antenna, brand, model, appearance, form, shape, color, material, and / or specifications. A Type 1 / Type 2 device may include an access point, a router, a mesh router, an Internet of Things (IoT) device, a wireless terminal, one or more radio / RF subsystems / radio interfaces (e.g., 2.4 GHz radio, 5 GHz radio, fronthaul radio, backhaul radio), a modem, an RF front end, an RF / radio chip or integrated circuit (IC).

[0151] At least one of Type 1 devices, Type 2 devices, links between them, objects, characteristics, STI, motion monitoring, and tasks may be associated with an identification (ID) such as a UUID. Type 1, Type 2, or another device may acquire, store, retrieve, access, preprocess, condition, process, analyze, monitor, or apply TSCI. Type 1 and Type 2 devices may communicate network traffic on other channels (e.g., Ethernet, HDMI, USB, Bluetooth, BLE, WiFi, LTE, other networks, wireless multipath channels) in parallel with wireless signals. Type 2 devices may passively observe, monitor, or receive wireless signals from Type 1 devices on wireless multipath channels without establishing a connection (e.g., association / authentication) with or requesting service from the Type 1 device.

[0152] A transmitter (i.e., Type 1 device) can function (act as) a receiver (i.e., Type 2 device) temporarily, sporadically, continuously, repeatedly, interchangeably, alternately, simultaneously, in parallel, and / or simultaneously, and vice versa. A device can function as a Type 1 device (transmitter) and / or a Type 2 device (receiver) temporarily, sporadically, continuously, repeatedly, simultaneously, in parallel, and / or simultaneously. There may be multiple wireless nodes, each of which is a Type 1 (TX) and / or a Type 2 (RX) device. TSCI may be obtained for each two nodes when exchanging / communicating wireless signals. An object's characteristics and / or STI may be monitored individually based on the TSCI or jointly based on two or more (e.g., all) TSCIs. An object's movement may be monitored actively (in that its Type 1 device, Type 2 device, or both are wearable / associated with the object) and / or passively (in that both the Type 1 device and the Type 2 device 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. The object (e.g., a user, an automated guided vehicle, or an AGV) may not need to carry / place any wearable / fixture (i.e., Type 1 and Type 2 devices are not wearable / attached equipment that the object needs to carry to perform a task). It can be active because the object may be associated with either a Type 1 or Type 2 device. The object can carry (or place) a wearable / attachment (e.g., a Type 1 device, a Type 2 device, or equipment communicatively coupled to either a Type 1 or Type 2 device).

[0153] The presentation may be visual, audio, image, video, animation, graphic presentation, text, etc. The computation of the task may be performed by a processor (or logic unit) of the Type 1 device, a processor (or logic unit) of the IC of the Type 1 device, a processor (or logic unit) of the Type 2 device, a processor of the IC of the Type 2 device, a local server, a cloud server, a data analysis subsystem, a signal analysis subsystem, and / or another processor. This work may be performed with or without a radio fingerprint or baseline (e.g., collection, processing, processing, transmission, and / or training phase / previous survey / latest survey / initial radio survey, passive indications), training, profile, trained profile, static profile, static profile, survey, initial radio survey, initial setup, installation, retraining, update, and reset).

[0154] A Type 1 device (TX device) may include at least one heterogeneous radio transmitter. A Type 2 device (RX device) may include at least one heterogeneous radio receiver. A Type 1 device and a Type 2 device may be co-located. A Type 1 device and a Type 2 device may be the same device. Any device may have a data processing unit / apparatus, a computing unit / system, a network unit / system, a processor (e.g., a logic unit), a memory communicatively coupled to the processor, and a set of instructions stored in the memory to be executed by the processor. Some processors, memories, and sets of instructions may cooperate. There may be multiple Type 1 devices interacting (e.g., communicating, exchanging signals / control / notifications / other data) with the same Type 2 device (or multiple Type 2 devices) and / or there may be multiple Type 2 devices interacting with the same Type 1 device. Multiple Type 1 / Type 2 devices may be synchronous and / or asynchronous, have the same / different window widths / sizes and / or time shifts, have the same / different synchronization start times, synchronization end times, etc. The wireless signals transmitted by multiple Type 1 devices may be sporadic, intermittent, continuous, repetitive, synchronous, simultaneous, concurrent, and / or simultaneous. Multiple Type 1 / Type 2 devices may operate independently and / or cooperatively. Type 1 and / or Type 2 devices may have / include heterogeneous hardware circuits (e.g., heterogeneous chips or ICs capable of generating / receiving wireless signals, extracting CI from received signals, or making CI available). They may be communicatively coupled to the same or different servers (e.g., cloud servers, edge servers, local servers, hub devices).

[0155] The operation of one device can be based on the operation, state, internal state, storage, processor, memory output, physical location, computational resources, or network of another device. Different devices may communicate directly and / or through another device / server / hub device / cloud server. A device can be associated with one or more users and have associated settings. Settings may be selected once, pre-programmed, and / or changed (e.g., adjusted, changed, modified) / varied over time. There may be additional steps in a method. Method steps and / or additional steps may be performed in the order shown or in a different order. Any steps may be performed in parallel, iteratively, or otherwise iteratively or otherwise. A user may be a human, adult, elderly adult, male, female, infant, child, baby, pet, animal, living being, machine, computer module / software, etc.

[0156] For one or more Type 1 devices interacting with one or more Type 2 devices, any processing (e.g., time domain, frequency domain) can be different for different devices. Processing can be based on location, orientation, direction, role, user-related characteristics, settings, configuration, available resources, available bandwidth, network connection, hardware, software, processor, co-processor, memory, battery life, available power, antenna, antenna type, antenna directional / unidirectional characteristics, power settings, and / or other parameters / characteristics of the device.

[0157] The wireless receiver (e.g., a Type 2 device) may receive a signal and / or another signal from the wireless transmitter (e.g., a Type 1 device). The wireless receiver may receive another signal from another wireless transmitter (e.g., a second Type 1 device). The wireless transmitter may transmit a signal and / or another signal to another wireless receiver (e.g., a second Type 2 device). The wireless transmitter, the wireless receiver, another wireless receiver, and / or another wireless transmitter may move with the object and / or another object. The other object may be tracked.

[0158] A Type 1 and / or Type 2 device may be capable of wirelessly coupling with at least two Type 2 and / or Type 1 devices. The Type 1 device may be triggered / controlled to switch / establish a wireless coupling (e.g., association, authentication) from the Type 2 device to a second Type 2 device at another location in the location. Similarly, the Type 2 device may be triggered / controlled to switch / establish a wireless coupling from the Type 1 device to a second Type 1 device at yet another location in the location. The switching may be controlled by a server (or hub device), a processor, the Type 1 device, the Type 2 device, and / or another device. The radios used before and after the switching may be different. A second wireless signal (second signal) may be transmitted through the channel between the Type 1 device and the second Type 2 device (or between the Type 2 device and the second Type 1 device). A second TSCI of the channel may be obtained from the second signal. The second signal may be the first signal. A characteristic, STI, and / or another quantity of the object may be monitored based on the second TSCI. The Type 1 and Type 2 devices may be the same. The characteristics, STIs, and / or other quantities with different timestamps may form a waveform. The waveform may be displayed in a presentation.

[0159] The wireless signal and / or another signal may have data embedded therein. The wireless signal may be a series of probe signals (e.g., repeated transmission of a probe signal, reuse of one or more probe signals). The probe signal may vary / change over time. The probe signal may be a standard-compliant signal, a protocol signal, a standardized wireless protocol signal, a control signal, a data signal, a wireless communication network signal, a cellular network signal, a WiFi signal, an LTE / 5G / 6G / 7G signal, a reference signal, a beacon signal, a motion detection signal, and / or a motion sensing signal. The probe signal may be formatted according to a wireless network standard (e.g., WiFi), a cellular network standard (e.g., LTE / 5G / 6G), or another standard. The probe signal may include a packet having a header and a payload. The probe signal may have data embedded therein. The payload may include data. The probe signal may replace a data signal. The probe signal may be embedded in a data signal. The wireless receiver, the wireless transmitter, the other wireless receiver, and / or the other wireless transmitter may be associated with at least one processor, a memory communicatively coupled to the respective processor, and / or a respective set of instructions stored in the memory that, when executed, cause the processor to perform any and / or all steps necessary to determine the object's STI (e.g., motion information), initial STI, initial time, direction, instantaneous position, instantaneous angle, and / or velocity. The processor, memory, and / or set of instructions may be associated with the Type 1 device, at least one Type 2 device, the object, a device associated with the object, another device associated with the location, a cloud server, a hub device, and / or another server.

[0160] A Type 1 device can broadcast a signal to at least one Type 2 device(s) over a location channel. The signal is transmitted without the Type 1 device establishing a wireless 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 can transmit to a specific media access control (MAC) address common to multiple Type 2 devices. Each Type 2 device can tune its MAC address to a specific MAC address. The specific MAC address can be associated with a location. The association can be recorded in an association table in an association server (e.g., a hub device). A location can be identified by a Type 1 device, a Type 2 device, and / or another device based on the specific MAC address, a series of probe signals, and / or at least one TSCI extracted from the probe signals. For example, a Type 2 device can be moved to a new location (e.g., from another location). A Type 1 device can be reconfigured at a location such that Type 1 and Type 2 devices are unaware of each other. During setup, the Type 1 device may be instructed / guided / caused / controlled (e.g., using a dummy receiver, using hardware pin configuration / connection, using saved configuration, using local configuration, using remote configuration, using downloaded configuration, using a hub device, or using a server) to send a series of probe signals to specific MAC addresses. Upon powering up, the Type 2 device may scan for probe signals according to a table (e.g., stored in a designated source, server, hub device, cloud server) of MAC addresses that can be used to broadcast in different locations (e.g., different MAC addresses used for different locations such as a house, office, enclosure, floor, multi-story building, store, airport, mall, stadium, hall, station, subway, lot, region, area, district, province, city, country, continent, etc.).When a Type 2 device detects a probe signal sent to a specific MAC address, the Type 2 device can use a table to identify a location based on the MAC address. The location of the Type 2 device can be calculated based on the specific MAC address, the sequence of probe signals, and / or at least one TSCI obtained by the Type 2 device from the probe signals. The calculation can be performed by the Type 2 device. The specific MAC address can be changed (e.g., adjusted, modified, or revised) over time. It can be changed according to a timetable, rule, policy, mode, condition, situation, and / or change. The specific MAC address can be selected based on MAC address availability, a preselected list, collision patterns, traffic patterns, data traffic between the Type 1 device and other devices, available bandwidth, random selection, and / or a MAC address switching plan. The specific MAC address can be the MAC address of a second wireless device (e.g., a dummy receiver or a receiver acting as a dummy receiver).

[0161] The Type 1 device may transmit a probe signal on a channel selected from a set of channels. At least one CI of the selected channel may be acquired by each Type 2 device from the probe signal transmitted on the selected channel. The selected channel may be changed (e.g., adjusted, changed, modified) over time. The change may be according to a timetable, rule, policy, mode, condition, situation, and / or change. The selected channel may be selected based on channel availability, random selection, a preselected list, co-channel interference, inter-channel interference, channel traffic patterns, data traffic between the Type 1 device and another device, effective bandwidth associated with the channel, security criteria, channel switching plan, criteria, quality criteria, signal quality conditions, and / or considerations.

[0162] The specific MAC address and / or selected channel information can be communicated between a Type 1 device and a server (e.g., a hub device) over a network. The specific MAC address and / or selected channel information can also be communicated between a Type 2 device and a server (e.g., a hub device) over another network. A Type 2 device can communicate the specific MAC address and / or selected channel information to another Type 2 device (e.g., via a mesh network, Bluetooth, WiFi, NFC, ZigBee, etc.). The specific MAC address and / or selected channel can be selected by a server (e.g., a hub device). The specific MAC address and / or selected channel can be signaled in an announcement channel by a Type 1 device, a Type 2 device, and / or a server (e.g., a hub device). Any information can be preprocessed before being communicated.

[0163] A wireless connection (e.g., association, authentication) between a Type 1 device and another wireless device can be established (e.g., using a signal handshake). The Type 1 device can send a first handshake signal (e.g., a sounding frame, a probe signal, a request to send RTS) to the other device. The other device can respond by sending a second handshake signal (e.g., a command or a clear to send CTS) to the Type 1 device, triggering the Type 1 device to send a signal (e.g., a series of probe signals) in a broadcast manner to multiple Type 2 devices without establishing a connection with any Type 2 devices. The second handshake signal can be a response or acknowledgment (e.g., an ACK) to the first handshake signal. The second handshake signal can include data having location and / or Type 1 device information. The other device can be a dummy device with a purpose (e.g., primary purpose, secondary purpose) to establish a wireless connection with the Type 1 device, receive the first signal, and / or send the second signal. The other device can be physically attached to the Type 1 device.

[0164] In another example, another device can send a third handshake signal to a Type-1 device that triggers the Type-1 device to broadcast a signal (e.g., a series of probe signals) to multiple Type-2 devices without establishing a connection (e.g., association, authentication) with any of the Type-2 devices. The Type-1 device can respond to the third special signal by sending a fourth handshake signal to the other device. Another device can be used to trigger multiple Type-1 devices to broadcast. The triggering may be sequential, partially sequential, partially parallel, or fully parallel. The other device may have multiple radio circuits to trigger multiple transmitters in parallel. Parallel triggering can also be achieved by using at least one additional device to perform a trigger in parallel with another device (as the other device does). The other device cannot communicate (or suspend communication) with the Type-1 device after establishing a connection with it. The suspended communication may resume. The other device can enter an inactive mode, dormant mode, sleep mode, standby mode, low power mode, OFF mode, and / or power-down mode after establishing a connection with the Type-1 device. The other device may have a specific MAC address such that the Type 1 device sends signals to the specific MAC address. The Type 1 device and / or the other device may be controlled and / or coordinated by a first processor associated with the Type 1 device, a second processor associated with the other device, a third processor associated with the specified source, and / or a fourth processor associated with the other device. The first and second processors may coordinate with each other.

[0165] A first series of probe signals may be transmitted to at least one first Type 2 device by a first antenna of the Type 1 device through a first channel at a first location. A second series of probe signals may be transmitted to at least one second Type 2 device by a second antenna of the Type 1 device through a second channel at a second location. The first series of probe signals and the second series of probe signals may be different or not different. The at least one first Type 2 device may be different or not different from the at least one second Type 2 device. The first and / or second series 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 be the same or different. The two locations may have different sizes, shapes, and multipath characteristics. The first and second locations may overlap. The immediate areas in the vicinity of 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, or both may be WiFi, but the first may be 2.4GHz WiFi and the second may be 5GHz WiFi, or both may be 2.4GHz WiFi, but with different channel numbers, SSID names, and / or WiFi settings.

[0166] Each Type 2 device can obtain at least one TSCI from each series of probe signals, where the CI is each channel between the Type 2 device and the Type 1 device. Some first Type 2 device(s) and some second Type 2 device(s) can be the same. The first and second series of probe signals can be synchronous / asynchronous. The probe signals can be transmitted with data or replaced with data signals. The first and second antennas can be the same. The first series of probe signals can be transmitted at a first rate (e.g., 30 Hz). The second series of probe signals can be transmitted at a second rate (e.g., 200 Hz). The first and second rates can be the same / different. The first and / or second rates can be changed (e.g., adjusted, varied, modified) over time. The change can be according to a schedule, rule, policy, mode, condition, situation, and / or change. Any rate can be changed (e.g., adjusted, varied, modified) over time. The first and / or second series of probe signals may be transmitted to a first MAC address and / or a second MAC address, respectively. The two MAC addresses may be the same or different. The first series of probe signals may be transmitted in a first channel. The second series of probe signals may be transmitted in a 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 change may be according to a timetable, rule, policy, mode, state, condition, and / or change.

[0167] A Type 1 device and another device may be controlled and / or coordinated, physically attached to a common device, or of / within a common device. They may be controlled by / connected to a common data processor or connected to a common bus interconnect / network / LAN / Bluetooth network / NFC network / BLE / 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, identity (ID), identifier, home, residence, physical address, location, geographic coordinates, IP subnet, SSID, home device, office device, and / or manufacturing device. Each Type 1 device may be a signal source for a respective set of Type 2 devices (i.e., it sends a respective signal (e.g., a respective series of probe signals) to a respective set of Type 2 devices). Each Type 2 device selects a Type 1 device from among all Type 1 devices as its signal source. Each Type 2 device may select a Type 1 device asynchronously. At least one TSCI may be obtained by each Type 2 device from each series of probe signals from the Type 1 device, and the CI is a channel between the Type 2 device and the Type 1 device. Each Type 2 device selects a Type 1 device as its signal source from all Type 1 devices based on the identity (ID) or Type 1 / Type 2 device identifier, the task to be performed, past signal sources, history (e.g., of past signal sources, the Type 1 device, another Type 1 device, each Type 2 receiver, and / or another Type 2 receiver), a threshold for switching signal sources, and / or user information, account, access information, parameters, characteristics, and / or signal strength (e.g., associated with the Type 1 device and / or each Type 2 receiver). Initially, the Type 1 device may be the signal source of the initial set of each Type 2 device (i.e., the Type 1 device sends each signal (series of probe signals) to each initial set of Type 2 devices).Each initial respective Type 2 device selects a Type 1 device from among all Type 1 devices as its signal source.

[0168] A signal source of a particular Type 2 device (a Type 1 device) may be changed (e.g., adjusted, altered, modified) if: (1) the time interval between two adjacent probe signals (e.g., between the current probe signal and the immediately previous probe signal, or between the next probe signal and the current probe signal) received from the current signal source of the Type 2 device exceeds a first threshold; (2) the signal strength associated with the current signal source of the Type 2 device is less than a second threshold; (3) the processed signal strength associated with the current signal source of the Type 2 device is less than a third threshold, and the signal strength is processed with a low-pass filter, a band-pass filter, a median filter, a moving average filter, a weighted average filter, a linear filter, and / or a nonlinear filter; and / or (4) the signal strength (or processed signal strength) associated with the current signal source of the Type 2 device is below a fourth threshold for a significant percentage (e.g., 70%, 80%, 90%) of the recent time window. The percentage can exceed a fifth threshold. The first, second, third, fourth and / or fifth thresholds may be time-varying.

[0169] Condition (1) can occur when a Type 1 device and a Type 2 device gradually move farther 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) through (4) can occur when the two devices move far enough away from each other that the signal strength becomes very weak.

[0170] The signal source of a Type 2 device may remain unchanged if another Type 1 device has a signal strength weaker than the current signal source by a factor (e.g., 1, 1.1, 1.2, or 1.5). If the signal source is changed (adjusted, modified, modified, etc.), the new signal source may become effective in the near future (e.g., each next time). The new signal source may be the Type 1 device with the strongest signal strength and / or processed signal strength. The current signal source and the new signal source may be the same or different.

[0171] A list of available Type 1 devices may be initialized and maintained by each Type 2 device. The list may be updated by examining signal strengths and / or processed signal strengths associated with each set of Type 1 devices. A Type 2 device may select between a first series of probe signals from a first Type 1 device and a second series of probe signals from a second Type 1 device based on their respective probe signal rates, MAC addresses, channels, characteristics / properties / statuses, tasks to be performed by the Type 2 device, the first and second series of signal strengths, and / or other considerations.

[0172] The series of probe signals may be transmitted at a constant rate (e.g., 100 Hz). The series of probe signals may be scheduled at regular intervals (e.g., 0.01 seconds for 100 Hz), although each probe signal may experience small time perturbations, perhaps due to timing requirements, timing control, network control, handshaking, message passing, collision avoidance, carrier sensing, congestion, resource availability, and / or other considerations. The rate may be changed (e.g., adjusted, altered, modified). The change may be according to a schedule (e.g., hourly), a rule, a policy, a mode, a condition, and / or a change (e.g., whenever an event occurs). For example, the rate may be normally 100 Hz but changed to 1000 Hz in demanding situations and to 1 Hz in low-power / standby situations. The probe signals may be transmitted in bursts.

[0173] The probe signal rate may vary based on the task performed by the Type 1 or Type 2 device (e.g., a task may normally require 100 Hz, temporarily 1000 Hz for 20 seconds). In one example, transmitters (Type 1 devices), receivers (Type 2 devices), and associated tasks may be adaptively (and / or dynamically) associated with classes (e.g., classes that are low priority, high priority, emergency, critical, normal, privileged, non-subscribed, subscribed, paid, and / or unpaid). The (transmitter's) rate may be adjusted for some classes (e.g., high priority classes). If the needs of that class change, the rate can be changed (e.g., adjusted, modified, modified). If the receiver has critically low power, the rate may be reduced to reduce the receiver's power consumption for responding 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.

[0174] The rate may be changed by (or based on) the following: 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 needs of the Type 2 device and / or the tasks performed by the Type 2 device, and 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 immediately change the rate. The server may detect a developing condition and gradually adjust the rate. Characteristics and / or STIs (e.g., movement information) may be monitored individually based on TSCIs associated with a particular Type 1 device and a particular Type 2 device, and / or jointly based on any TSCIs associated with a particular Type 1 device and any Type 2 device, and / or jointly based on any TSCIs associated with a particular Type 2 device and any Type 1 device, and / or globally based on any TSCIs associated with any Type 1 device and any Type 2 device. Any collaborative monitoring may relate to: a user, a user account, a profile, a home, a map of the location, an environmental model of the location, and / or a user history.

[0175] A first channel between a Type 1 device and a Type 2 device may be different 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, modulations, wireless standards, coding, encryption, payload characteristics, networks, network IDs, SSIDs, network characteristics, network settings, and / or network parameters. The two channels may be associated with different types of wireless systems (e.g., two of the following: 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, and radar-like systems). For example, one channel is WiFi and the other is LTE. The two channels may be associated with similar types of wireless systems, but different networks. For example, a first channel may be associated with a WiFi network named "Pizza and Pizza" in the 2.4 GHz band with a bandwidth of 20 MHz, while a second channel may be associated with a WiFi network with an SSID of "StarBud Hotspot" in the 5 GHz band with a bandwidth of 40 MHz. The two channels may be different channels within the same network (e.g., the "StarBud Hotspot" network).

[0176] In one embodiment, the wireless monitoring system can include training a classifier for multiple events at a location based on training TSCIs associated with the multiple events. The CIs or TSCIs associated with an event can consider / include wireless samples / characteristics / fingerprints associated with the event (and / or the location, environment, object, object movement, state / emotional state / mental state / condition / stage / gesture / gait / action / movement / activity / daily activity / history / event of the object, etc.). For each of multiple known events occurring at the location at a respective training (e.g., survey, wireless survey, initial wireless survey) time associated with the known event, a respective training wireless signal (e.g., a respective series of training probe signals) can be transmitted to at least one first Type-2 heterogeneous wireless device through a wireless multipath channel at the location at the respective training time by an antenna of the first Type-1 heterogeneous wireless device using a processor, memory, and set of instructions of the first Type-1 device.

[0177] At least one respective time series of training CIs (training TSCIs) may be asynchronously acquired by each of the at least one first Type 2 device from the (respective) training signal. The CIs may be CIs of a channel between the first Type 2 device and the first Type 1 device at a training time associated with a known event. The at least one training TSCI may be preprocessed. The training may be a wireless survey (e.g., during installation of the Type 1 device and / or the Type 2 device).

[0178] For a current event occurring within the location in a current time period, a current wireless signal (e.g., a series of current probe signals) may be transmitted to at least one second Type 2 heterogeneous wireless device through a channel of the location in a current time period related to the current event by an antenna of the second Type 1 heterogeneous wireless device using a processor, memory, and set of instructions of the second Type 1 device. At least one time series of current CIs (current TSCIs) may be asynchronously obtained by each of the at least one second Type 2 device from the current signals (e.g., the series of current probe signals). The CIs may be CIs of a channel between the second Type 2 device and the second Type 1 device in a current time period related to the current event. The at least one current TSCI may be preprocessed.

[0179] A classifier may be applied by at least one second Type 2 device to classify at least one current TSCI obtained from the series of current probe signals, to classify at least a portion of a particular current TSCI, and / or to classify a combination of at least a portion of a particular current TSCI with another portion of another TSCI. The classifier may divide the TSCIs (or features / STIs or other analysis or output responses) into clusters and associate the clusters with particular events / objects / subjects / locations / movements / activities. Labels / tags may be generated for the clusters. The clusters may be stored and searched. A classifier may be applied to associate the current TSCI (or perhaps a characteristic / STI or other analysis / output response related to the current event) with: a cluster, a known / specific event, a class / category / group / grouping / list / cluster, a set of known events / subject / location / movement / activity, an unknown event, a class / category / group / grouping / list / cluster, a set of unknown events / subject / location / movement / activity, and / or another event / subject / location / movement / activity / class / category / group / grouping / list / cluster. Each TSCI may include at least one CI, each associated with a respective timestamp. Two TSCIs associated with two Type 2 devices differ by different start times, durations, stop times, amount of CIs, sampling frequencies, and sampling periods. The CIs may have different characteristics. The first and second Type 1 devices may be at the same location. They may be the same device. At least one second Type 2 device (or their location) may be a replacement for at least one first Type 2 device (or their location). A particular second Type 2 device and a particular first Type 2 device may be the same device. The subset of first Type 2 devices and the subset of second Type 2 devices may be the same.The at least one second Type 2 device and / or the subset of at least one second Type 2 device may be a subset of the at least one first Type 2 device. The at least one first Type 2 device and / or the subset of at least one first Type 2 device may be a replacement for the subset of the at least one second Type 2 device. The at least one second Type 2 device and / or the subset of at least one second Type 2 device may be a replacement for the subset of the at least one first Type 2 device. The at least one second Type 2 device and / or the subset of at least one second Type 2 device may be in the same respective locations as the subset of the at least one first Type 2 device. The at least one first Type 2 device and / or the subset of at least one first Type 2 device may be in the same respective locations as the subset of the at least one second Type 2 device.

[0180] The antenna of the Type 1 device and the antenna of the second Type 1 device may be in the same location. The antenna of the at least one second Type 2 device and / or the antenna of the subset of at least one second Type 2 device may be in the same respective location as each antenna of the subset of at least one first Type 2 device. The antenna of the at least one first Type 2 device and / or the antenna of the subset of at least one first Type 2 device may be in the same respective location as each antenna of the subset of at least one second Type 2 device.

[0181] A first section of a first duration of a first TSCI and a second section of a second duration of a second TSCI may be aligned. A map between items in the first section and items in the second section may be calculated. 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 the 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 the second operation. The first operation and / or the second operation may include subsampling, resampling, interpolation, filtering, transformation, feature extraction, preprocessing, and / or other operations.

[0182] A first item in a first section may be mapped to a second item in a second section. A first item in a first section may also be mapped to another item in a second section. Another item in a first section may also be mapped to a second item in a second section. The mapping may be one-to-one, one-to-many, many-to-one, or many-to-many. At least one function of at least one of the first item in a first section of a first TSCI, another item in a first TSCI, a timestamp of the first item, a time difference of the first item, a time difference of the first item, an adjacent timestamp of the first item, an adjacent timestamp of the first item, another timestamp related to the first item, a second item in a second section of a second TSCI, another item in a second TSCI, a timestamp of the second item, a time difference of the second item, a time difference of the second item, an adjacent timestamp of the second item, and another timestamp related to the second item may satisfy at least one constraint.

[0183] One constraint may be that the difference between the timestamp of the first item and the timestamp of the second item may be bounded upper by an adaptive (and / or dynamically adjusted) upper threshold and lower by an adaptive lower threshold.

[0184] The first section may be the entire first TSCI. The second section may be the entire second TSCI. The first time duration may be equal to the second time duration. The time duration of a section of a TSCI may be determined adaptively (and / or dynamically). A provisional section of the TSCI may be calculated. A start time and an end time of a section (e.g., provisional section, section) may be determined. A section may be determined by removing the start and end portions of the provisional section. The start portion of the provisional section may be determined as follows: Iteratively, items in the provisional section with increasing timestamps may be considered as the current item, one item at a time.

[0185] At each iteration, at least one activity measure / index may be calculated and / or considered. The 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 with a timestamp not greater than the current timestamp, and / or a future item in the provisional section with a timestamp not less than the current timestamp. If at least one criterion (e.g., quality criterion, signal quality condition) associated with the at least one activity measure is met, the current item may be added to the beginning of the provisional section.

[0186] The at least one criterion associated with the activity measure may include at least one of the following: (a) the activity measure is less than an adaptive (dynamically adjusted) upper threshold, (b) the activity measure is greater than an adaptive lower threshold, (c) the activity measure is less than an adaptive upper threshold consecutively for at least a predetermined amount of consecutive timestamps, (d) the activity measure is greater than an adaptive lower threshold consecutively for at least another predetermined amount of consecutive timestamps, (e) the activity measure is less than an adaptive upper threshold consecutively for at least a predetermined percentage of a predetermined amount of consecutive timestamps, (f) the activity measure is greater than an adaptive lower threshold consecutively for at least another predetermined percentage of another predetermined amount of consecutive timestamps, (g) another activity measure associated with another timestamp related to the current timestamp is less than another adaptive upper threshold and greater than another adaptive lower threshold, or (h) at least one activity measure associated with at least one respective timestamp related to the current timestamp is less than a respective upper threshold and greater than a respective lower threshold. (i) the percentage of timestamps associated with the activity measures in a set of timestamps associated with the current timestamp that are less than their respective upper thresholds and greater than their respective lower thresholds exceeds a threshold; and (j) another criterion (e.g., quality criterion, signal quality condition).

[0187] The activity measure / index associated with the item at time T1 may include 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 fixed 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 (e.g., adjusted, varied, modified) over time; T2 may be updated periodically; T2 may be the start 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 other items.

[0188] At least one of the first function, the second function, the third function, and / or the fourth function may be a function (e.g., F(X, Y, ...)) with at least two arguments X and Y. The two arguments may be scalars. The function (e.g., F) may be at least one of 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 may be certain predetermined quantities. For example, the function may be simply abs(XY), or (XY)^2, (XY)^4. The function may be a robust function. For example, the function is (XY)^2 when abs(XY) is less than a 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. Also, when abs(Xy) is greater than T, the function may be bounded by a slowly increasing function so that outliers cannot significantly affect the results. Another example of this function may be (abs(X / Y)-a) (where a=1). In this way, when X=Y (i.e., no change or activity), the function yields a value of 0. When X is greater than Y, (X / Y) is greater than 1 (assuming 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, both arguments X and Y may be n-tuples, with X = (x_1, x_2, ..., x_n) and Y = (y_1, y_2, ..., y_n). The function may be at least one of x_i, y_i, (x_i - y_i), (y_ix_i), abs(x_i - y_i), x_i^a, y_i^b, abs(x_i^a - y_i^b), (x_i - y_i)^a, (x_i / y_i), (x_i + a) / (y_i + b), (x_i^a / y_i^b), and ((x_i / y_i)^ab), where i is a component index of the n-tuples 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 sum of another function per component of at least one of x_i, y_i, (x_i-y_i), (y_ix_i), abs(x_i-y_i), x_i^a, y_i^b, abs(x_i^a-y_i^b), (x_i-y_i)^a, (x_i / y_i), (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. For example, the function may be in the form sum_{i=1}^n(abs(x_i / y_i)-1) / n, or sum_{i=1}^nw_i*(abs(x_i / y_i)-1), where w_i is the weight of component i.

[0189] The map may be computed using dynamic time warping (DTW). The DTW may include constraints on at least one of the map, the items of the first TSCI, the items of the second TSCI, the first duration, the second duration, the first section, and / or the second section. Suppose the i^{th} domain item is mapped to the j^{th} range item in the map. The constraints may be on the allowable combinations of i and j (constraints on the relationship between i and j). A 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 may be computed.

[0190] The first section and the second section may be aligned such that a map including a plurality of links may be established between a first item of the first TSCI and a second item of the second TSCI. Each link may associate one of the first items with a first timestamp and one of the second items with a second timestamp. A mismatch cost between the aligned first section and the aligned second section may be calculated. The mismatch cost may include a function of a cost for an item between the first item and the second item associated by a particular link of the map and a link-wise cost for a link associated with the particular link of the map.

[0191] The aligned first section and the aligned second section may be represented as a first vector and a second vector, respectively, of the same vector length. The mismatch cost may include at least one of a dot product, a dot product-like measure, a correlation-based measure, a correlation indicator, a covariance-based measure, a discrimination score, a distance, a Euclidean distance, an absolute distance, an Lk distance (e.g., L1, L2,...), a weighted distance, a distance-like measure, and / or another similarity value between the first vector and the second vector. The mismatch cost may be normalized by the respective vector lengths.

[0192] A parameter derived from the mismatch cost between a first section of a first duration of a first TSCI and a second section of a second duration of a second TSCI may be modeled with a statistical distribution. At least one of a scale parameter, a position parameter, and / or another parameter of the statistical distribution may be estimated. 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. A first sliding window may be applied to the first TSCI, and a corresponding second sliding window may be applied to the second TSCI. The first sliding window of the first TSCI and the corresponding second sliding window of the second TSCI may be aligned.

[0193] A 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, and the current event may be associated with at least one of a known event, an unknown event, and / or another event based on the mismatch cost.

[0194] The classifier may be applied to at least one of each first section of a first duration of the first TSCI and / or each second section of a second duration of the second TSCI to obtain at least one provisional classification result, each provisional classification result being associated with a respective first section and a respective second section.

[0195] The current event can be associated with at least one of a known event, an unknown event, a class / category / group / grouping / list / set of unknown events, and / or another event based on the mismatch cost. The current event can be associated with at least one of a known event, an unknown event, and / or another event based on the most numerous provisional classification results in the multiple sections of the first TSCI and the multiple sections of the corresponding second TSCI. For example, the current event can be associated with a specific known event if the mismatch cost points to the specific known event N consecutive times (e.g., N=10). In another example, the current event can be associated with a specific known event if the percentage of mismatch costs within the immediately preceding N consecutive N times that point to the specific known event exceeds a predetermined threshold (e.g., >80%). In another example, the current event can be associated with the known event that achieves the smallest mismatch cost the most times in time. The current event can be associated with the known event that achieves the smallest overall mismatch cost, which is a weighted average of at least one mismatch cost associated with at least one first section. The current event may be associated with a particular known event that achieves a minimum of another overall cost. The current event may be associated with an "unknown event" if none of the known events achieves a mismatch cost lower than a first threshold T1 in a sufficient percentage of at least one first section. The current event may also be associated with an "unknown event" if none of the events achieves an overall mismatch cost lower than a second threshold T2. The current event may be associated with at least one of a known event, an unknown event, and / or another event based on the mismatch cost and additional mismatch cost associated with at least one additional section of the first TSCI and at least one additional section of the second TSCI.The known events may include at least one of a door closed event, a door open event, a window closed event, a window open event, a multi-state event, an on state event, an off state event, an intermediate state event, a continuous state event, a discrete state event, a person present event, a person absent event, a life present event, and / or a life absent event.

[0196] A projection for each CI may be trained using a dimensionality reduction method based on the training TSCI. The dimensionality reduction method may include at least one of principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher's linear discriminant, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or another method. The projection may be applied to at least one of the training TSCI associated with at least one event and / or the current TSCI for a classifier. The classifier for at least one event may be trained based on the projection associated with the at least one event and the associated training TSCI. The at least one current TSCI may be classified / categorized based on the projection and the current TSCI. The projection may be retrained using at least one of the dimensionality reduction method and another dimensionality reduction method based on at least one of the training TSCI, the at least one current TSCI before retraining the projection, and / or additional training TSCI. Other dimensionality reduction methods may include at least one of principal component analysis (PCA), PCA with different kernels, independent component analysis (ICA), Fisher's linear discriminant, vector quantization, supervised learning, unsupervised learning, self-organizing maps, autoencoders, neural networks, deep neural networks, and / or other methods. The at least one event classifier may be retrained based on at least one of the retrained projections, the training TSCI associated with the at least one event, and / or the at least one current TSCI. The at least one current TSCI may be classified based on the retrained projections, the retrained classifier, and / or the current TSCI.

[0197] Each CI may include a vector of complex values. Each complex value may be preprocessed to provide a magnitude of the complex value. Each CI may be preprocessed to provide a vector of non-negative real numbers containing the magnitude of the corresponding complex value. Each training TSCI may be weighted in training the projection. The projection may include multiple projection components. The projection may include at least one most significant projection component. The projection may include at least one projected component that may be useful to the classifier.

[0198] Channel information (CI) includes signal strength, signal amplitude, signal phase, spectral power measures, modem parameters (e.g., used in connection with modulation / demodulation in digital communication systems such as WiFi, 4G / LTE, etc.), dynamic beamforming information, transfer function components, radio state (e.g., used in digital communication systems to decode digital data, baseband processing state, RF processing state, etc.), measurable variables, sensing data, layer coarse-grained / fine-grained information (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 correction settings, effects of the environment (e.g., location) on the radio signal during propagation, input signal (radio signal transmitted by Type 1 device) and output signal (Type 2) The CI may be associated with or include a transformation into a radio signal (radio signal received by the device), steady state behavior of the environment, a condition profile, radio channel measurements, a received signal strength indicator (RSSI), channel state information (CSI), a channel impulse response (CFR), a channel frequency response (CFR), characteristics of frequency components (e.g., subcarriers) in the bandwidth, channel characteristics, channel response, a timestamp, auxiliary information, data, metadata, user data, account data, access data, security data, session data, status data, supervision data, home data, identification (ID), identifiers, device data, network data, proximity 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 a time of arrival. The CSI can equalize / restore / minimize / reduce multipath channel effects (transmission channel) and demodulate signals similar to those transmitted by the transmitter through the multipath channel. The CI may be associated with information related to a frequency band, a frequency signature, a frequency phase, a frequency amplitude, a frequency trend, a frequency characteristic, a frequency-like characteristic, a time domain element, a frequency domain element, a time-frequency domain element, an orthogonal decomposition characteristic, and / or a non-orthogonal decomposition characteristic of a signal passing through a channel. The TSCI may be a stream of wireless signals (e.g., CIs).

[0199] The CI may be pre-processed, processed, post-processed, stored (e.g., in a local memory, portable / mobile memory, removable memory, storage network, cloud memory, in a volatile manner, in a non-volatile manner), retrieved, transmitted, and / or received. One or more modem parameters and / or radio condition parameters may be kept constant. The modem parameters may be applied to a radio subsystem. The modem parameters may represent radio conditions. A motion detection signal (e.g., a baseband signal and / or packets decoded / demodulated from the baseband signal, etc.) may be obtained by processing (e.g., downconverting) a first radio signal (e.g., an RF / WiFi / LTE / 5G signal) by the radio subsystem using the radio conditions represented by the stored modem parameters. The modem parameters / radio conditions may be updated (e.g., using previous modem parameters or previous radio conditions). Both the previous and updated modem parameters / radio conditions may be applied to a radio subsystem of a digital communication system. Both the previous and updated modem parameters / radio conditions may be compared / analyzed / processed / monitored in a task.

[0200] The channel information may also be modem parameters (e.g., stored or newly calculated) used to process the wireless signal. The wireless signal may include multiple probe signals. The same modem parameters can be used to process multiple probe signals. The same modem parameters can also be used to process multiple wireless signals. The modem parameters may include parameters indicating settings or overall configurations for operation of the radio subsystem or baseband subsystem (or both) of the wireless sensor device. The modem parameters may include one or more of gain settings, RF filter settings, RF front-end switch settings, DC offset settings, or IQ compensation settings for the radio subsystem, or digital DC correction settings, digital gain settings, and / or digital filtering settings (e.g., for the baseband subsystem). CI may also relate to information related to time, time signature, timestamp, time amplitude, time phase, time trend, and / or time characteristics of a signal. CI may be associated with information related to the time-frequency division, signature, amplitude, phase, trend, and / or characteristics of a signal. CI may relate to signal decomposition. A CI may relate to information related to direction, angle of arrival (AoA), angle of a directional antenna, and / or phase of a signal passing through a channel. A CI may relate to the attenuation pattern of a signal passing through a channel. Each CI may be associated with a Type 1 device and a Type 2 device. Each CI may be associated with an antenna of a Type 1 device and an antenna of a Type 2 device.

[0201] The CI can be obtained from communication hardware (e.g., a Type 2 device or a Type 1 device) capable of providing the CI. The communication hardware can be a WiFi-enabled chip / IC (integrated circuit), a chip compliant with 802.11 or 802.16 or other wireless / wireless standards, a next-generation WiFi-enabled chip, an LTE-enabled chip, a 5G-enabled chip, a 6G / 7G / 8G-enabled chip, a Bluetooth-enabled chip, an NFC (near field communication)-enabled chip, a BLE (Bluetooth low energy)-enabled chip, a UWB chip, or other communication chips (e.g., Zigbee, WiMax, mesh networks). The communication hardware calculates the CI and stores the CI in a buffer memory so that the CI is available for extraction. The CI can include data related to channel state information (CSI) and / or at least one matrix. The at least one matrix can be used for channel equalization, beamforming, etc. The channel can be associated with a location. Attenuation can be due to signal propagation at the location, signal propagation through / at / near the air (e.g., the air at the location), reflection, refraction, diffraction, refractive media / reflective surfaces such as walls, doors, furniture, obstacles, and / or barriers, etc. Attenuation can be due to reflections off surfaces and obstacles (e.g., reflective surfaces, obstacles) such as floors, ceilings, furniture, fixtures, objects, people, pets, etc. Each CI can be associated with a timestamp. Each CI can include N components (e.g., N frequency-domain components in CFR, N time-domain components in CIR, or N decomposed components). Each component can be associated with a component index. Each component can be a real, imaginary, or complex quantity, magnitude, phase, flag, and / or set. Each CI can include a vector or matrix of complex numbers, a set of mixed quantities, and / or a multidimensional collection of at least one complex number.

[0202] Components of the TSCI associated with a particular component index may form respective component time series associated with the respective index. The TSCI may be divided into N component time series. Each individual component time series is associated with a respective component index. Object motion characteristics / STI 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 based on some criteria / cost function / signal quality metric (e.g., based on signal-to-noise ratio and / or interference level) for further processing.

[0203] Component-wise characteristics of the TSCI component-feature time series may be calculated. The component-wise characteristics may be scalars (e.g., energy) or functions with domains and ranges (e.g., autocorrelation functions, transforms, inverse transforms). Object motion characteristics / STIs may be monitored based on the component-wise characteristics. Overall characteristics (e.g., aggregate characteristics) of the TSCI may be calculated based on the component-wise characteristics of each component time series of the TSCI. The overall characteristics may be a weighted average of the component-wise characteristics. Object motion characteristics / STIs may be monitored based on the overall characteristics. The total amount may be a weighted average of the individual amounts.

[0204] Type 1 devices and Type 2 devices may support WiFi, WiMax, 3G / 3G Beyond, 4G / 4G Beyond, 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 standards, 802.15 standards, 802.16 standards, 3GPP standards, and / or other wireless systems.

[0205] A common wireless system and / or a common wireless channel may be shared by a Type 1 transceiver and / or at least one Type 2 transceiver. The at least one Type 2 transceiver may transmit respective signals simultaneously (or: asynchronously, synchronously, sporadically, continuously, repeatedly, in parallel, simultaneously, and / or at one time) using the common wireless system and / or the common wireless channel. The Type 1 transceiver may transmit signals to the at least one Type 2 transceiver using the common wireless system and / or the common wireless channel.

[0206] Each Type 1 device and Type 2 device may have at least one transmit and receive antenna. Each CI may be associated with one of the transmit antennas of the Type 1 device and one of the receive antennas of the 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.

[0207] At least one TSCI may correspond to various antenna pairs between a Type 1 device and a Type 2 device. The Type 1 device may have at least one antenna. The Type 2 device may also have at least one antenna. Each TSCI may be associated with an antenna of the Type 1 device and an antenna of the Type 2 device. Averaging or weighted averaging across antenna links may be performed. The averaging or weighted averaging may be across at least one TSCI. The averaging may optionally be performed over a subset of the at least one TSCI corresponding to a subset of the antenna pairs.

[0208] The timestamps of some CIs of a TSCI may be irregular and may be corrected so that the corrected timestamps of the time-corrected CIs are evenly spaced in time. In the case of multiple Type 1 devices and / or multiple Type 2 devices, the corrected timestamps may be related to the same clock or different clocks. An original timestamp associated with each of the CIs may be determined. The original timestamps may not be evenly spaced in time. The original timestamps of all CIs of a particular portion of a particular TSCI in the current sliding time window may be corrected so that the corrected timestamps of the time-corrected CIs are evenly spaced in time.

[0209] Characteristics and / or STI (e.g., motion information) may include location, location coordinates, change in location, location (e.g., initial location, new location), location on map, height, horizontal position, vertical position, distance, displacement, speed, acceleration, rotational speed, rotational acceleration, direction, movement angle, orientation, direction of movement, rotation, path, deformation, translation, contraction, extension, gait, gait cycle, head movement, repetitive movement, periodic movement, pseudo-periodic movement, impulse movement, sudden movement, falling movement, transient movement, behavior, transient behavior, movement cycle, movement frequency, time trend, temporal profile, temporal characteristics, occurrence, change, temporal change, change in CI, change in frequency, change in timing, change in gait cycle, timing, start time, start time, end time, duration, movement history, movement type, movement classification, frequency, frequency spectrum, frequency characteristics, presence, absence, proximity, proximity , retreat, object identification / identifier, object composition, head movement velocity, head movement direction, mouth-related rate, eye-related rate, breathing rate, heart rate, tidal volume, breathing depth, inhalation time, exhalation time, inhalation to sweep time ratio, airflow rate, heart rate interval, heart rate variability, hand movement rate, hand movement direction, leg movement, body movement, walking speed, hand movement velocity, position characteristics, object movement-related characteristics (e.g., change in position / location), tool movement, machine movement, compound movement, and / or combination of multiple movements, event, signal statistics, signal dynamics, anomaly, movement statistics, movement parameters, motion detection indication, 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, covariance, autocovariance, cross-covariance, inner product, Cartesian product, motion signal transformation, motion features, motion presence, motion absence, motion localization, motion discrimination, motion recognition, object presence, object absence, object entrance, object exit, object change, movement cycle, movement count, gait cycle, movement rhythm, movement deformation, gesture, handwriting, head movement, mouth movement, cardiac movement, visceral movement, motion trend, size, length, area, volume, volume, shape, morphology, tag, start / start position, end position, start / start amount, end amount, event, fall event, security The information may include security events, accident events, home events, office events, factory events, warehouse events, manufacturing events, assembly line events, maintenance events, car-related events, navigation events, tracking events, door events, door open events, door close events, window events, window open events, window closed events, repeatable events, one-time events, consumption, non-consumption, status, physical status, health status, comfort status, emotional status, mental status, other events, analysis, output response, and / or other information. Characteristics and / or STIs may be calculated / monitored based on features calculated from the 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 / marked / presented / indicated / highlighted / stored / communicated based on feature analysis. Analysis may include motion detection / motion assessment / presence detection. Computational workloads may be shared among Type 1 devices, Type 2 devices, and other processors.

[0210] The Type 1 device and / or the Type 2 device may be a local device, which may be a smartphone, a smart device, a TV, a sound bar, a set-top box, an access point, a router, a repeater, a wireless signal repeater / extender, a remote control, a speaker, a fan, a refrigerator, a microwave oven, a coffee machine, a hot water pot, an appliance, a table, a chair, a light, a lamp, a door lock, a camera, a microphone, a motion sensor, a security device, a fire hydrant, a garage door switch, a power adapter, a computer, a dongle, a computer peripheral, an electronic pad, a sofa, a tile, an accessory, a home device, a vehicle device, an office device, a building equipment, a manufacturing equipment, a watch, a glass, a clock, a television, an oven, an air conditioner, an accessory, a utility, an appliance, a smart machine, a smart vehicle, an Internet of Things (IoT), a smart house, a smart office, a smart building, a smart parking lot, a smart system, and other devices.

[0211] Each Type 1 device may be associated with a respective identifier (e.g., ID). Each Type 2 device may also be associated with a respective identification (ID). The ID may include numbers, a combination of text and numbers, a name, a password, an account, an account ID, a web link, a web address, an index to some information, and / or another ID. The ID may be assigned. The ID may be assigned by hardware (e.g., hardwired, via a dongle, and / or other hardware), software, and / or firmware. The ID may be stored (e.g., in a database, in memory, in a server (e.g., a hub device), in the cloud, locally stored, remotely stored, permanently stored, or temporarily stored) and may be searched. The ID may be associated with at least one record, account, user, household, address, phone number, social security number, customer number, another ID, another identifier, timestamp, and / or collection of data. The ID and / or a portion of the ID of the Type 1 device may be made available to the Type 2 device. The ID 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 control.

[0212] The object may be a person, user, subject, passenger, child, elderly, infant, sleeping infant, infant in a vehicle, patient, worker, high value worker, expert, medical specialist, waiter, customer in a mall, traveller at an airport / train station / bus terminal / shipping terminal, staff / laborer / customer service person in a factory / mall / supermarket / office / workplace, service person in a sewer / air ventilation system / lift well, lift in a lift well, elevator, inmate, person to be tracked / monitored, animal, plant, living thing, pet, dog, cat, smartphone, phone accessory, computer, tablet, portable computer, dongle, computer accessory equipment, network equipment, WiFi equipment, IoT device, smart watch, smart glasses, smart device, speaker, key, smart key, wallet, wallet, handbag, backpack, goods, cargo, luggage, equipment, motor, machine, air conditioner, fan, air conditioning equipment, lighting fixture, movable light, television, camera, audio and / or video equipment, It can be stationery, surveillance equipment, parts, signs, tools, carts, tickets, parking passes, passes, plane tickets, credit cards, plastic cards, access cards, food packaging, utensils, tables, chairs, cleaning equipment / tools, vehicles, automobiles, cars in parking facilities, goods in a warehouse / store / supermarket / distribution center, boats, bicycles, airplanes, drones, remote controlled cars / planes / boats, robots, manufacturing equipment, assembly lines, materials / unfinished parts / robots / trolleys / transports on a factory floor, tracked objects in an airport / shopping mart / supermarket, non-objects, absence of objects, presence of objects, objects with shape, objects changing shape, shapeless objects, mass of a fluid, mass of a liquid, mass of a gas / smoke, fire, flames, electromagnetic (EM) sources, EM media, and / or other objects.The object itself may be communicatively coupled 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 AC-powered and bulky, and may be moved during installation, cleaning, maintenance, renovation, etc. The object may also be placed on a mobile platform, such as a lift, pad, mobile platform, elevator, conveyor belt, robot, drone, forklift, car, boat, or vehicle. The object may have multiple parts, each with a different motion (e.g., change of location / position). For example, the object may be a person walking in front. While walking, his left and right hands may move in different directions with different instantaneous speeds, accelerations, and motions.

[0213] The wireless transmitter (e.g., a Type 1 device), the wireless receiver (e.g., a Type 2 device), another wireless transmitter, and / or another wireless receiver may travel with the object and / or another object (e.g., in a previous trip, a current trip, and / or a future trip). They may be communicatively coupled to one or more nearby devices. They may transmit TSCIs and / or information related to the TSCIs to nearby devices and / or to each other. They may be associated with nearby devices. The wireless transmitter and / or wireless 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 the nearby device.

[0214] The nearby device may be a smartphone, an iPhone, an Android phone, a smart device, a smart appliance, a smart vehicle, a smart gadget, a smart TV, a smart refrigerator, a smart speaker, a smart watch, smart glasses, a smart pad, an iPad, a computer, a wearable computer, a notebook computer, a gateway. The nearby device may be connected to a cloud server, a local server (e.g., a hub device), and / or other servers via the Internet, a wired Internet connection, and / or a wireless Internet connection. The nearby device may be portable. The portable device, nearby devices, local server (e.g., hub device), and / or cloud server can share tasks (e.g., acquiring TSCI, determining object characteristics / STI related to object movement (e.g., position / change in position), calculating time series of power (e.g., signal strength) information, determining / calculating specific functions, searching for local extrema, classification, identifying specific values ​​of offset time, denoising, processing, simplification, cleaning, wireless smart sensing tasks, extracting CIs from signals, switching, segmenting, estimating trajectory / path / track, processing maps, processing trajectory / path / track based on environmental models / constraints / limits, correction, correction adjustment, tuning, map-based (or model-based) correction, error detection, checking for boundary hits, thresholding) and calculation and / or storage of information (e.g., TSCI). Nearby devices may not move with the object. Nearby devices can be portable / non-portable / mobile / non-mobile. Nearby devices can use battery power, solar, AC power, and / or other power sources. The nearby device may have replaceable / non-replaceable and / or rechargeable / non-rechargeable batteries. The nearby device may resemble the object. The nearby device may have the same (and / or similar) hardware and / or software as the object.The nearby devices may be smart devices, network-enabled devices, devices with connections 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, smart watches, smart clocks, smart appliances, smart machines, smart appliances, smart tools, smart vehicles, Internet of Things (IoT) devices, Internet-enabled devices, computers, portable computers, tablets, and other devices. Nearby devices and / or at least one processor associated with a wireless receiver, a wireless transmitter, another wireless receiver, another wireless transmitter, and / or a cloud server (in the cloud) may determine an initial STI for the object. Two or more of them may jointly determine initial spatio-temporal information. Two or more of them may share intermediate information in determining the initial STI (e.g., initial location).

[0215] In one example, a wireless transmitter (e.g., a Type 1 device or a tracker bot) moves with an object. The wireless transmitter can send a signal to a wireless receiver (e.g., a Type 2 device or an Origin Register) to determine the object's initial STI (e.g., initial location). The wireless transmitter can also send a signal and / or another signal to another wireless receiver (e.g., another Type 2 device or another Origin Register) to monitor the object's movement (spatio-temporal information). The wireless receiver can also receive a signal and / or another signal from the wireless transmitter and / or another wireless transmitter to monitor the object's movement. The location of the wireless receiver and / or another wireless receiver can be known. In another example, a wireless receiver (e.g., a Type 2 device or a tracker bot) can move with the object. The wireless receiver can receive a signal transmitted from the wireless transmitter (e.g., a Type 1 device or an Origin Register) to determine the object's initial spatio-temporal information (e.g., initial location). The wireless receiver may also receive a signal and / or another signal from another wireless transmitter (e.g., another Type 1 device or another origin register) for monitoring the current movement (e.g., space-time information) of the object. The wireless transmitter may also transmit a signal and / or another signal to the wireless receiver and / or another wireless receiver (e.g., another Type 2 device or another tracker bot) for monitoring the movement of the object. The location of the wireless transmitter and / or another wireless transmitter may be known.

[0216] Locations include sensing areas, rooms, houses, offices, property, workspaces, corridors, lifts, lift wells, escalators, elevators, sewers, ventilation systems, stairs, assembly areas, ducts, air ducts, pipes, enclosed spaces, enclosed structures, semi-enclosed structures, enclosed areas with at least one wall, plants, machines, engines, structures, structures with wood, structures with glass, structures with metal, structures with walls, structures with doors, structures with gaps, structures with reflective surfaces, structures with liquids, buildings, rooftops, stores, factories, assembly lines, homes, etc. Hotel rooms, museums, classrooms, schools, universities, government buildings, warehouses, garages, malls, airports, train stations, bus terminals, hubs, transportation hubs, cargo terminals, government buildings, public facilities, schools, universities, entertainment venues, recreational facilities, hospitals, pediatric / neonatal wards, nursing homes, elderly care facilities, community centers, stadiums, playgrounds, fields, basketball courts, tennis courts, soccer stadiums, baseball fields, gymnasiums, garages, shopping marts, knolls, supermarkets, manufacturing facilities, parking facilities Facilities, construction sites, mining facilities, transportation facilities, highways, roads, valleys, forests, trees, terrain, landscapes, caves, patios, land, roads, amusement parks, urban areas, rural areas, suburban areas, metropolitan areas, gardens, squares, plazas, music halls, downtown facilities, open facilities, semi-open facilities, closed areas, train platforms, train stations, distribution centers, warehouses, shops, distribution centers, storage facilities, underground spaces, spatial (e.g. above ground, space) facilities, floating facilities, caves, tunnel facilities, indoor facilities, outdoor facilities, outdoor facilities with some walls / doors / reflective barriers Areas such as facilities, open facilities, semi-open facilities, automobiles, trucks, buses, vans, containers, ships / boats, submarines, trains, trams, airplanes, vehicles, mobile platforms, caves, tunnels, pipes, channels, metropolitan areas, downtown areas with relatively tall buildings, valleys, wells, ducts, pathways, gas lines, oil pipes, water pipes, interconnecting pathways / arrays / roads / tubes / cavities / caves / pipe-like structures / voids / fluid spaces, human bodies, animal bodies, body cavities, organs, bones, teeth, soft tissue, hard tissue, rigid tissue, non-hard tissue, blood / body fluid ducts, wind ducts, air ducts, burrows, etc. The location may be an indoor space, an outdoor space, and the location may include both inside and outside spaces.For example, a location can include both inside and outside a building. For example, a location can be a building with one or more floors, and part of the building can be underground. The shape of the building can be, for example, round, square, rectangular, triangular, or irregular. These are merely examples. The present disclosure can be used to detect events in other types of locations or spaces.

[0217] The wireless transmitter (e.g., a Type 1 device) and / or wireless receiver (e.g., a Type 2 device) may be embedded in a portable device (e.g., a module or a device having a module) that may travel with the object (e.g., in a previous travel and / or a current travel). The portable device may be communicatively coupled to the object using a wired connection (e.g., via USB, micro USB, Firewire, HDMI, serial port, parallel port, and other connectors) and / or a connection (e.g., Bluetooth, Bluetooth Low Energy (BLE), WiFi, LTE, NFC, ZigBee). The portable device may be a lightweight device. The portable device may be powered by batteries, rechargeable batteries, and / or AC power. The portable device may be very small (e.g., on the 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, bulky, and / or heavy installed machinery. Portable devices include WiFi hotspots, access points, Mobile WiFi (MiFi), dongles with USB / micro USB / Firewire / other connectors, smartphones, portable computers, computers, tablets, smart devices, Internet of Things (IoT) devices, WiFi enabled devices, LTE enabled devices, smart watches, smart glass, smart mirrors, smart antennas, smart batteries, smart lights, smart pens, smart rings, smart doors, smart windows, smart clocks, smart batteries, smart wallets, smart belts, smart handbags, smart cloth / garments, smart ornaments, smart packaging, smart paper / books / magazines / posters / printed materials / signage / displays / illuminated systems / lighting systems, smart keys / tools, smart bracelets / chains / necklaces / clothes / accessories, smart pads / cushions, smart tiles / blocks / bricks / building materials / other materials,Smart trash can / waste container, smart food carriage / storage, smart ball / racket, smart chair / sofa / bed, smart shoes / footwear / carpet / mat / shoe rack, smart gloves / handwear / ring / handwear, smart hat / cap / cosmetics / sticker / tattoo, smart mirror, smart toy, smart pill, smart cookware, smart bottle / food container, smart tool, smart device, IoT device, WiFi enabled device, network enabled device, 3G / 4G / 5G / 6G enabled device, UMTS device, 3GPP device, GSM device, EDGE device, TDMA device, FDMA device, CDMA device, WCDMA device, TD-SCDMA device, embedded device, embeddable device, air conditioner, refrigerator, heater, furnace, furniture, oven, cooking device , TV / Set Top Box (STB) / DVD Player / Audio Player / Video Player / Remote Control, Hi-Fi, Audio Device, Speaker, Lamp / Light, Wall, Door, Window, Roof, Tile / Roofing Shingle / Structure / Attic Structure / Device / Feature / Installation / Fixture, Lawn Mower / Garden Equipment / Tools / Machine Tools / Garage Tools, Garbage Can / Container, 20ft / 40ft Container, Storage Container, Factory / Production / Manufacturing Equipment, Repair Tool, Fluid Container, Machine, Installed Machine, Vehicle, Cart, Wagon, Warehouse Vehicle, Automobile, Bicycle, Motorcycle, Boat, Watercraft, Airplane, Basket / Box / Bag / Bucket / Container, Smart Plate / Cup / Bowl / Pot / Mat / Utensil / Kitchenware / Kitchen Accessories / Cabinet / Table / Chair / Tile / Light / Water Pipe / Faucet / Gas Range / Oven / Dishwasher / , etc. Portable devices may have batteries that may be replaceable, non-replaceable, rechargeable, and / or non-rechargeable. Portable devices may be charged wirelessly. The portable device may be a smart payment card. The portable device may be a payment card used in parking lots, highways, entertainment parks, or other locations / facilities requiring payment. The portable device may have an identity (ID) / identifier, as described above.

[0218] Events can be monitored based on the TSCI. Events can be object-related events such as an object (e.g., a person and / or a patient) falling, rolling, hesitating, resting, impact (e.g., a person hitting a punching bag, door, window, bed, chair, table, desk, cabinet, box, another person, animal, bird, flying, table, chair, ball, bowling ball, tennis ball, football, soccer ball, baseball, basketball, volleyball), two-body action (e.g., leaving a balloon, catching a fish, molding clay, writing a paper, person typing into a computer), moving a car in a garage, a person carrying a smartphone and walking around an airport / mall / government office / office / etc, autonomous mobile objects / machines moving around (e.g., vacuum cleaner, utility vehicle, car, drone, self-driving car), etc. The tasks or wireless smart sensing tasks are object detection, presence detection, proximity detection, object recognition, activity recognition, object verification, object counting, daily activity monitoring, health monitor, vital signs monitoring, health status monitoring, baby monitoring, elderly monitoring, sleep monitoring, sleep stage monitoring, gait monitoring, movement monitoring, tool detection, tool recognition, tool verification, patient detection, patient monitoring, patient verification, machine detection, machine verification, human detection, human recognition, human verification, baby detection, baby recognition, baby verification, human breathing detection, human breathing recognition, human breathing estimation, human breathing verification, human heart rate detection, human heart rate recognition, human heart rate estimation, human Heart rate verification, fall detection, fall recognition, fall estimation, fall verification, emotion detection, emotion recognition, emotion estimation, emotion verification, motion detection, motion degree estimation, motion recognition, motion estimation, motion verification, periodic motion detection, periodic motion estimation, periodic motion verification, repetitive motion detection, periodic motion recognition, repetitive motion estimation, repetitive motion verification, static motion recognition, static motion detection, static motion estimation, static motion verification, cyclostationary motion detection, cyclostationary motion recognition, cyclostationary motion estimation, cyclostationary motion verification, transient motion detection, transient motion recognition, transient motion estimation, transient motion verification, trend detection, trend recognition, trend estimation, trend verification, breathing detection, breathing recognition, breathing estimation, human biometric detection, human biometric recognition, human biometric estimation,Human biometric 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, supervised learning, unsupervised learning, semi-supervised learning, clustering, feature extraction, feature training, principal component analysis, eigenvalue decomposition, frequency decomposition, time decomposition, Time-Frequency Decomposition, Function Decomposition, Other Decompositions, Training, Discriminative Training, Supervised Training, Unsupervised Training, Semi-Supervised Training, Neural Networks, Sudden Motion Detection, Fall Detection, Hazard Detection, Life-Threat Detection, Regular Motion Detection, Stationary Motion Detection, Cyclostationary Motion Detection, Intrusion Detection, Suspicious Motion Detection, Security, Safety Monitoring, Navigation, Guidance, Map-Based Processing, Map-Based Correction, Model-Based Processing / Correction, Irregularity Detection, Localization, Room Sensing, Tracking, Multiple Object Tracking, Indoor Tracking, Indoor Positioning, Indoor Navigation, Energy Management, Power Transmission, Wireless Power Transmission, Object Counting, Car Tracking in Parking Garages, Device / System Activation (e.g., Security Systems, Access Systems, Alarms, Sirens, Speakers, Televisions, Entertainment Systems, Cameras, Heating / Air Conditioning (HVAC) Systems, Ventilation Systems, Lighting Systems, Gaming Systems, Coffee Machines, Cooking Appliances, Cleaning Equipment, Housekeeping Equipment), Geometric Estimation The tasks may include communication, augmented reality, wireless communication, data communication, signal broadcasting, networking, coordination, management, encryption, protection, cloud computing, other processing, and / or other tasks. The tasks may be performed by a Type 1 device, a Type 2 device, another Type 1 device, another Type 2 device, a nearby device, a local server (such as a hub device), an edge server, a cloud server, and / or another device. The tasks may be based on TSCI between any pair of Type 1 and Type 2 devices. A Type 2 device may also be a Type 1 device, and vice versa. A Type 2 device may fulfill the role (e.g., functionality) of a Type 1 device temporarily, continuously, sporadically, simultaneously, and / or concurrently, and / or vice versa. A first portion of the tasks may include pre-processing, processing, signal conditioning, signal processing, post-processing,may include at least one of sporadic / continuous / concurrent / simultaneous / dynamic / adaptive / on-demand / as-needed processing, calibration, noise removal, feature extraction, coding, encryption, transformation, mapping, motion detection, motion estimation, motion change detection, motion pattern detection, motion pattern estimation, motion pattern recognition, vital sign detection, vital sign estimation, vital sign recognition, periodic motion detection, periodic motion estimation, repetitive motion detection / estimation, breathing rate detection, breathing rate estimation, breathing pattern detection, breathing pattern estimation, breathing pattern recognition, heart rate detection, heart rate estimation, cardiac pattern detection, cardiac pattern estimation, cardiac pattern recognition, gesture detection, gesture estimation, gesture recognition, velocity detection, velocity estimation, object location, object tracking, navigation, acceleration estimation, acceleration detection, fall detection, change detection, intruder (and / or tampering) detection, baby detection, baby monitoring, patient monitoring, object recognition, wireless power transmission, and / or wireless charging;

[0219] The second part of the task may be a smart home task, a smart office task, a smart building task, a smart factory task (e.g., manufacturing with a machine or assembly line), a smart Internet of Things (IoT) task, a smart system task, a smart home operation, a smart office operation, a smart building operation, a smart manufacturing operation (e.g., movement of supplies / parts / raw materials to a machine / assembly line), an IoT operation, a smart system operation, turning on lights, turning off lights, controlling light in at least one of a room, area, and / or location, playing a sound clip, playing a sound clip in at least one of a room, area, and / or location, playing at least one sound clip of welcome, greeting, farewell, a first message, and / or a second message related to the first part of the task, turning on an appliance, turning off an appliance, The control may include at least one of controlling appliances in at least one of the rooms, areas, and / or locations; turning on an electrical system; turning off an electrical system; controlling an electrical system in at least one of the rooms, areas, and / or locations; turning on a security system; turning off a security system; controlling a security system in at least one of the rooms, areas, and / or locations; turning on a mechanical system; turning off a mechanical system; controlling a mechanical system in at least one of the rooms, areas, and / or locations; and / or controlling at least one of an air conditioning system, a heating system, a ventilation system, a lighting system, a heater, a stove, an entertainment system, a door, a fence, a window, a garage, a computer system, a networked device, a networked system, a home appliance, an office appliance, a lighting device, a robot (e.g., a robotic arm), a smart vehicle, a smart machine, an assembly line, a smart device, an Internet of Things (IoT) device, a smart home device, and / or a smart office device.

[0220] The tasks are to detect when the user comes home, detect when the user leaves, detect when the user moves from one room to another, detect when a window / door / garage door / blinds / curtains / panels / solar panels / sunshades are controlled / locked / unlocked / opened / closed / partially opened, detect pets, detect / monitor when the user is doing something (e.g. sleeping on the sofa, sleeping in the bedroom, running on the treadmill, cooking, sitting on the sofa, watching TV, eating in the kitchen, eating in the dining room, going up and down stairs, going out / coming back, in the bathroom), monitor / detect the location of the user / pet and do something automatically when detected (e.g. sending a message, notifying / reporting), do something to the user when a user is detected, turn on / off / dim the lights, turn on / off the music / radio / home entertainment system, turn on / off the TV / may include turning on / off / adjusting / controlling hi-fi / set-top box (STB) / home entertainment system / smart speaker / smart device, turning on / off / adjusting air conditioning system, turning on / off / adjusting ventilation system, turning on / off / adjusting heating system, adjusting / controlling curtains / light shades, turning on / off / starting computer, turning on / off / preheating / controlling coffee machine / hot water kettle, turning on / off / preheating / controlling cooker / oven / microwave / other cooking appliance, checking / adjusting temperature, checking weather forecast, checking phone message box, checking email, checking system, controlling / adjusting system, checking / controlling / arming / disarming security system / baby monitor, checking / controlling refrigerator, reporting (e.g. through speaker such as Google Home, Amazon Echo, via web page / email / messaging system / notification system).

[0221] For example, if a user arrives at home in their vehicle, the tasks may automatically detect that the user or their vehicle is approaching, open the garage door upon detection, turn on the driveway / garage lights as the user approaches the garage, turn on the air conditioner / heater / fan, etc. As the user enters the house, the tasks may automatically turn on the entrance lights, turn off the driveway / garage lights, play a greeting message to welcome the user, turn on music, turn on the radio and tune it to the user's favorite 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 / impending events on the user's daily calendar (e.g., romantic lighting and music because the user is going to have dinner with his / her girlfriend in an hour), microwave the food the user prepared that morning, perform a diagnostic check of all systems in the house, check the weather forecast for tomorrow's tasks, check news of interest to the user, and update the user's calendar, to-do list, etc. Check reminders, check phone answering systems, messaging systems, email, communicate verbal reports using dialogue systems / speech synthesis, remind the user of their mother's birthday (e.g., using audible tools such as speakers, hi-fi, speech synthesis, sound, voice, music, song, sound field, background sound field, dialogue system, etc.; using visual tools such as TV / entertainment system / computer / notebook / smartpad / display / light / color / brightness / pattern, using tactile tools / virtual reality tools / gestures / tools, using smart devices / appliances / materials / furniture / fixtures, using web tools / servers / hub devices / cloud servers / fog servers / edge servers / home networks / mesh networks, using messaging tools / notification tools / communication tools / scheduling tools / email, using user interfaces / GUIs, using scents / smells / aromas / tastes, using neural tools / neural system tools, or a combination), create reports, provide reports (e.g., using reminding tools as described above).A task may be to proactively start an air conditioner / heater / ventilation system or proactively adjust a smart thermostat temperature setting. When a user moves from the front door to the living room, the tasks may be to turn on the living room lights, open the living room curtains, open the windows, turn off the front door light behind the user, turn on the TV and set-top box, turn on the set-top box, set the TV to the user's preferred channel, adjust the appliances according to the user's preferences and conditions / states (e.g., adjust the lighting, select / play music to create a romantic atmosphere), etc.

[0222] Another example could be: When a user wakes up in the morning, the task could be to detect the user moving around in the bedroom, open the blinds / curtains, open the windows, turn off the alarm clock, adjust the room temperature profile from a night temperature profile to a day temperature profile, turn on the bedroom lights, turn on the toilet light as the user approaches the bathroom, check the radio or streaming channels, play the morning news, turn on the coffee machine, preheat 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, turn off the bedroom and toilet lights, move music / messages / reminders from the bedroom to the kitchen, turn on the kitchen TV, 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 so the user can check the back yard, adjust the kitchen temperature setting, etc. Another example could be: When the user leaves home for work, the tasks may be to detect the user's departure, say goodbye and / or have a nice day, open / close the garage door, turn on / off the garage and driveway lights, turn off / dim to save energy (only if the user fails), close / lock all windows / doors (only if the user fails), turn off appliances (especially the stove, oven, microwave), turn on / arm the home security system to protect the home against intruders, adjust the air conditioning / heating / ventilation system to an "away from home" profile to save energy, send alerts / reports / updates to the user's smartphone, etc.

[0223] The motions are classified as no motion, rest motion, motionless motion, motion, change of location / position, deterministic motion, transient motion, falling motion, repetitive motion, periodic motion, pseudo-periodic motion, periodic / repetitive motion related to breathing, periodic / repetitive motion related to heartbeat, periodic / repetitive motion related to living organisms, periodic / repetitive motion related to machines, periodic / repetitive motion related to man-made objects, periodic / repetitive motion related to nature, complex motion related to transient and periodic elements, repetitive motion, non-deterministic motion, stochastic motion, chaotic motion, random motion, complex motion with non-deterministic and deterministic elements, stationary random motion, pseudo-stationary random motion, cyclostationary random motion, non-stationary random motion, non-stationary random motion with a periodic autocorrelation function (ACF), random motion with a periodic ACF over time. motion, pseudo-stationary random motion over time, random motion where the instantaneous ACF has a pseudo-periodic / repetitive component over time, machine motion, mechanical motion, vehicle motion, drone motion, air-related motion, wind-related motion, weather-related motion, water-related motion, fluid-related motion, ground-related motion, change in magnetic properties, subsurface motion, earthquake motion, plant motion, animal motion, animal motion, human motion, normal motion, abnormal motion, dangerous motion, warning motion, suspicious motion, rain, fire, flood, tsunami, explosion, collision, imminent collision, human motion, head motion, face motion, eye motion, oral motion, tongue motion, neck motion, finger motion, hand motion, arm motion, shoulder motion, body motion, chest motion, abdominal motion, hip motion, leg motion, foot motion, body joint motion, knee motion, elbow motion, upper body motion, lower body motion, skin motion, subcutaneous motion, subcutaneous tissue motion. The motion may include at least one of blood vessel movement, venous movement, organ movement, heart movement, lung movement, stomach movement, intestinal movement, bowel movement, eating movement, breathing movement, facial expression, eye expression, mouth expression, vocal movement, singing movement, eating movement, gesture, hand gesture, arm gesture, keystroke, typing stroke, user interface gesture, man-machine interaction, gait, dance movement, coordinated movement, and / or coordinated body movement.

[0224] The heterogeneous ICs of the Type 1 device and / or any Type 2 receiver may include 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 sub-6 GHz radio, a 60 GHz radio, a sub-60 GHz radio, and / or another radio. The heterogeneous IC may include a processor, a memory communicatively coupled to the processor, and a set of instructions stored in the memory for execution by the processor. The IC and / or any processor may include at least one of a general purpose processor, a special purpose processor, a microprocessor, a multiprocessor, a multi-core processor, a parallel processor, a CISC processor, a RISC processor, a microcontroller, a central 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, another programmable logic device, discrete logic, and / or a combination. Heterogeneous ICs are used in broadband networks, wireless networks, mobile networks, mesh networks, cellular networks, wireless local area networks (WLANs), wide area networks (WANs), metropolitan area networks (MANs), WLAN standards, WiFi, LTE, LTE-A, LTE-U, 802.11 standards, 802.11a, 802.11b, 802.11g, 802.11n, 802.11ac, 802.11af, 802.11ah, 802.11ax, 802.11ay, mesh networking standards 802.16, 3G, 3.5G, 4G, Beyond 4G, 4.5G, 6G, 7G, 8G, 9G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA, Bluetooth, Bluetooth Low-Energy It may support Bluetooth Low Energy (BLE), NFC, Zigbee, WiMax, and other wireless network protocols.

[0225] The processor may include a general-purpose processor, a special-purpose processor, a microprocessor, a microcontroller, an embedded processor, a digital signal processor, a central processing unit, a graphical processing unit (GPU), a multiprocessor, a multi-core processor, and / or a processor with graphics capabilities, and / or a combination thereof. The memory may be volatile, non-volatile, random-access memory (RAM), read-only memory (ROM), programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), a hard disk, flash memory, CD-ROM, DVD-ROM, magnetic storage, optical storage, organic storage, a storage system, a storage network, network storage, cloud storage, edge storage, local storage, external storage, internal storage, or any other form of non-transitory storage medium known in the art. The set of instructions (machine-executable code) corresponding to the method steps may be directly embodied in hardware, software, firmware, or a combination thereof. The set of instructions may be embedded, pre-loaded, loaded at boot time, loaded on-the-fly, loaded on-demand, pre-installed, installed, and / or downloaded.

[0226] The presentation may be visual (e.g., using a combination of sights, graphics, text, symbols, color, shade, video, animation, sound, voice, audio, etc.), graphical (e.g., using GUI, animation, video), textual (e.g., web page with text, messages, animated text), symbolic (e.g., emojis, signs, hand gestures), or mechanical (e.g., vibration, actuator movement, haptics, etc.) presentation.

[0227] The computational workload associated with this method is shared among the processor, the type 1 heterogeneous wireless device, the type 2 heterogeneous wireless device, the local server (eg, the hub device), the cloud server, and other processors.

[0228] Operations, pre-processing, processing, and / or post-processing may be applied to the data (e.g., TSCI, autocorrelation, TSCI features). Operations may include pre-processing, processing, and / or post-processing. Pre-processing, processing, and / or post-processing may be operations. Operations may include pre-processing, processing, post-processing, scaling, calculating confidence coefficients, calculating line-of-sight (LOS) quantities, non-LOS calculations, and the like. Computing non-linear (NLOS) quantities, computing quantities including line-of-sight and non-linear (NLOS), computing quantities for a single link (e.g., path, communication path, link between a transmitting antenna and a receiving antenna), computing quantities including multiple links, computing functions of operands, filtering, linear filtering, nonlinear filtering, folding, grouping, energy computation, low-pass filtering, band-pass filtering, high-pass filtering, median filtering, rank filtering, quartile filtering, percentile filtering, finite impulse response (FIR) filtering, infinite impulse response (IIR) filtering, moving average (MA) filtering, autoregressive (AR) filtering, autoregressive moving average (ARMA) filtering, selective filtering, adaptive filtering, interpolation, decimation, subsampling, upsampling, resampling, time correction, time-based correction, phase correction, magnitude correction, phase cleaning, amplitude cleaning, matched filtering, enhancement, restoration, noise removal, smoothing, signal conditioning, enhancement, restoration, linear transform, nonlinear transform, inverse transform, frequency transformation, inverse frequency transformation, Fourier transform (FT), discrete-time FT (DTFT), Discrete FT (DFT), Fast FT (FFT), Wavelet Transform, Laplace Transform, Hilbert Transform, Hadamard Transform, Trigonometric Transform, Sin Transform, Cosine Transform, DCT, Power of 2 Transform, Sparse Transform, Graph Based Transform, Graph Signal Processing, Fast Transform, Transform Combined with Zero Padding, Cyclic Padding, Padding, Zero Padding, Feature Extraction, Decomposition, Projection, Orthogonal Projection, Non-Orthogonal Projection, Overprojection (oecomlee)ojecion), eigendecomposition, singular value decomposition (SVD), principle component analysis (ICA), independent component analysis (ICA), grouping, sorting, thresholding, soft thresholding, hard thresholding, clipping, soft clipping, first derivative, second derivative, higher derivative, convolution, multiplication, division, addition, subtraction, integration, maximization, minimization, least squared error, recursive least squares, constrained least squares, batch least squares, least absolute deviation, least mean squared deviation, least absolute deviation, local maximization, local minimization, cost function optimization, neural networks, recognition, labeling, training, class filtering, machine learning, supervised learning, unsupervised learning, semi-supervised learning, comparison with other TSCI, similarity score calculation, quantization, vector quantization, matching pursuit, compression, encryption, encoding, storage, transmission, normalization, time normalization, frequency domain normalization, classification, clustering, labeling, tagging, learning, detection, estimation, learning network, mapping, remapping, expansion, storage, search, transmission, reception, representation, combining, merging, splitting, tracking, monitoring, matched filtering, Kalman filtering, particle filtering, interpolation, extrapolation, histogram estimation The operations may include importance sampling, Monte Carlo sampling, compressed sensing, representation, merging, combining, dividing, scrambling, error protection, forward error correction, 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, permutation, combination, sorting, AND, OR, XOR, union, intersection, vector addition, vector subtraction, vector multiplication, vector division, inverse, norm, distance, and / or another operation. The operations may be pre-processing, processing, and / or post-processing. Operations may be applied jointly on multiple time series or functions.

[0229] Functions (e.g., functions of operands) can be scalar functions, vector functions, discrete functions, continuous functions, polynomial functions, properties, characteristics, magnitude, phase, exponential functions, logarithmic functions, trigonometric functions, transcendental functions, boolean functions, linear functions, algebraic functions, nonlinear functions, piecewise functions, real functions, complex functions, vector-valued functions, inverse functions, derivative functions, integral functions, circular functions, functions of other functions, one-to-one functions, one-to-many functions, many-to-one functions, many-to-many functions, zero crossings, absolute functions, index functions, mean, mode, median, range, statistics, histogram, variance, standard deviation, measure of change, expansion, dispersion, deviation, divergence, range, interquartile range, total deviation, absolute deviation, arithmetic mean, geometric mean, harmonic mean, trimmed mean, percentile, square, cube, square root, power, sine, cosine, tangent, cotangent elliptic functions, parabolic functions, hyperbolic functions, game functions, zeta functions, absolute value, threshold, limit functions, floor functions, rounding functions, sign functions, quantization, piecewise constant functions, composite functions, functions of functions, time functions processed by operations (e.g. filtering), probabilistic functions, stochastic functions, deterministic functions, periodic functions, iterative functions, transformations, frequency transforms, inverse frequency transforms, discrete time transforms, Laplace transforms, Hilbert transforms, sine transforms, cosine transforms, trigonometric transforms, wavelet transforms, integer transforms, power of two transforms, sparse transforms, projections, decompositions, principal component analysis (PCA), neural networks, feature extraction, moving functions, functions for moving windows of adjacent items in a time series, filtering functions, convolutions, mean functions, histograms, variance / standard deviation functions, statistical functions, short-time transforms, discrete transforms, Discrete Fourier Transform, Discrete Cosine Transform, Discrete Sine Transform, Hadamard Transform, Eigenvalue Decomposition, Eigenvalue, Singular Value Decomposition (SVD), Singular Value, Orthogonal Decomposition, Matching Pursuit, Sparse Transform, Arbitrary Decomposition, Graph Based Processing, Graph Based Transform, Graph Signal Processing, Classification, Class / Group / Category Identification, Labeling, Learning, Machine Learning, Detection, Estimation, Feature Extraction, Learning Networks, Feature Extraction, Noise Reduction, Signal Enhancement, Coding, Encryption, Mapping, Remapping, Vector Quantization, Low-Pass Filtering, High-Pass Filtering, Band-Pass Filtering, Matched Filtering, Kalman Filtering, Pre-Processing, Post-Processing, Particle Filtering, FIR Filtering, IIR Filtering, Autoregressive (AR) Filtering, Adaptive Filtering, First Derivative, Higher-Order Derivative, Integration, Zero Crossing, Smoothing, Median Filtering, Mode Filtering, Sampling, Random Sampling, Resampling Function, Downsampling, Downconverting, Upsampling, Upconverting, Interpolation, Extrapolation, Importance Sampling, Monte Carlo Sampling, Compressed Sensing, Statistics, These may include short-term statistics, long-term statistics, autocorrelation functions, cross-correlation functions, moment-generating functions, time averages, weighted averages, special functions, Bessel functions, error functions, complementary error functions, beta functions, gamma functions, integral functions, Gaussian functions, Poisson functions, etc. Machine learning, training, discriminative training, deep learning, neural networks, continuous-time processing, distributed computing, distributed storage, acceleration using GPUs / DSPs / coprocessors / multi-cores / multi-processing may be applied to the steps (or each step) of the present disclosure.

[0230] The frequency transform may include a Fourier transform, a Laplace transform, a Hadamard transform, a Hilbert transform, a sine transform, a cosine transform, a trigonometric transform, a wavelet transform, an integer transform, a power of two transform, zero-padding and combinations of transforms, a power Fourier transform with zero-padding, and / or another transform. Fast and / or approximate versions of the transforms may be performed. The transforms may be performed using floating-point and / or fixed-point arithmetic.

[0231] The inverse frequency transform may include an inverse Fourier transform, an inverse Laplace transform, an inverse Hadamard transform, an inverse Hilbert transform, an inverse sine transform, an inverse cosine transform, an inverse trigonometric transform, an inverse wavelet transform, an inverse integer transform, an inverse power of two transform, a combination of zero padding and transforms, an inverse Fourier transform with zero padding, and / or another transform. Fast and / or approximate versions of the transform may be performed. The transform may be performed using floating-point and / or fixed-point arithmetic.

[0232] Quantities / features from TSCI can be calculated. Quantities include: movement, location, map coordinates, height, speed, acceleration, movement angle, rotation, dimension, volume, time trend, one-time pattern, recurring pattern, evolving pattern, time pattern, mutually exclusive pattern, associated / correlated pattern, cause-effect, short-term / long-term correlation, tendency, slope, preference, statistics, typical behavior, atypical behavior, time trend, time profile, periodic movement, repetitive movement, repetition, tendency, change, sudden change, gradual change, frequency, transient, breathing, gait, behavior, event, suspicious event, dangerous event, warning event, warning, belief, proximity, collision, power, signal, signal power, signal strength, signal volume, received signal strength indicator (RSSI), signal amplitude, signal phase, signal frequency component, signal frequency band component, channel state information (CSI), map, time, frequency, time-period The statistics may include at least one of wavenumber, decomposition, orthogonal decomposition, non-orthogonal decomposition, tracking, respiration, palpitations, statistical parameters, cardiopulmonary statistics / analysis (e.g., output response), daily activity statistics / analysis, chronic disease statistics / analysis, medical statistics / analysis, early (or instantaneous or simultaneous or delayed) indicator / suggestion / sign / marker / verifier / detection / symptom / condition / state, biometric, baby, patient, machine, device, temperature, vehicle, parking lot, location, lift, elevator, space, road, fluid flow, home, room, office, house, building, warehouse, storage, system, ventilation, fan, pipe, duct, people, human, car, boat, truck, plane, drone, downtown, crowd, impulse event, cyclostationary, environment, vibration, material, surface, 3D, 2D, local, global, presence, and / or other measurable quantity / variable.

[0233] The sliding time window may have a time-varying window width. It may be smaller initially to allow for rapid acquisition and may increase over time to a steady-state size. The steady-state size may be related to the monitored frequency, repetitive motion, transient motion, and / or STI. Even in the steady state, the window size may be adaptively (and / or dynamically) changed (e.g., adjusted, varied, modified) based on battery life, power consumption, available computing power, changes in the volume of interest, the nature of the monitored motion, etc.

[0234] The time shift between two sliding time windows at adjacent time instances can be constant / variable / locally adaptive / dynamically adjusted over time. If a shorter time shift is used, any monitoring updates can be more frequent, which can be used for rapidly changing conditions, object movements, and / or objects. A longer time shift can be used for slower conditions, object movements, and / or objects. The window width / size and / or time shift can be changed (e.g., adjusted, changed, modified) according to user requests / selections. The time shift can be changed automatically (e.g., as controlled by a processor / computer / server / hub device / cloud server) and / or adaptively (and / or dynamically).

[0235] At least one characteristic (e.g., a characteristic value or characteristic point) of a function (e.g., an autocorrelation function, an autocovariance function, a cross-correlation function, a cross-covariance function, a power spectral density, a time function, a frequency domain function, a frequency transform) may be determined (e.g., by the object tracking server, a processor, a Type 1 heterogeneous device, a Type 2 heterogeneous device, and / or another device). At least one characteristic of the function may include a maximum, a minimum, an extremum, a local maximum, a local minimum, a local extremum, a local extremum with a positive time offset, a first local extremum with a positive time offset, an nth local extremum with a positive time offset, a first local extremum with a negative time offset, a bounded maximum, a bounded minimum, a bounded extremum, a significant maximum, a significant minimum, a significant extremum, a gradient, a derivative, a higher order derivative, a maximum gradient, a minimum gradient, a local maximum gradient, a local maximum gradient with a positive time offset, a local minimum gradient, a bounded maximum gradient, a bounded minimum gradient, a maximum higher order derivative, a minimum higher order derivative, a bounded higher order derivative, a zero crossing, a zero crossing with a positive time offset, an nth zero crossing with a positive time offset, a zero crossing with a negative time offset, an nth zero crossing with a negative time offset, a bounded zero crossing, a zero crossing of a gradient, a zero crossing of a gradient of a higher order derivative, and / or other characteristics. At least one argument of the function associated with at least one characteristic of the function may be identified. A quantity (eg, spatial-temporal information of an object) may be determined based on at least one argument of the function.

[0236] Characteristics (e.g., characteristics of an object's movement at a location) can include instantaneous characteristics, short-term characteristics, recurring characteristics, recursive characteristics, history, incremental characteristics, change characteristics, deviation characteristics, phase, amplitude, degree, time characteristics, frequency characteristics, time-frequency characteristics, decomposition characteristics, orthogonal decomposition characteristics, non-orthogonal decomposition characteristics, deterministic characteristics, probability characteristics, stochastic characteristics, autocorrelation function (ACF), mean, variance, standard deviation, measure of change, spread, variance, deviation, divergence, range, interquartile range, total variation, absolute deviation, total deviation, statistics, duration, timing, trend, periodic characteristics, recurring characteristics, long-term characteristics, historical characteristics, average characteristics, recent characteristics, past characteristics, future characteristics, predicted characteristics, position, distance, height, speed, direction, velocity, acceleration, change in acceleration, angle, angular speed, change in angular velocity, of an object The change in the angular acceleration includes at least one of: angular acceleration, change in angular acceleration, orientation of the object, angle of rotation, deformation of the object, shape of the object, change in shape of the object, change in size of the object, change in structure of the object, and / or change in properties of the object.

[0237] At least one local maximum and at least one local minimum of the function may be identified. At least one local signal-to-noise ratio-like (SNR-like) parameter may be calculated for each pair of adjacent local maximums and minima. The SNR-like parameter may be a function (e.g., linear, logarithmic, exponential, monotonic) of the fraction of the amount (e.g., power, magnitude) of the local maximum 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 local minimum. Significant local peaks may be identified or selected. Each significant local peak may be a local maximum with an SNR-like parameter greater than a threshold T1 and / or a local maximum with an amplitude greater than a threshold T2. At least one local minimum and at least one local minimum in the frequency domain may be identified / calculated using a persistence-based approach.

[0238] A set of selected significant local peaks may be selected from the set of identified significant local peaks based on a selection criterion (e.g., quality criteria, signal quality state). An object characteristic / STI may be calculated based on the set of selected significant local peaks and frequency values ​​associated with the set of selected significant local peaks. In one example, the selection criterion may always correspond to selecting the strongest peak in the range. The strongest peak may be selected, but non-selected peaks may still be significant (or even strong).

[0239] Unselected significant peaks may be saved and / or monitored as "reserved" peaks for use in future selections in future sliding time windows. As an example, there may be a particular peak (at a particular frequency) that appears consistently over time. Initially, even though it is significant, it may not be selected (because other peaks may become stronger). However, at a later time, the peak may become stronger and more dominant and may be selected. If it is "selected," it may be back-traced in time and deemed "selected" at an earlier time that was significant but not selected. In such a case, the back-traced 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 isolated in time (i.e., appears only briefly in time).

[0240] In other examples, the selection criteria may not correspond to selecting the strongest peak in the range. Instead, it may consider not only the "strength" of the peak, but also the "trace" of peaks that may have occurred in the past, especially peaks that have been identified over time. For example, if a finite state machine (FSM) is used, it may select peaks based on the state of the FSM. The decision threshold may be adaptively (and / or dynamically) calculated based on the state of the FSM.

[0241] The similarity score and / or component similarity score may be calculated (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) based on a pair of temporally adjacent CIs of a TSCI. The pairs may be obtained from the same sliding window or two different sliding windows. The similarity score may also be based on a pair of temporally adjacent or less-adjacent CIs from two different TSCIs. The similarity score and / or component similarity score may be / include time reversal resonating strength (TRRS), correlation, cross-correlation, autocorrelation, correlation indicator, covariance, cross-covariance, autocovariance, dot product of two vectors, distance score, norm, metric, quality metric, signal quality condition, statistical property, discrimination score, neural network, deep learning network, machine learning, training, discrimination, weighted average, preprocessing, denoising, signal conditioning, filtering, time correction, time alignment, phase offset compensation, transform, component-wise operation, feature extraction, finite state machine, and / or another score. Property and / or STI may be determined / calculated based on the similarity score.

[0242] Any threshold may be predetermined, adaptively (and / or dynamically) determined, and / or determined by a finite state machine. Adaptive determination may be based on time, space, location, antenna, path, link, condition, battery life, remaining battery capacity, available power, available computational resources, available network bandwidth, etc.

[0243] A threshold applied to a test statistic to distinguish between two events (or two conditions, or two situations, or two states), A and B, may be determined. Data (e.g., CI, channel state information (CSI), power parameters) may be collected under A and / or under B in a training scenario. A trial statistic may be calculated based on the data. A distribution of the trial statistic under A may be compared to a distribution of the trial statistic under B (a reference distribution), and a threshold may be selected according to some criteria. The criteria may include maximum likelihood estimation (ML), maximum a posteriori probability (MAP), discriminative 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 metric, signal quality condition). The threshold may be adjusted to achieve different sensitivities to A, B, and / or other events / conditions / situations / states. The threshold adjustment may be automatic, semi-automatic, and / or manual. Threshold adjustments may be applied once, occasionally, frequently, periodically, repeatedly, occasionally, sporadically, and / or on-demand. Threshold adjustments may be adaptive (and / or dynamically adjusted). Threshold adjustments may depend on objects, object movement / location / orientation / motion, object characteristics / STI / size / characteristics / traits / habits / behavior, location, at / at / of location, features / fixtures / furniture / barriers / materials / machines / creatures / objects / boundaries / surfaces / media, map, map constraints (or environmental model), events / states / scenes / conditions, time, timing, duration, current state, past history, user, and / or personal preference, etc.

[0244] The stopping criterion (or skip or bypass or blocking or pausing or passing or rejecting criterion) of an iterative algorithm may be that the change in the current parameter (e.g., offset value) in the update in the 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 iteration progresses. With respect to the offset value, the adaptive threshold may be determined based on the task, the initial specific value, 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.

[0245] The local extrema may be determined as a corresponding extremum of the regression function in the regression window. The local extrema may be determined based on a set of time offset values ​​and a set of associated regression function values ​​within the regression window. Each of the set of associated regression function values ​​associated with the set of time offset values ​​may be within a range from the corresponding extremum of the regression function in the regression window.

[0246] Searching for local extrema, robust search, minimization, optimization, statistical optimization, dual 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, computation of variations, optimal control, dynamic programming, mathematical programming, multiobjective optimization, multimodal optimization, disjunctive programming, space mapping, infinite dimensional optimization, heuristics, metaheuristics, convex programming, semidefinite programming, cone programming, second order cone programming, integer programming, quadratic programming, fractional programming, numerical analysis, simplex algorithm, iterative methods, gradient descent, subgradient methods, coordinate gradient methods, conjugate gradient methods, Newton's algorithm, sequential quadratic programming, interior point methods, elliptic methods, reduced gradient methods, quasi-Newton methods, simultaneous perturbation stochastic approximation, interpolation, pattern search methods, line search, non-differential optimization, genetic algorithms, evolutionary algorithms, dynamic relaxation The search for local extrema may involve an objective function, a loss function, a cost function, a utility function, a fitness function, an energy function, and / or an energy function.

[0247] The regression may be performed using a regression function to fit the sampled data (e.g., CIs, CI features, components of CIs) or another function (e.g., an autocorrelation function) in a regression window. The length of the regression window and / or the position of the regression window may be varied 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. The regression analysis may minimize at least one of the following: error, aggregate error, component error, error in a projected domain, error in a selected axis, error in a selected orthogonal axis, absolute error, squared error, absolute deviation, squared deviation, higher-order error (e.g., third-order, fourth-order), robust error (e.g., squared error versus absolute error for smaller magnitude errors and for larger errors, or a first type of error for smaller magnitude errors and a second type of error for larger magnitude errors), another error, a weighted sum (or weighted average) of absolute / squared errors (e.g., in the case of a wireless transmitter with multiple antennas and a wireless receiver with multiple antennas, each pair of transmitter antenna and receiver antenna forms a link), mean absolute error, mean squared error, mean absolute deviation, and / or mean squared deviation. Errors associated with different links may have different weights. One possibility is that some links and / or some components with greater 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 further errors, absolute cost, squared cost, higher-order cost, robust cost, further 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 further costs). The determined regression error can be an absolute error, a squared error, a higher-order error, a robust error, a further 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 further errors.

[0248] The time offset associated with the maximum regression error (or minimum regression error) of the regression function for a particular function within the regression window may be the updated current time offset for the iteration.

[0249] The local extrema can be searched for based on a quantity including the difference between two different errors (e.g., the difference between an absolute error and a squared error), each of which can include 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.

[0250] The quantity may be compared to reference data or a reference distribution, such as an F-distribution, a central F-distribution, another statistical distribution, a threshold, a threshold associated with a probability / histogram, a threshold associated with a probability / histogram of finding a false peak, a threshold associated with an F-distribution, a threshold associated with a central F-distribution, and / or a threshold associated with another statistical distribution.

[0251] The regression window may be determined based on at least one of: a movement of the object (e.g., a change in location / position), a quantity related to the object, at least one characteristic and / or STI of the object related to the movement of the object, an estimated location of a local extremum, a noise characteristic, an estimated noise characteristic, a signal quality metric, an F-distribution, a central F-distribution, another statistical distribution, a threshold, a preset threshold, a threshold related to a probability / histogram, a threshold related to a desired probability, a threshold related to the probability of finding a false peak, a threshold related to an F-distribution, a threshold related to a central F-distribution, a threshold related to another statistical distribution, a condition that the quantity at the window center is a maximum within the regression window, a condition that only one of the local extrema of a particular function for a particular value exists for the first time within the regression window, another regression window, and / or other conditions.

[0252] The width of the regression window can be determined based on the particular local extrema to be searched for, including a first local maximum, a second local maximum, a higher-order local maximum, a first local maximum with a positive time offset, a second local maximum with a positive time offset, a higher-order local maximum with a positive time offset, a first local maximum with a negative time offset, a second local maximum with a negative time offset, a second local maximum with a negative time offset, a higher-order local maximum with a negative time offset, a first local minimum, a second local minimum, a higher-order local minimum, a first local minimum with a positive time offset, The local minimum may include a second local minimum with a positive time offset, a higher-order local minimum with a positive time offset, a first local minimum with a negative time offset, a second local minimum with a negative time offset, a higher-order local minimum with a negative time offset, a first local extremum, a second local extremum, a higher-order local extremum, a first local extremum with a positive time offset, a second local extremum with a positive time offset, a first local extremum with a negative time offset, a second local extremum with a negative time offset, and a higher-order extremum with a negative and / or negative time offset.

[0253] The current parameters (e.g., time offset value) may be initialized based on a target value, a target profile, a trend, a past trend, a current trend, a target velocity, a velocity profile, a target velocity profile, a past velocity trend, an object's motion or movement (e.g., a change in location / position), at least one characteristic and / or STI of the object associated with the object's motion, a position quantity of the object, an initial velocity of the object associated with the object's motion, a predefined value, an initial width and duration of the regression window, a value based on the signal's carrier frequency, a value based on the signal's subcarrier frequency, a signal's bandwidth, an antenna's aggregate value associated with the channel, noise characteristics, a signal h metric, and / or an 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.

[0254] In the presentation, information may be displayed along with a map (or environmental model) of the location. Information may include location, zone, area, region, coverage area, corrected location, approximate location, location wrt a map of the location, location wrt a segmented location, direction, route, route wrt a map and / or segmentation, trace (e.g., location within a time window such as the last 5 seconds, or the last 10 seconds, where the time window duration may be adaptively (and / or dynamically) adjusted, and the time window duration may be adaptively (and / or dynamically) adjusted for speed, acceleration), route history, approximate regions / zones along the route, history / summary of past locations, history of past locations of interest, frequently visited areas, customer traffic, herd distribution, herd behavior, herd control information, speed, acceleration, movement statistics, respiration rate, heart rate, presence / absence of movement, Presence or absence of a person, pet, or object, presence or absence of vital signs, gesture control (controlling a device using gestures), location-based gesture control, location-based operation information, identity (ID) or identifier of the object of interest (e.g., pet, person, self-guided machine / device, vehicle, drone, car, boat, bicycle, unmanned car, machine with fan, air conditioner, TV, machine with moving parts), user identification (e.g., person), user information, position / speed / acceleration / direction / movement / gesture / gesture control / movement trace, user ID or identifier, user activity, user state, user sleep / rest characteristics, user emotional state, user vital signs, location The event information may include environmental information, location weather information, earthquake, explosion, storm, rain, fire, temperature, collision, impact, vibration, events, door opening events, door slamming events, window opening events, window slamming events, fall events, combustion events, freezing events, water-related events, wind-related events, air movement events, accident events, quasi-periodic events (e.g., running on a treadmill, hopping, skipping rope, somersaults, etc.), recurring events, swarming events, vehicle events, user gestures (e.g., hand gestures, arm gestures, foot gestures, leg gestures, body gestures, head gestures, face gestures, mouth gestures, eye gestures, etc.).Location may be two-dimensional (e.g., using two-dimensional coordinates), three-dimensional (e.g., using three-dimensional coordinates). Location may be relative (e.g., with respect to a map or environmental model) or relational (e.g., halfway between point A and point B, around a corner, upstairs, on a table, on the ceiling, on the floor, on a couch, close to point A, a distance R from point A, within a radius of R from point A, etc.). Location may be expressed in Cartesian coordinates, polar coordinates, and / or another representation.

[0255] 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. The size may change over time. The orientation of the symbol may change over time. The symbol may be a number reflecting an instantaneous quantity (e.g., user's vital signs / respiratory rate / heart rate / gesture / status / condition / action / movement, temperature, network traffic, network connectivity, device / machine status, remaining device power, device state, etc.). The rate of change, size, orientation, color, intensity, and / or symbol may reflect the respective movement. Information may be presented visually and / or verbally explained (e.g., using pre-recorded audio or speech synthesis). Information may be written in text. Information may also be presented in a mechanical manner (e.g., animated gadgets, moving parts).

[0256] The user interface (UI) device may be a smartphone (e.g., iPhone, Android phone), a tablet (e.g., iPad), a laptop (e.g., notebook computer), a personal computer (PC), a device with a graphic user interface (GUI), a smart speaker, a device with voice / sound / speaker capabilities, a virtual reality (VR) device, an augmented reality (AR) device, a smart car, an in-car display, a voice assistant, an in-car voice assistant, etc. The map (or environmental model) may be two-dimensional, three-dimensional, and / or higher dimensional. (e.g., a time-varying two-dimensional / three-dimensional map / environment 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 a 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, wiring, cable runs, air ducts, crawl spaces, ceiling layouts, and / or underground layouts. A location can be segmented / subdivided / regionalized / grouped into multiple zones / regions / geographical areas / sectors / sections / territories / districts / administrative districts / sites / neighborhoods / areas / stretches / open spaces, such as bedrooms, living rooms, storage rooms, walkways, kitchens, dining rooms, foyers, garages, first floors, second floors, restrooms, offices, conference rooms, reception areas, various office areas, various warehouse areas, various facility areas, etc. The segments / regions / regions can be presented on a map / model. Different regions may be color-coded. Different regions may be presented with characteristics (e.g., color, brightness, color intensity, texture, animation, blinking, blink rate, etc.). The logical segmentation of a location can be performed using at least one heterogeneous Type-2 device, or server (e.g., hub device), or cloud server, etc.

[0257] Here is an example of the disclosed system, device, and method. Stephan and his family want to install the disclosed wireless motion detection system to detect movement in their 2,000-square-foot, two-story townhouse in Seattle, Washington. Because his house is two stories, Stephan decides to use one Type 2 device (named A) and two Type 1 devices (named B and C) on the first floor. The first floor is centered around three rooms: the kitchen, dining room, and living room, with the dining room in the middle and arranged in a straight line. The kitchen and living room are on opposite sides of the house. He places a Type 2 device (A) in the dining room, one Type 1 device (B) in the kitchen, and another Type 1 device (C) in the living room. With this device installation, he specifically uses the motion detection system to partition the first floor into three zones: the dining room, the living room, and the kitchen. When motion is detected by the AB pair and the AC pair, the system analyzes the motion information and associates the motion with one of three zones.

[0258] When Stefan and his family go away for the weekend (e.g., going camping for a long weekend), Stefan turns on the motion detection system using a mobile phone app (e.g., Android phone app or iPhone app). When the system detects motion, an alert signal is sent to Stefan (e.g., SMS text message, email, push message to the mobile phone app, etc.). If Stefan pays a monthly fee (e.g., $10 / month), a service company (e.g., a security company) receives the alert signal through a wired network (e.g., broadband) or wireless network (e.g., home WiFi, LTE, 3G, 2.5G, etc.) and performs security procedures for Stefan (e.g., calling him to check the problem, sending someone to check the house, contacting the police on Stefan's behalf, etc.). Stefan loves his elderly mother and is concerned about her well-being when he is home alone. When his mother is home alone while the rest of the family is out (e.g., going to work, shopping, or on vacation), Stephan uses his mobile app to turn on the motion detection system to ensure his mother is okay. He then uses the mobile app to monitor his mother's movements around the house. When Stephan uses the mobile app to see his mother moving around the house between three areas, according to her daily routine, Stephan knows that his mother is okay. Stephan is grateful that the motion detection system can help him monitor his mother's well-being while he is away from home.

[0259] On a typical day, his mother wakes up around 7:00 AM. She plans to make breakfast in the kitchen in about 20 minutes. She then eats breakfast in the dining room for about 30 minutes. Then, she does her daily exercise in the living room before sitting on the sofa and watching her favorite TV show. The motion detection system allows Stephan to see the timing of movements in each of three areas of the house. When the movements fit into her daily routine, Stephan knows roughly that his mother should be doing well. However, if the movement pattern seems abnormal (e.g., no movement until 10:00 AM, staying in the kitchen too long, remaining motionless for long periods of time, etc.), Stephan suspects something is wrong and calls his mother to check on her. Stephan may even ask someone (e.g., family member, neighbor, paid staff member, friend, social worker, service provider) to check on his mother.

[0260] Occasionally, Stephan feels the need to reposition the Type 2 device. He simply unplugs the device from its original AC power plug and plugs it into another AC power plug. He is pleased that the wireless motion detection system is plug-and-play and relocation does not affect the system's operation. Once powered on, it works immediately. On another occasion, Stephan is so confident that our wireless motion detection system can indeed detect motion with very high accuracy and very low alarms that he can actually use the mobile app to monitor motion on the first floor. He decides to install a similar configuration (i.e., one Type 2 device and two Type 1 devices) on the second floor to monitor the bedrooms on the second floor. Again, he finds system setup extremely easy, as he simply plugs the Type 2 and Type 1 devices into the second floor's AC power plugs. No special installation is required. He can then use the same mobile app to monitor motion on both the first and second floors. Each Type 2 device on the first and second floors can interact with all Type 1 devices on both the first and second floors. Stephen is happy to see that as he doubles his investment in Type 1 and Type 2 devices, he has more than double the capacity of the combined system.

[0261] According to various embodiments, each CI (CI) may include at least one of channel state information (CSI), frequency domain CSI, a frequency representation of CSI, frequency domain CSI associated with at least one subband, time domain CSI, intra-domain CSI, a channel response, a channel response estimate, a channel impulse response (CIR), a channel frequency response (CFR), channel characteristics, a channel filter response, CSI of a wireless multipath channel, information of a wireless multipath channel, a timestamp, auxiliary information, data, metadata, user data, account data, access data, security data, session data, status data, management data, family data, identity (ID), identifier, device data, network data, proximity 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 includes hardware components (e.g., a wireless transmitter / receiver with an antenna, analog circuitry, a power supply, a processor, a memory) and corresponding software components. According to various embodiments of the present disclosure, the disclosed system includes a Bot (referred to as a Type 1 device) and an Origin (referred to as a Type 2 device) for vital signs detection and monitoring, each device comprising a transceiver, a processor, and a memory.

[0262] The disclosed system can be applied in many ways. In one example, a Type 1 device (transmitter) may be a small WiFi-enabled device placed 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. In one example, a Type 2 (receiver) may be a WiFi-enabled device placed 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 be 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 (breathing) of living infants. 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 automobiles to monitor passenger and driver health, detect driver sleep, and detect any babies left in the car. 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 can be deployed by emergency services in disaster areas to search for victims trapped in rubble. Type 1 and Type 2 devices can be placed in an area to detect the breathing of any intruders. Non-wearable wireless respiratory monitoring has many applications.

[0263] The hardware modules may be configured to include Type 1 transceivers and / or Type 2 transceivers and may be sold / used under variable brands to design, build, and sell final commercial products. The products using the disclosed systems and / or methods may be home / office security products, sleep monitoring products, WiFi products, mesh products, TVs, STBs, entertainment systems, HiFi, speakers, home appliances, lamps, stoves, ovens, microwaves, tables, chairs, beds, shelves, tools, appliances, torches, vacuum cleaners, smoke detectors, sofas, pianos, fans, doors, windows, door / window handles, locks, smoke detection equipment, car accessories, computing devices, office supplies, air conditioners, heaters, pipes, connectors, surveillance cameras, access points, computer equipment, mobile devices, LTE devices, 3G / 4G / 5G / 6G devices, UMTS devices, 3GPP devices, GSM devices, EDGE devices, TDMA devices, FDMA devices, CDMA devices, WCDMA devices, TD-SCDMA devices, gaming devices, eyeglasses, glass panels, VR goggles, necklaces, watches, waistbands, belts, wallets, pens, hats, implanted devices, tags, parking tickets, smartphones, etc.

[0264] The summarization may include: analysis, output response, selected time window, sub-sampling, transformation, and / or projection. The presentation may include presenting at least one of a month / week / day view, simplified / detailed view, cross-sectional view, small / large form factor view, color-coded view, comparison view, summary view, video, web view, audio announcement, and another presentation related to the cyclical / recurring characteristics of the recurring motion.

[0265] A Type 1 / Type 2 device is any device that includes an antenna, a device having an antenna, a device having a housing (e.g., for a radio, antenna, data / signal processing unit, radio IC, circuitry), a device that interfaces / attaches / connects / links to another device / system / computer / phone / network / data aggregator, a device having a user interface (UI) / graphical UI / display, a device having a wireless transceiver, a device having a wireless transmitter, a device having a wireless receiver, an Internet of Things (IoT) device, a device having a wireless network, a device having both wired and wireless network capabilities, a device having a wireless integrated circuit (IC), a Wi-Fi device, a device having a Wi-Fi chip (e.g., compliant with 802.11a / b / g / n / ac / ax standards), a Wi-Fi access point (AP), a Wi-Fi client, a WiFi router, a Wi-Fi repeater, a WiFi hub, a WiFi mesh network router / hub / AP, a wireless mesh network router, an ad-hoc network device, a wireless mesh network device, a mobile device (e.g., 2G / 2.5G / 3G / 3G).5G / 4G / LTE / 5G / 6G / 7G, UMTS, 3GPP, GSM, EDGE, TDMA, FDMA, CDMA, WCDMA, TD-SCDMA), cellular device, base station, mobile network base station, mobile network hub, mobile network compatible device, LTE device, device with LTE module, mobile module (e.g., circuit board with mobile-enabling chip (IC) such as Wi-Fi chip, LTE chip, BLE chip), device with mobile module, smartphone, companion device for smartphone (e.g., dongle, attachment, plug-in), dedicated device, plug-in device, AC-powered device, battery-powered device, device with processor / memory / instruction set, smart device / gadget / An item can be a watch, stationery, pen, user interface, paper, mat, camera, television (TV), set-top box, microphone, speaker, refrigerator, oven, machine, phone, wallet, furniture, door, window, ceiling, floor, wall, table, chair, bed, nightstand, air conditioner, heater, pipe, duct, cable, carpet, decoration, gadget, USB device, plug, dongle, lamp / light, tile, ornament, bottle, vehicle, automobile, AGV, drone, robot, laptop, tablet, computer, hard disk, network card, equipment, racket, ball, shoe, wearable device, clothing, eyeglasses, hat, necklace, food, pill, small device that moves within a living being's body (e.g., blood vessels, lymph, digestive system), and / or another device. The Type 1 device and / or Type 2 device can be communicatively coupled to the Internet, another device that accesses 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 and / or Type 2 devices may operate under local control, may be controlled by another device via a wired or wireless connection, may operate automatically, or may be controlled by a central system located remotely (e.g., away from the home).

[0266] In one embodiment, a Type-B device may be a transceiver that may perform as both an origin (Type-2 device, Rx device) and a bot (Type-1 device, Tx device), i.e., a Type-B device may be both a Type-1 (Tx) device and a Type-2 (Rx) device (e.g., simultaneously or alternately), such as a mesh device, mesh router, etc. In one embodiment, a Type-A device may be a transceiver that may function only as a bot (Tx device), i.e., it may be only a Type-1 device or only a Tx, such as a simple IoT device. It may have the functionality of an origin (Type-2 device, Rx device), but in some embodiment, it functions only as a bot. All Type-A and Type-B devices form a tree structure. The root may be a Type-B device that has access to a network (e.g., the Internet). For example, it may be connected to broadband service 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 may be a root node, a non-leaf node, or a leaf node.

[0267] In one example, Peter enjoys using GPS-based mapping navigation software (GPS app) on his smartphone when traveling in the United States, primarily the New York metropolitan area where he lives. The GPS app allows him to get to any new place without getting lost (by entering his destination into the app). The GPS app allows him to see his location on a map, whether traveling by car or on foot. This can then give him real-time traffic information so he can reach his destination in the shortest time, or without paying tolls or getting on the highway. It can also reroute if he makes a wrong turn.

[0268] However, GPS apps don't work in many of Peter's favorite indoor locations in New York City, including shopping malls, sports stadiums, hospitals (to visit his sick grandmother), and JFK Airport. Peter hopes someone can come up with good indoor map navigation software that works well both indoors and outdoors.

[0269] Peter is pleased to learn that Origin Wireless (OW) has partnered with a telecommunications company to provide several indoor location / tracking / navigation solutions. He quickly downloads the software package and installs the OW client app on his mobile device. He learns that the telecommunications company recently deployed OW beacon software on numerous of the carrier's WiFi routers (e.g., Type 1 heterogeneous wireless devices, stationary) that have been widely deployed around major U.S. cities (including New York), and indeed throughout the United States. The OW beacon software successfully transforms the carrier's WiFi routers into beacon devices for transmitting GPS-like navigation WiFi beacon signals to mobile devices (e.g., Type 2 heterogeneous wireless devices with a processor, memory, and instruction set) anywhere in the United States. When Peter is at a shopping mall, the OW client app controls Peter's mobile device to receive navigation WiFi beacon signals from nearby telecommunications company WiFi routers in the mall and calculates spatial-temporal information (STI) of his current location using the methods and systems disclosed herein. With the updated location, the OW client app connects to a server via the network (Internet) to update the map according to Peter's movements.

[0270] Peter likes the OW client app because it often provides coupons for nearby stores, shopping tips, current sale items, daily specials, and many points of interest (e.g., break rooms, water coolers, restaurants, food courts, movie theaters, mall exits). When he clicks a button, stores in the mall are labeled in the GUI. When he clicks on the ABC restaurant he wants to visit, he can see a lot of related information, such as opening hours, wait times, and other information, such as promotions, reviews, and ratings. He can join the queue for a table for two. While waiting in the parking lot for his girlfriend to arrive, he can also browse the menu and choose what to order. When he clicks on the map icon, a navigation route is immediately generated, which will take Peter to the restaurant in the shortest time possible, avoiding rush-hour congestion and closed areas due to renovations. He especially likes how the OW client app can seamlessly transition between using GPS to navigate outdoors and the OW system to navigate indoors. It would be the perfect date night.

[0271] This disclosure introduces EasiTrack, an indoor tracking device that achieves decimeter accuracy using a single, commodity WiFi access point (AP) and can be deployed at near-zero cost on a large scale. EasiTrack makes two key technical contributions. First, it incorporates an algorithm that can accurately estimate a target's travel distance using the RF signals it receives. Second, EasiTrack devised a map-augmented tracking algorithm that outputs fine-grained locations by combining distance estimates with indoor maps that are now ubiquitously available. This teaching discloses a fully functional real-time system centered around a satellite-like architecture that allows EasiTrack to support any number of clients. EasiTrack has been deployed in different scenarios (including offices, hotels, museums, and manufacturing facilities) to track both humans and machines. Results show that EasiTrack achieves a median location accuracy of 0.58 m and a 90th percentile of 1.33 m in both line-of-sight and non-line-of-sight scenarios, supporting a large 50 m x 50 m coverage area using a single AP. EasiTrack has also been proven to be easy to deploy across many buildings at little cost.

[0272] Thanks to the widespread availability of already deployed WiFi infrastructure, indoor location systems using commercial-off-the-shelf (COTS) WiFi are one of the most promising solutions for ubiquitous tracking. They should ideally meet the following four requirements:

[0273] Low (zero) cost: They should be easy to install and deploy with little or ideally zero effort. Ideally, they should be able to locate a mobile device using a single arbitrarily placed AP (without any knowledge of location, orientation, etc.) without requiring any hardware modifications on either side.

[0274] Scalable: Systems must be scalable in two folds: they must be portable to many different buildings and, like GPS, must support large populations of simultaneous mobile devices, both at negligible cost.

[0275] Large Coverage: The system should cover a large area with line of sight (LOS) to the AP or behind multiple walls, close or far away, with consistent high accuracy.

[0276] Accuracy: They must be able to provide sub-meter accuracy, as required for many applications. Such accuracy is needed, for example, to enable navigation within a building, directing customers to products in a store, or directing a robot to a workstation.

[0277] If all the above requirements are met, one can imagine the indoor positioning system becoming a ubiquitous "indoor GPS" that is made available anywhere with a WiFi signal and for any device with a commodity WiFi chip.

[0278] No existing technology meets all these requirements. Recent techniques based on angle of arrival (AoA) or time of flight (ToF) have been able to produce sub-meter median accuracy. However, they typically require large bandwidth (for ToF) and many phased antennas (for AoA) for good performance, both of which are limited by COTS WiFi. Furthermore, they all require precise placement of multiple APs (to obtain their reference position and orientation). Fingerprint-based approaches are neither sufficiently accurate nor cost-effective due to expensive pre-training. Other techniques combined with inertial sensors are deployable but have limited accuracy. The latest release, 802.11mc, supports round-trip time measurements, but does not provide sub-meter resolution and still requires multiple APs for multilateration.

[0279] The present teachings disclose EasiTrack, an indoor tracking system that meets all four of the above requirements. EasiTrack achieves sub-meter accuracy in both line-of-sight and non-line-of-sight (NLOS) scenarios using a single AP without knowledge of its location. It can be easily deployed at scale using a digital floorplan of the area of ​​interest without extraneous input, which is required for most location-based services anyway and is ubiquitously available. EasiTrack's architecture is centered around a satellite-like design that supports a large number of simultaneous clients without impacting channel capacity and maintains privacy because clients do not need to announce their presence but simply passively listen to APs.

[0280] As a client moves, EasiTrack first estimates the distance traveled from its measured channel state information (CSI) and infers the direction of movement from its built-in inertial sensors. It then employs a novel map-augmented tracking algorithm to incorporate the distance and direction estimation, outputting a fine-grained location. Specifically, EasiTrack advances the state-of-the-art in tracking in two distinct ways:

[0281] (1) Multipath-Based Distance Estimation: EasiTrack first contributes to a super-resolution distance estimation algorithm, which accurately estimates the incremental displacement of a mobile radio using only its own measured CSI. The unique insight disclosed is that when an array translates, two of its antennas may traverse the same location in space, one preceding the other by a time delay, during which the array will have moved a distance exactly equal to the antenna separation. Therefore, by using the abundant multipath indoors as a unique position profile, we can design an algorithm that continuously tracks the time delay. The movement speed can be calculated by dividing the antenna separation by the time delay, and correspondingly, the movement distance can be derived by integrating the speed over time. The algorithm is highly accurate, yet is only suitable for relatively constrained movements such as shopping carts, robots, and automated guided vehicles (AGVs). To enable EasiTrack to track unconstrained humans as well, we can further incorporate recent techniques developed to infer movement distance from CSI for unconstrained movements. Furthermore, we can design a universal architecture that allows switching between the two approaches for different scenarios. Instead of solving and dealing with multipath as in previous work by truly exploiting them, EasiTrack's range estimation is position independent and works under both LOS and NLOS conditions.

[0282] (2) Map-Augmented Location Tracking: While distance estimation is somewhat accurate, it does not guarantee sub-meter tracking accuracy when combined with the orientation obtained by inertial sensors, which suffers from significant cumulative errors. EasiTrack employs indoor maps to address these uncertainties. Imagine Google Maps using outdoor road maps to improve GPS accuracy and place cars on the correct roads. Similarly, indoor maps impose geometric constraints on the target's movement within a building. For example, users cannot walk through walls.

[0283] This observation led us to design a map augmentation algorithm for accurate indoor tracking. The disclosed algorithm models the indoor map as a weighted graph and feeds it into a novel graph-based particle filter (GPF), which jointly accounts for distance and orientation errors and outputs accurate locations. The graph-based model allows EasiTrack to use a small number of particles (e.g., 50), thereby enabling it to run in real time even on resource-constrained devices. Furthermore, the disclosed GPF simply requires information about accessible and inaccessible areas from the map, which can be extracted directly from flat floor plan images. It does not require structured knowledge such as corridors, intersections, doors, or rooms. Therefore, EasiTrack can easily scale to many buildings with little cost.

[0284] A fully functional EasiTrack real-time system, including a hardware prototype and software suite using a commodity WiFi chipset, was constructed. To comprehensively evaluate the accuracy and ease of deployment, EasiTrack was deployed in diverse environments for tracking both humans and machines, including three office buildings, one museum, one hotel, and two manufacturing facilities. Key results revealed the following: 1) EasiTrack achieved a median position error of 0.58 m and a 90th percentile error of 1.33 m for tracking objects, and a median error of 0.70 m and a 90th percentile error of 1.97 m for tracking humans, under both LOS and NLOS conditions with a single AP; 2) The cost of deploying EasiTrack in a new building is nearly zero. Once a map is provided, APs can be set up in minutes at any location to provide coverage; and 3) EasiTrack can provide large tracking coverage comparable to the signal coverage of an AP. In the disclosed deployment, this allows one AP to track an area of ​​50m x 50m.

[0285] The disclosed EasiTrack is an indoor tracking system that achieves sub-meter accuracy and scales to large buildings at (near) zero cost. EasiTrack also contributes to a CSI-based distance estimation algorithm and a map-augmented tracking algorithm, each of which can separately benefit existing technologies. Figure 1 shows the overall architecture in which EasiTrack passively collects CSI from one AP and records it with an inertial sensor embedded in the mobile client. It first calculates distance traveled from the CSI, leveraging abundant multipath and orientation from the IMU. This then fuses the two estimation types for continuous tracking, combined with an indoor map. This architecture allows EasiTrack to function like an "indoor GPS" (but without the need for multiple "satellites") supporting a large client population. Figure 1 shows an overview of the EasiTrack architecture. The left part shows EasiTrack's satellite-like protocol. In an exemplary usage scenario, a client, which could be a mobile, wearable, robot, automated guided vehicle (AGV), or any other electronic device equipped with a Wi-Fi radio and an inertial sensor (IMU), passively listens to the AP and measures CSI from incoming packets. The client also reads its built-in inertial sensor. The EasiTrack core engine runs on the client, inferring distance and heading from the measurements and incorporating them together to track the client's continuous location. The AP is simple; it only periodically transmits signals that can be detected by the client. This simple-core, complex-edge architecture design offers significant advantages: 1) Scalable: It enables simultaneous location determination of many clients without impacting channel capacity. 2) Privacy: Clients do not need to emit any signals that might reveal their presence, preserving user privacy and thereby preventing others from probing their location. 3) Flexible: The client can run different tracking algorithms as needed, all under the same environment. The right part of Figure 1 shows the EasiTrack workflow on a mobile client.There are two main modules: distance estimation and map augmented tracking.

[0286] Distance Estimation: EasiTrack includes two unique approaches to estimate the distance traveled by a user: Antenna Alignment (AA) and Focusing Ball (FB). Both approaches utilize abundant multipath to estimate distance traveled, but are applicable to various tracking scenarios. AA is highly accurate and suitable for tracking objects with relatively constrained motion, such as shopping carts, robots, and industrial AGVs. Meanwhile, FB is less accurate than AA, but is more general and superior for tracking unconstrained humans. EasiTrack can switch between the two approaches on the fly as needed.

[0287] Map-Augmented Tracking: EasiTrack tracks a user's location by fusing distance traveled estimated from CSI with direction of travel measured by an inertial sensor. While the logic flow is similar to traditional autonomous navigation, the present teachings disclose a novel design that incorporates an indoor map for accurate tracking in a scalable manner. EasiTrack takes an indoor map as input and converts it into a weighted graph. The output graph is then fed into a novel graph-based particle filter (GPF), which exploits the geometric constraints imposed by the map to jointly learn the target's precise 2D position and orientation as it moves. The localization results are then displayed to the user along with the map. Because the disclosed GPF uses only a regular indoor map (e.g., a floor plan image), it can easily scale to large buildings at little cost.

[0288] Accurate estimation of a device's distance traveled has been a major bottleneck in indoor tracking and inertial sensing. Previously, IMUs have been widely used to multiply the number of steps by stride length. However, this category of approaches is well-known to produce large errors due to the difficulty of estimating stride length, which varies significantly over time and across subjects. The present teachings disclose two different approaches that can estimate the incremental displacement of a moving device by taking advantage of the abundant multipath indoors.

[0289] The core idea of ​​AA-based distance estimation is as follows: Consider two antenna arrays moving along a line connecting them (called the antenna array line). FIG. 2 illustrates an example antenna alignment according to some embodiments of the present disclosure. As the arrays move, after a displacement of Δd, antenna 1 arrives at the position at t_2 where antenna 2 was at t_0. Thus, the moving speed can be derived as Δd / (t_2-t_0). Similarly, the instantaneous speed can be estimated for all subsequent time points, which is Δd / (t_k-t_(k-2)) for the current time point t_k. For ease of display, antenna 2 is plotted upside down.

[0290] As shown in Figure 2, as the radio moves, one antenna directly follows the trajectory of the other. In other words, the two antennas move through the same spatial location and therefore observe similar (ideally identical) CSI series one after the other, with a certain time delay Δt. Clearly, during Δt, the array moved a distance Δd equal to the antenna separation. Δd is fixed and known for a given array. Therefore, if we can estimate the accurate time delay Δt, we obtain an accurate velocity estimate as v = Δd / Δt. By continuously estimating Δt, we could derive the real-time velocity along the entire trajectory. The distance traveled is then calculated as: TIFF0007623106000001.tif10133

[0291] The above intuition leads directly to a novel mechanism named antenna alignment for mobile distance estimation. The key is to determine the exact time delay Δt, i.e., the time difference when an antenna arrives at a location traversed by another antenna (i.e., the two antennas become "aligned"). Due to abundant multipath indoors, the CSI measured at one location can serve as a unique location profile. The task is then equivalent to identifying the exact time difference when one antenna observes the most similar channel measurement result to another. In the following, we first introduce a similarity metric for the CSI and then present a method to accurately estimate the time delay.

[0292] Time-Reversed Resonance Strength: EasiTrack employs a similarity metric called time-reversed resonance strength (TRRS), which is calculated for two CSIs H1 and H2 as follows: TIFF0007623106000002.tif1581 where * H denotes the conjugate transpose. Clearly, η(H1,H2)∈[0,1], which achieves 1 if and only if H1=aH2, where a≠0 is some complex scaling factor. If H1 and H2 are both normalized, then TRRS is the square of their dot product, TIFF0007623106000003.tif884η, while expressed in an unsophisticated form, serves as a useful measure and has been used for fingerprinting with centimeter accuracy. Time reversal is a physical phenomenon in which the energy of a transmitted signal, when combined with its time-reversed and conjugate counterpart, is focused in both the spatial and time domains. TRRS was first specified in terms of the channel impulse response for location discrimination. It can be adapted to an equivalent formulation for the channel frequency response.

[0293] To make it more discriminative and robust, we can exploit the spatial diversity from multiple transmit antennas. Let the CSI measured at the ith antenna at time t be H i (t)={H i,1 (t),H i,2(t),···,H i,s (t)} is expressed as, where H i,k where (t) is the CSI between the ith receive antenna and the kth transmit antenna, and S is the number of transmit antennas. Then, the TRRS between the CSI measured by the ith and jth receive antennas is given by: TIFF0007623106000004.tif11113

[0294] Averaging allows for a larger effective bandwidth from multiple transmit antennas for a more reliable similarity measure, while avoiding the need to synchronize the CSI within it.

[0295] Time delay estimation mechanism: To pinpoint the exact time delay at which two antennas i and j are spatially aligned, we use [H j (tl),···,H j (t),···,H j for antenna j measured within a time window of 2l, denoted as (t+l)], i One can perform a local mapping between CSI snapshots of antenna i at time t, denoted as (t). Ideally, only one specific measurement, Hj(t+Δt), would yield a very large maximum similarity, indicating that antennas i and j observe the same channel (at the same location) with a time difference of Δt. Figure 3A shows the TRRS trend [η(H i (t),H j (t+k)),k=-l,···l] trend. As can be seen, we can identify the time delay by looking for the maximum TRRS peak. Formally, we can have: TIFF0007623106000005.tif1291

[0296] In practice, two additional techniques are employed to improve the accuracy of time delay estimation. First, as shown in Figure 3A, a regression can be performed around the TRRS peak area to find finer-grained time lags, which resolves the resolution limit by the sampling rate. Second, instead of looking at individual time points, a TRRS matrix over time can be considered (as shown in Figure 3B), where each column represents the TRRS trend (as in Figure 3A) for that particular time point. Based on the TRRS matrix, dynamic programming can be used to robustly find consecutive TRRS peaks over time.

[0297] AA involves CSI mapping, but because the mapping is performed over a transient window, it does not require pre-training and is not affected by environmental dynamics. Furthermore, unlike fingerprinting, which requires finding the best match across all records, EasiTrack is only interested in relative TRR trends over a short time window, which allows for higher resolution at the sub-centimeter level (Figure 3A).

[0298] Practical Motion Handling: In principle, to estimate distance using AA, the target would need to move along the antenna array line, which is impractical to enforce. Fortunately, EasiTrack relaxes this constraint, again because it considers relative TRRS trends rather than absolute TRRS values. Figure 3C shows an example of the TRRS matrix under different deviation angles. As can be seen, at smaller deviation angles, a clear similarity peak still exists, but the peak value is slightly reduced, which does not affect distance estimation. According to the disclosed experimental measurements, EasiTrack can tolerate deviations up to approximately 15° when the adjacent antenna separation is 4 centimeters. This is a crucial feature for practical applications, such as realistic tracking of targets such as robots, AGVs, shopping carts, etc., allowing target movement distance to be estimated without strictly following the antenna array line.

[0299] AA is inappropriate for tracking humans because it is impractical to expect a person to carry a device and move it along a line while walking naturally. To estimate the distance traveled by a pedestrian, we can incorporate a recently developed method based on the focusing ball effect, which discovers and models critical statistical properties of indoor RF signal propagation. The spatial distribution of the multipath profile, represented by the CSI, depends on a deterministic function of spatial separation. This provides a basis for inversely inferring the distance traveled from the CSI distribution each time the device moves. We briefly review the key results below and leave further details to the reader.

[0300] Consider the TRRS in equation (2) for two CSIs measured by the same receive antenna i at two locations L_0 and L, denoted as H_i(t;L_0) and H_i(t;L). TIFF0007623106000006.tif11103where λ is the wavelength, d is the distance between L0 and L, and j0(x) is the zeroth-order Bessel function of the first kind. From equation (4), the travel distance can be estimated by calculating the TRRS distribution for one CSI and the subsequent measurements on the receiver.

[0301] Compared to AA, the accuracy of FB for distance estimation will be lower because TRRS measurements may actually deviate slightly from the theoretical distribution due to the non-uniformity of the multipath distribution. The FB method excels in independence of movement direction and location, as these characteristics arise from the nature of multiple indoor multipaths. As a result, it is preferable for human tracking and is therefore adopted in EasiTrack. A key feature of both approaches is that, unlike traditional systems that attempt to resolve and counter multipath for tracking, EasiTrack uniquely exploits multiple multipaths together. The more multipaths there are, the better the performance it achieves. As a result, distance can be estimated independently of many factors, including position, orientation, and environment.

[0302] In addition to distance traveled, EasiTrack also requires direction of travel to continuously track a target. EasiTrack utilizes ubiquitous inertial sensors and follows standard practices to derive orientation information. In particular, it primarily employs gyroscopes to measure rotation angles and accumulate measurements to estimate the current heading. For two-dimensional movement, the gyroscope readings can be projected onto the direction of gravity based on the device's local coordinate system. The direction of gravity can be inferred from the accelerometer before the device begins to move. In this way, the device can be in any position and does not need to be placed horizontally.

[0303] Inertial sensors are also utilized to detect motion and further determine whether the target is turning, which will be used later in tracking. Intuitively, position can be calculated directly by integrating successive measurements of distance traveled and orientation, in a manner similar to dead reckoning. However, this approach suffers from significant errors due to erroneous orientation measured by inertial sensors. In EasiTrack, indoor maps are incorporated to achieve accurate tracking using coarse-grained orientation and distance observations. Two opportunities motivate the disclosed design. First, indoor maps impose effective geometric constraints on target motion. Second, digital maps are ubiquitously available today. In particular, industrial efforts have been made to calibrate indoor maps for public locations. Research advances are also facilitating the automatic generation of digital maps through mobile crowdsourcing.

[0304] While road maps are used to correct for GPS errors, the present teachings utilize indoor maps in a lightweight and scalable manner. Specifically, we can devise a graph-based particle filter based on a novel graph-based representation of the indoor map. Before diving deep into the disclosed GPF, the graph representation is shown below.

[0305] As input, it can take a standard indoor map in the form of a plain image. This does not require knowledge of structured information, e.g., walls, corridors, and rooms, which is difficult to obtain from a map. Instead, it can simply extract access information that tells whether a location (pixel) is accessible or not. Thus, the map can, for example, be a simple image with a specific color (e.g., black walls) indicating inaccessible pixels, in the most typical form of an indoor map.

[0306] Map discretization: To reduce computational complexity and achieve real-time tracking, a map can be converted into a weighted graph. First, the space (i.e., the map) may be sampled by a uniformly distributed set of points with a particular grid size s, where each point becomes a vertex. Every vertex may be accessible or inaccessible depending on its location on the map (i.e., pixel in the image). Then, an edge may be added between a vertex and each of its neighboring vertices, provided that the edge does not intersect with an obstacle. Each edge is Weighted by TIFF0007623106000007.tif8146. By doing so, we may construct a weighted graph G=(V,E) as shown in Figure 4A. The sampling density s trades off grid resolution and number of vertices. To target sub-meter accuracy, s should be less than 1 m and adapts to different maps. Figures 35A-35B show an exemplary graph-based map representation according to some embodiments of the present invention. For clarity, only a partial map is displayed.

[0307] Reachability Matrix: After converting the map into a weighted graph, we can extract intrinsic geometric properties by generating a reachability matrix M, which represents the pairwise reachability distance of |V| vertices. Each element m_ij of the matrix represents the reachability distance between vertices V_i and V_j, which is defined as the shortest path distance. m_ij is infinite if there is no obstacle-free path between V_i and V_j. Figure 4B shows an example of reachability for one vertex, which essentially shows all shortest paths starting from it to all other vertices.

[0308] The reachable distance is different from the Euclidean distance, but is rather the walking distance between two locations. In practice, the reachable distance is usually larger than the Euclidean distance because the straight line connecting two locations is frequently blocked indoors. To avoid M becoming too large when the tracking area is large, in practice, we can ignore vertex pairs that are too far apart and make M a relatively sparse matrix. For example, only elements with m_ij ≤ 10m are stored.

[0309] The method for designing and implementing an extended particle filter using a graphic map is presented below along with preprocessed results. Given N particles X = (X (1) ,X (2) ,···,X (N) ) may be used. Each particle is X (i) =(x (i) ,y (i) ,θ (i) ), where (x (i) ,y (i) ) indicates the two-dimensional position, and θ (i) is the orientation of the i-th particle. Particle movement: For a movement measurement (Δdt,θt) at time t, the i-th particle is updated as follows: TIFF0007623106000008.tif7130where δ (i) and α (i) is the Gaussian random noise added to the range and bearing measurements.

[0310] Particle weighting: At each step, particles are assigned two weights: 1) any particle that hits an inaccessible area (typically a wall) during its movement "dies" and is removed; 2) all other particles survive and are weighted based on their distance-to-live (DTL), as defined below:

[0311] The main idea for determining particle weights without the aid of any additional measurements from additional signal sources is that a particle should get a larger weight if it is more likely to survive for a longer distance before hitting some obstacle, and a smaller weight if it is not. This insight leads directly to the specification of DTL.

[0312] FIG. 5 shows an example of DTL according to some embodiments of the present disclosure. In Case 1, lightly shaded particles have a larger DTL than darkly shaded ones. In Case 2, dark particles get a larger weight when the particles are moving towards the right. In Case 3, both particles have very large DTLs and are therefore forced to a fixed max-DTL value. At runtime, the DTL is approximated from pre-calculated DTLs along the four cardinal directions. As shown in FIG. 5, the DTL for a particle is calculated as the maximum accessible distance from the particle position along the particle movement direction. A max-DTL may be set to avoid excessive values ​​in the case of very long paths (Case 3 in FIG. 5).

[0313] In principle, the DTL needs to be calculated for every particle at every step, which is too costly. In the disclosed GPF, an efficient approximation can be devised. During the preprocessing stage, the DTLs in the four cardinal directions (i.e., left, right, up, and down) can be further calculated in advance. The DTL along a certain direction θ can then be calculated accordingly as the sum of the projections of the DTLs along the two cardinal directions closest to the current movement direction (e.g., right and up in the example of Figure 5). Although the resulting DTL is not necessarily equal to the exact DTL by definition, such an approximation may prove effective and efficient in practical systems. The main reason behind this is that only the relative ranking of particle weights, not their absolute values, is important in the GPF. The above operation preserves the weight ranking similar to the exact DTL, but significantly reduces runtime complexity.

[0314] Resampling: The disclosed GPF implements a novel approach for resampling, by first following the classical sequential importance resampling approach: TIFF0007623106000010.tif12158Resample N_live particles, where N_live denotes the number of particles surviving during the current step. However, for each dead (and therefore removed) particle, instead of resampling, you may choose to regenerate a new particle to avoid sample degradation issues (particles becoming overly concentrated).

[0315] As shown in Figure 4B, a specific neighborhood around the target's current location may be considered for regeneration. Assuming the target is currently at (x_t, y_t) closest to vertex V_t, we first collect all reachable vertices V_j with reachable distance m_tj ≤ r and calculate each of these DTLs based on the current movement direction. We then perform importance sampling to draw N-N_live new particles among these candidate locations using these DTLs as importance distributions. As shown in Figure 4B, the regeneration radius r dynamically increases (white circle) if the target is detected turning and decreases (green circle) otherwise.

[0316] Target Localization: Finally, at each step, the information of all particles may be used to estimate the target's location. Intuitively, the target location may be determined as the weighted centroid of all particle locations. However, the centroid may be in an inaccessible location, or the line connecting the centroid to the current location may intersect with a wall. Therefore, EasiTrack may rely on a similar concept of medoids. Generally, a medoid is a representative in the dataset that has the smallest average dissimilarity to all objects in the dataset. Compared to centroids, using medoids ensures that the resulting target location is always valid. TIFF0007623106000011.tif35155The sequence of localization results is then smoothed and displayed to the user.

[0317] Gyroscopes are known to suffer from significant cumulative errors. As reported in a recent study, errors can accumulate to over 50° after a two-minute run. According to the disclosed experiments, this can produce an error of over 30° for a single 90° natural rotation. Since all particles are guided to move in the wrong direction, these significant errors in orientation result in significant position errors that, if not calibrated, cannot be corrected even by a GPF.

[0318] To eliminate the accumulated error, a technique may be devised to automatically reset the heading opportunistically. The key observation is that when a target is moving along a nearly straight path but not turning, the movement trajectory during this non-turning period provides useful hints for inferring the current heading. EasiTrack utilizes these hints to perform opportunistic movement direction recalibration. Specifically, when such a non-turning segment is detected, the current movement direction may be reset by estimating the centerline direction of a straight segment. A typical behavior is that a target spends most of its time moving straight, not turning. Therefore, the reset operation can be adopted very frequently. Continuous recalibration will effectively mitigate the accumulated heading error even over very long movements.

[0319] EasiTrack only measures distance and direction of travel, so it requires a global position to start. Technically, a particle filter could determine its initial position by initializing uniformly across all possible positions. However, this is only feasible, especially with a very large number of particles, which is practically computationally prohibitive for very large tracking areas. Furthermore, particles only progressively converge after a long time, and may never converge at all.

[0320] Considering practical applications and systems, a promising alternative is to utilize the last-seen GPS signal at the building entrance rather than just requiring manual input from the end user. The current EasiTrack system can primarily use manual user input, since the primary goal is to demonstrate the disclosed distance estimation and map enhancement techniques. While these techniques may not provide an initial position with high accuracy, the errors they introduce will be overcome later by the disclosed GPF. For implementation purposes, a fully functional real-time system of EasiTrack can be constructed, including many implementation details.

[0321] CSI Denoising: The CSI measured on COTS WiFi includes phase offsets such as carrier frequency offset (CFO), sampling frequency offset (SFO), and symbol timing offset (STO) in addition to the initial phase offset caused by the phase-locked loop. EasiTrack does not suffer from bothersome initial phase distortions that can only be pre-calibrated manually, e.g., by taking the absolute value of equation (1). By using a phase cleaning approach, other linear offsets can be calibrated out.

[0322] Integration of multiple receive antennas: The disclosed AA method may require a minimum of two antennas as an array. In the disclosed implementation, the WiFi chipset has three antennas. To fully utilize them, they can be arranged in a uniformly spaced linear array. The first and second antennas can then be treated as a pair, the second and third antennas as another pair, and the results can be integrated. The FB method works with a single antenna, but can also fuse the TRRS of all receive antennas to facilitate distance estimation.

[0323] Motion detection: Inertial sensors can be used to determine whether the target is moving. EasiTrack estimates the distance traveled only if it detects that the device is moving. To mitigate the interference of extraneous motion (e.g., hand movements) in human tracking, a mask can be further applied to detect whether the user is walking by detecting steps using the IMU.

[0324] Tracking in Very Wide Areas: EasiTrack uses a single AP to achieve ubiquitous tracking with remarkable coverage. Technically, this works anywhere covered by the AP, regardless of whether it is in LOS or NLOS conditions. In practice, one single AP may not be sufficient to completely cover a very wide tracking area. In such situations, multiple APs are required to provide complete coverage. Therefore, although EasiTrack uses only one AP at a time for tracking, it needs to handle handover between different APs when clients roam in space. For this purpose, a clear RSS-based handover scheme can be used in the disclosed implementation.

[0325] Map Preprocessing: EasiTrack only requires information about accessible and inaccessible areas from a regular image of an indoor floor plan. This information can be automatically recognized by pixel color. For example, walls may be marked with a dark color, while open spaces are black and white (or vice versa). However, depending on the quality of the obtained map image, there may be some interference (e.g., text, dimensions, etc.) that affects the automatic recognition. To address this, the map may be manually preprocessed to highlight all obstacles (mainly walls) with a specific color. In one embodiment, it takes about 10 minutes for a person with basic knowledge of Photoshop® to process a typical map.

[0326] Using COTS WiFi, the disclosed system can be evaluated in real-world scenarios. EasiTrack can then be deployed in five different buildings to evaluate human and cart tracking, and two industrial manufacturing facilities to test AGV tracking.

[0327] Methodology: First, the accuracy and influence factors of the disclosed distance estimation method AA can be evaluated and compared with FB. To do so, the EasiTracker can be placed on a cart and moved along an approximately straight line. Various traces of approximately 10 m in length can be evaluated, and both AA and FB can be performed on the collected CSI, respectively. A camera can be set up to capture the ground truth traveled distance.

[0328] Overall Accuracy: Figure 6 compares the overall performance of AA and FB for distance traveled estimation. As can be seen, AA achieves a significant median error of approximately 0.25 m and a 90th percentile error of 0.69 m, while FB yields errors of 0.75 m and 1.82 m, respectively. While the improved performance of AA comes at the expense of relatively constrained motion, AA opens up a wide range of exciting opportunities due to its high accuracy, particularly for tracking machines and objects such as AGVs, shopping carts, robots, etc. Several factors that can affect the performance of AA have been studied. The influencing factors of the FB method have been extensively studied. Therefore, when incorporating it into EasiTrack, only the overall tracking performance may be evaluated.

[0329] Sampling rate: A specific sampling rate is required by EasiTrack to ensure sufficient resolution in distance estimation; otherwise, the peak resolution (recall Figure 3A) will be limited. Results show that the 200 Hz sampling rate available with EasiTrack is adequate for typical velocities up to 2 m / s.

[0330] Antenna Diversity and Isolation: Figure 7 shows antenna pair diversity. Different combinations of the three antennas available on the disclosed device are tested, as well as fusing two pairs. As can be seen, AA consistently produces high performance using different antenna pairs, improving when multiple pairs are combined. We also investigated different antenna separations ranging from λ / 2, λ, 3λ / 2 to 2λ. As shown in Figure 8, the different separations make negligible difference. However, as a rule of thumb, the separation should be greater than λ / 2, otherwise coupling effects begin to come into play.

[0331] Methodology: The tracking performance of the EasiTrack system can now be studied. The system can be deployed on one floor of a typical office building. Inside the floor are rooms separated by drywall, concrete pillars, and an elevator. To provide good coverage, the AP can be placed in the center of the building. The AP may operate on channel 153. There is normal WiFi traffic on the same and adjacent channels. During the experiment, people are working in the surrounding area.

[0332] With AA-based EasiTrack (EasiTrack-AA), the device can be placed on a cart and pushed around. Meanwhile, with FB-based (EasiTrack-FB), a person can be asked to walk naturally while holding the device in their hand. To study quantitative positioning errors, a set of checkpoints can be marked at a density of approximately every 2 meters. Ground truth is recorded when a user passes through a checkpoint. Several users can then be recruited to walk (or push a cart) around different areas. Whether testing EasiTrack-AA or EasiTrack-FB, multiple users can be tracked simultaneously. While EasiTrack can support any volume, due to budget constraints, three tracking kits (each including a Galileo Gen2 board and a Surface Pro) can be built. Results are then sorted to evaluate each of the two methods.

[0333] Overall performance: Figure 9 shows that both EasiTrack-AA and EasiTrack-FB achieve sub-meter median accuracy. Specifically, EasiTrack-AA produced a median error of 0.58 m and a 90th percentile error of 1.33 m for cart tracking. Surprisingly, EasiTrack-FB achieves slightly worse performance for person tracking, with a median error of approximately 0.70 m and a 90th percentile error of 1.97 m. Compared with the distance estimation accuracy in Figure 6, the overall tracking accuracy of EasiTrack-AA is slightly lower, which is mainly limited by the orientation error arising from the sensor. In contrast, the accuracy of EasiTrack-FB is significantly improved by the contribution of the disclosed GPF, which effectively corrects position errors in the map.

[0334] Impact of particle number: A unique feature of EasiTrack is that it achieves significant performance using a small number of particles thanks to its graph-based model. We can examine how performance varies with particle quantity. Therefore, we evaluate performance across particle numbers ranging from 20 to 800. EasiTrack achieves significant performance with as few as 100 particles. Results show that EasiTrack requires several orders of magnitude fewer particles than existing approaches. In one embodiment, 20 particles can be too few and result in very large tail errors, especially for EasiTrack-FB. In practice, a minimum of 50 particles is recommended.

[0335] Impact of Errors in Initial Position: Because the current implementation disclosed uses manual input for the initial position, it is interesting to examine how sensitive it is to uncertainties in the initial position. Random errors ranging from 0.5 m to 2 m can be added to the starting point to evaluate traces with incorrect initial positions. The disclosed GPF sensitively overcomes these errors and maintains similar performance, only slightly degrading to a median error of 0.73 m and a 90th percentile error of 1.56 m for EasiTrack-AA when there is a 2 m error in the initial position. In other words, EasiTrack does not depend on an accurate initial position to start with. Instead, it requires only coarse-grained input, which can again be provided by the user, an opportunistic GPS, or other available anchors.

[0336] Benefits of Orientation Reset: The benefits of the orientation reset module can be studied independently. To do so, all traces can be rerun offline by disabling the orientation reset function and the results compared. For EasiTrack-FB, we found that automatic orientation reset, which eliminates the large accumulated orientation error caused by the sensor, significantly reduces the large error, from 3.1m to 1.7m in the 90th percentile. For EasiTrack-AA, we achieve a minimal improvement in accuracy, since the orientation error is significantly less when the device is placed on a cart than when walking as a human. Once the orientation error accumulates, the orientation set becomes effective.

[0337] Coverage: The disclosed benchmark evaluation demonstrates EasiTrack's tracking coverage in a typical office measuring 36m x 22m. To stress the disclosed system to demonstrate its coverage limitations, tracking can be tested in a large building measuring approximately 100m x 35m with many concrete walls and pillars. In this case, one AP is not sufficient to cover the entire floor, allowing tracking with handover between two APs to be tested. Two APs can be placed at two diagonal corners of the building, each covering approximately half of the tracking area. The results verify that EasiTrack smoothly roams between the two APs, achieving similar high accuracy not only in the overlapping area (the central hallway) but also under the coverage of either AP, thereby enabling tracking over very large areas by simply setting up additional APs.

[0338] To verify the tracking accuracy and simplicity of deployment of EasiTrack, we perform real-world deployments at multiple locations, considering two different scenarios: human tracking and AGV tracking. Both cases show good tracking performance. EasiTrack runs in real time with no perceptible delay to the end user. One may be concerned about the system complexity introduced by different modules, especially distance estimation and GPF. We verify that EasiTrack can run in real time even on small embedded devices.

[0339] 10 shows a flow diagram of a map-augmented tracking method 1000 according to some embodiments of the present invention. As shown in FIG. 10, CSI collection 1010 is performed by, for example, collecting CSI 1001 of a wireless multipath channel. The collected CSI is used to estimate a travel distance in operation 1012, which may be based on a virtual antenna alignment (AA) method or a time-reversal focusing ball (FB) method, as described above. The result of the travel distance estimation is provided to a graph-based particle filter 1040.

[0340] Additionally, readings are obtained from the inertial sensors 1002 in operation 1020. An orientation estimation 1022 is performed based on these readings, and the orientation estimation results are provided to a graph-based particle filter 1040.

[0341] Further, processing of the indoor map 1003 is performed in operation 1030, and the processed map information is used to perform map discretization 1032. Thereafter, reachability construction 1034 is performed to generate results that are provided to a graph-based particle filter 1040.

[0342] After the graph-based particle filter 1040, an accumulated orientation error correction 1042 is performed, and a target position estimation 1044 is performed to generate a target object position estimate 1050. Thus, the movement of the target object is tracked or monitored by the map-augmented tracking technique. According to various embodiments, the order of some of the operations in Figure 10 may be interchanged.

[0343] The present teachings disclose a ubiquitous indoor tracking device that achieves sub-meter accuracy in both LOS and NLOS scenarios using a single unknown AP and scales to large buildings and end clients at near-zero cost. The present teachings also disclose a map-augmented probabilistic tracking algorithm based on a graph-based particle filter that fuses an indoor map with CSI-based distance and IMU-based direction information.

[0344] The present teachings disclose EasiTrack, an indoor localization system that achieves sub-meter accuracy using a single AP and scales to many buildings at near-zero cost, making it a promising solution for ubiquitous indoor tracking. The present teachings also disclose a CSI-based approach for distance estimation and a map-augmented tracking algorithm. The system can be deployed and validated in different buildings and facilities for tracking humans and machines.

[0345] In various embodiments of the present disclosure, wireless tracking may be performed according to the following clauses.

[0346] Item A1: A method / apparatus / system for wireless monitoring, comprising: using a processor, memory, and instruction set to transmit a series of probe signals by an antenna of a first wireless device to at least one disparate target wireless receiver through a wireless multipath channel of a venue; and asynchronously acquiring, from the series of probe signals, at least one time series of channel information (CI time series) by each of the at least one disparate target wireless receiver, wherein the channel information (CI) is of the wireless multipath channel between the disparate target wireless receiver and the first wireless device; and acquiring, from the series of probe signals, at least one time series of channel information (CI time series) by each of the at least one disparate target wireless receiver. and iteratively calculating a respective next position of the individual object at a respective next time (T1) within the individual map based on at least one of a respective current position of the individual object at a respective current time (T2), the respective incremental distance (ΔD), and a respective direction (theta) of the respective movement during the respective incremental period.

[0347] Item A2: A method / apparatus / system of the wireless monitoring system of item A1, wherein a specific movement of a specific object is monitored based on CI time series obtained by two or more heterogeneous target wireless receivers.

[0348] Item A3: The method / apparatus / system of the wireless monitoring system of item A1, wherein two or more distinct movements associated with two or more distinct objects are monitored relative to a particular map.

[0349] Item A4: The method / apparatus / system of the wireless monitoring system of item A1, wherein the individual incremental time (ΔT) is the time difference between two individual probe signals of the series of probe signals.

[0350] Item A5: A method / apparatus / system of the wireless monitoring system of item A1, wherein the individual direction (theta) of the individual movement during the individual incremental time period is an individual function of at least one of the individual direction of the individual movement at the individual current time (T2), the individual direction of the individual movement at the individual next time (T1), and the individual direction of the individual movement at another time.

[0351] Item A6: The method / apparatus / system of the wireless monitoring system of item A1, further comprising: using a particle filter to calculate the respective next position of the respective object at the respective next time on the respective map.

[0352] Item A7: A method / apparatus / system for a wireless monitoring system of Item A1, further comprising: initializing individual initial candidate positions for an individual initial number of the individual objects at an individual initial time (T0); iteratively calculating individual first candidate positions for a first dynamic number (N1) of the individual objects at the next time based on individual second candidate positions for a second dynamic number (N2) of the individual objects at the current time; and calculating the individual next positions of the individual objects at the individual next time based on at least one of the individual first candidate positions of the first dynamic number at the individual next time and the individual second candidate positions of the second dynamic number at the individual current time.

[0353] Item A8: A method / apparatus / system of the wireless monitoring system of Item A7, wherein the individual object moves within an individual area represented by the individual multi-dimensional map, the individual multi-dimensional map is represented as an individual multi-dimensional array A, and as a result, the accessibility of each position of the individual area is represented by a corresponding array element a that is a logical value between 0 and 1. If the array element a = 0, the position is inaccessible and prohibited. If a = 1, the position is fully accessible. If 0 < A < 1, the position is partially accessible. Each of the individual next position, the individual current position, the individual first candidate positions of the first dynamic number, and the individual second candidate positions of the second dynamic number is a point within the individual area and is represented as a corresponding array element a where a > 0. The individual direction of movement of the individual object at any individual position is locally represented as one of a plurality of allowable directions. A method / apparatus / system.

[0354] Item A9: A method / apparatus / system of the wireless monitoring system of Item A8, further comprising calculating the weight of the first dynamic number (N1) respectively associated with the individual first candidate positions, each weight being a function of at least one of the individual current position, the individual first candidate position, the corresponding individual second candidate position associated with the individual first candidate position, the individual direction of movement, and the distance between the individual first candidate position and the first inaccessible array element a in the individual direction, and calculating the individual next position of the individual object based on the individual first candidate positions of the first dynamic number and the associated weights of the first dynamic number. A method / apparatus / system.

[0355] Item A10: A method / apparatus / system of the wireless monitoring system of Item A9, wherein each weight is a monotonically non-decreasing function of the distance between the individual first candidate position and the first inaccessible array element a in the individual direction. A method / apparatus / system.

[0356] Item A11: The method / apparatus / system of the wireless monitoring system of item A9, wherein each weight is a bounded function of the distance between the respective first candidate location and the first unreachable array element a in the respective direction.

[0357] Item A12: The method / apparatus / system of the wireless monitoring system of item A9, further comprising calculating the respective next location of the respective object as a weighted average of the first dynamic number of first candidate locations.

[0358] Item A13: The method / apparatus / system of the wireless monitoring system of item A9, further comprising calculating the respective next position of the respective object as one of the respective first candidate positions.

[0359] Item A14: The method / apparatus / system of the wireless monitoring system of item A9, further comprising normalizing the weights.

[0360] Item A15: The method / apparatus / system of the wireless monitoring system of Item A9, further comprising: calculating a weighted cost of each individual first candidate location relative to the remainder of the first dynamic number of individual first candidate locations, wherein the weighted cost is a weighted sum of pairwise distances between the individual first candidate location and each of the remainder of the individual first candidate locations, the weights being normalized; and selecting the individual next location of the individual object as the individual first candidate location having the smallest weighted cost.

[0361] Item A16: A method / apparatus / system of the wireless monitoring system of item A8, wherein the multidimensional map is two-dimensional, the array A is a two-dimensional array, and each array element a has two indices.

[0362] Item A17: A method / apparatus / system of the wireless monitoring system of Item A8, wherein the multi-dimensional map is three-dimensional, the array A is a three-dimensional array, and each array element a has three indexes.

[0363] Item A18: A method / apparatus / system of the wireless monitoring system of Item A8, comprising calculating a predicted value for each of the second candidate positions of the individual object based on at least one of the second candidate position, the individual increment distance (ΔD), and the individual increment period (ΔT); creating a first candidate position of the individual object based on the predicted value of the second candidate position if the predicted value of the second candidate position is sufficiently reachable at the associated array element a = 1; labeling the second candidate position as "rejected" without creating a first candidate position if the predicted value of the second candidate position is prohibited at the associated array element a = 0; generating a random number between 0 and 1 if the predicted value of the second candidate position is partially reachable at the associated array element 0 < a < 1; and creating a first candidate position of the individual object based on the predicted value of the second candidate position if the random number is less than a.

[0364] Item A19: A method / apparatus / system of the wireless monitoring system of Item A18, further comprising generating a new first candidate position of the individual object by probabilistically taking the predicted value of the second candidate position that has not been rejected, using a probability distribution based on at least one of the weight associated with the predicted value, the weight associated with the second candidate position, the array element of the multi-dimensional array A associated with the predicted value, the array element of the multi-dimensional array A associated with the second candidate position, and another probability distribution, if the amount of the first candidate positions is less than a threshold.

[0365] Item A20: The method / apparatus / system of the wireless monitoring system of item A18, further comprising: if the quantity of first candidate locations is less than a threshold, creating a new first candidate location for the individual object that probabilistically takes the non-rejected second candidate location, with a probability based on a weight associated with the predicted value of the non-rejected second candidate location.

[0366] Item A21: The method / apparatus / system of the wireless monitoring system of item A18, further comprising: if the quantity of first candidate locations is less than a threshold, calculating a tentative next location based on the quantity of first candidate locations, and probabilistically creating a new first candidate location of the individual object in the vicinity of the tentative next location based on a probability distribution.

[0367] Item A22: The method / apparatus / system of the wireless monitoring system of item A18, further comprising, if the quantity of first candidate locations is less than a threshold, probabilistically creating a new first candidate location of the individual object as a predictor of locations sampled in the vicinity of the individual's current location based on a probability distribution.

[0368] Clause A23: The method / apparatus / system of the wireless monitoring system of clause A22, wherein the vicinity includes at least one of the second candidate locations that is not rejected.

[0369] Item A24: The method / apparatus / system of the wireless monitoring system of item A22, wherein the probability distribution is a weighted sum of a set of probability density functions (pdfs), each centered on one of the second candidate locations that is not rejected.

[0370] Item A25: The method / apparatus / system of the wireless monitoring system of item A24, wherein at least two of the sets of pdfs are general pdfs.

[0371] Item A26: The method / apparatus / system of the wireless monitoring system of item A24, wherein the weight of each pdf associated with a second candidate location in the weighted sum is a function of the array element associated with the second candidate location.

[0372] Clause A27: The method / apparatus / system of the wireless monitoring system of clause A1, further comprising: maintaining a dynamic number of individual candidate locations at any time; modifying the dynamic number of individual candidate locations by at least one of initializing at least one individual candidate location, updating at least one individual candidate location, adding at least one individual candidate location, pausing at least one individual candidate location, stopping at least one individual candidate location, resuming at least one paused individual candidate location, reinitializing at least one stopped individual candidate location, and removing at least one individual candidate location; and calculating the individual next location of the individual object at the individual next time based on at least one of the dynamic number of individual candidate locations at the individual next time and the dynamic number of individual candidate locations at another time.

[0373] Item A28: The method / apparatus / system of the wireless monitoring system of item A27, wherein the dynamic number of isolated candidate locations is limited by an upper limit at the respective current time.

[0374] Item A29: The method / apparatus / system of item A27 for wireless monitoring, wherein the dynamic number of candidate isolated locations may be limited by a lower bound at a certain time.

[0375] Item A30: The method / apparatus / system of the wireless monitoring system of item A27, further comprising adding at least one candidate location if the dynamic number of distinct candidate locations is lower than a lower limit.

[0376] Clause A31: A method implemented by a processor, a memory communicatively coupled to the processor, and an instruction set stored in the memory executed by the processor, comprising: acquiring a plurality of time series of channel information (TSCI) of a wireless multipath channel, the plurality of TSCI being extracted from a wireless signal transmitted from a first wireless device to a second wireless device through the wireless multipath channel, each of the plurality of TSCI being associated with an antenna of the first wireless device and an antenna of the second wireless device, one of the first wireless device and the second wireless device being a stationary device, and the other of the first wireless device and the second wireless device being a mobile device moving with an object, the mobile device having at least two antennas; and wherein a first channel information (CI) at a first time of a first TSCI associated with a first antenna of the mobile device is extracted at a second time of a second TSCI associated with a second antenna of the mobile device. determining that a first TSCI matches a second TSCI, wherein both the first TSCI and the second TSCI are associated with a specific antenna of the stationary device; calculating at least one movement parameter of the moving device based on at least one of the plurality of TSCIs, a configuration of the antenna of the moving device, a configuration of at least one antenna of the stationary device, a time difference between the first time and the second time, and a distance between the first antenna and the second antenna; calculating spatiotemporal information (STI) of the moving device based on at least one of the at least one movement parameter, the first time, the second time, the configuration of the antenna of the moving device, the configuration of at least one antenna of the stationary device, a past STI, and a past movement parameter, wherein at least one of the movement parameter and the STI is related to a current movement of the moving device; and tracking the object and the moving device based on the STI.

[0377] Clause A32: The method of clause A31, wherein at least one of the movement parameters and the STI includes at least one of position, horizontal position, vertical position, length, area, volume, capacity, direction, angle, distance, displacement, speed, velocity, acceleration, rotational velocity, rotational acceleration, gait cycle, presence, motion type, motion classification, motion characteristic, sudden motion, transient motion, periodic motion, periodic motion period, periodic motion frequency, transient motion, time trend, timing, timestamp, duration, time window, sliding time window, history, frequency trend, spatiotemporal trend, spatiotemporal change, and event, and tracking the object and the moving device includes determining a map location of the object, tracking the location of the object, tracking another motion analysis of the object, and tracking the object. guiding the movement of the object along a trajectory, guiding the movement of the object to avoid obstacles, tracking the movement of the object, tracking the behavior of the object, object behavior identification, detecting the movement of the object, detecting vital signs of the object, detecting periodic movements associated with the object, detecting breathing of the object, detecting a heart rate of the object, detecting an event associated with a current movement, detecting a tipping movement of the object, presenting the position of the object, presenting a history of the position of the object, and graphically displaying the position of the object.

[0378] Clause A33: The method of clause A31, further comprising: determining that a third CI at a third time of a third TSCI associated with a third antenna of the mobile device matches a fourth CI at a fourth time of a fourth TSCI associated with a fourth antenna of the mobile device, wherein both the third TSCI and the fourth TSCI are associated with another specific antenna of the stationary device; calculating a probabilistic model for a mobile parameter based on at least one of the match between the first CI and the second CI, the match between the third CI and the fourth CI, and another match between another two CIs; and determining a probability model for a mobile parameter based on the probabilistic model, the plurality of TSCIs, the configuration of antennas of the mobile device, the stationary device, and the third TSCI at a third time of a fourth CI associated with a fourth antenna of the mobile device, wherein both the third TSCI and the fourth TSCI are associated with another specific antenna of the stationary device. and probabilistically calculating the at least one movement parameter of the mobile device based on at least one of the configuration of a stationary device's antenna, a time difference between the first time and the second time, a time difference between the third time and the fourth time, a distance between the first antenna and the second antenna, and a distance between the third antenna and the fourth antenna, wherein the STI of the mobile device is calculated based on at least one of the at least one movement parameter, the first time, the second time, the third time, the fourth time, the configuration of the stationary device's antenna, the configuration of the mobile device's antenna, a past STI, and a past movement parameter.

[0379] Item A34: The method of item A31, further comprising: calculating a probabilistic model for the at least one movement parameter; probabilistically calculating the at least one movement parameter of the moving device based on the probabilistic model; and probabilistically calculating the STI of the moving device based on the at least one probabilistically calculated movement parameter.

[0380] Item A35: The method of item A31, further comprising: calculating a probabilistic model for the at least one movement parameter; probabilistically calculating the at least one movement parameter of the moving device based on the probabilistic model; calculating a number of candidate STIs for the moving device, each candidate STI being calculated based on a probabilistically calculated movement parameter; and calculating the STI based on the set of candidate STIs.

[0381] Example: Stride length estimation

[0382] Stride length estimation has a variety of applications, ranging from pedestrian tracking to personal healthcare. However, it is typically achieved through inertial sensing, which suffers from large errors due to noisy readings on low-cost, commodity sensors and unconstrained human gait. Unlike conventional methods that rely solely on inertial sensors, the present teachings disclose a fused wireless and inertial sensing design that estimates fine-grained stride length. The disclosed approach incorporates recent advances in WiFi sensing, based on centimeter-accurate walking distance estimation from wireless signals. A novel step detection algorithm using inertial sensor readings is then disclosed that not only counts steps but also reports time information for each detected step. The algorithm then fuses the time-annotated distance estimation results with the steps to derive stride length. Evaluation on a large public dataset shows that the step counting algorithm produces a 3% error. Furthermore, experiments on commodity hardware with eight users show an error of approximately 2 centimeters in stride length estimation.

[0383] Inertial sensing has been an inexpensive and convenient solution for many mobile applications, particularly pedestrian dead reckoning (PDR) and gait analysis. It has been used to support diagnosis and to quantify and treat neurological or musculoskeletal disorders that may result in gait disorders, limping, or reduced stride length. Meanwhile, PDR using low-cost inertial measurement units (IMUs) has been widely investigated to provide alternative positioning when GPS is unavailable. It integrates distance traveled, typically estimated as the number of steps multiplied by stride length, with heading information to provide a continuous position.

[0384] Despite extensive research, one of the most important aspects of inertial sensing remains the accurate estimation of stride length, a crucial stride-by-stride parameter for both gait analysis and PDR. Many algorithms have been developed for step detection, such as zero-crossing, peak detection, and autocorrelation. However, stride length estimation is more complex due to noisy readings on inexpensive sensors and variations in gait patterns between individuals and over time. Early solutions employ oversimplified linear / nonlinear models that are subject to error. Most conventional algorithms perform a double integration of acceleration over time, which requires a zero-velocity update point for reinitialization and is prone to noisy sensor data and motion interference. Recent research has built neural networks to learn stride length, but this requires a large amount of data for training. Other modalities, including camera systems and barometric pressure sensors, have also been employed for stride length estimation. However, these systems are less convenient and typically much more expensive than inertial sensors.

[0385] Today, most mobile devices are equipped with inertial sensors as well as multi-antenna Wi-Fi radios. This disclosure explores how to leverage this opportunity to integrate emerging wireless sensing with traditional inertial sensing to achieve accurate stride length estimation. Our idea is to estimate walking distance from wireless signals while capturing corresponding steps from IMU data. We adopt a virtual antenna alignment approach and can implement this for mobile environments using off-the-shelf Wi-Fi cards with two or three antennas. The user simply needs to hold the radio and walk freely, and walking distance is estimated from a series of measured channel state information (CSI) of the received Wi-Fi signal. First, we introduce a novel time-domain algorithm for step detection based on a finite state machine that not only counts steps but also reports the exact start and end times of each detected step. Many existing approaches are unable to obtain such time-annotated steps. Then, stride length can be estimated by dividing the traveled distance by the corresponding number of steps taken during the same period.

[0386] Experiments to validate the effectiveness of the algorithm include two parts. First, we investigate step detection on a large public dataset. The dataset contains time series of IMU data measured on a smartphone during typical unconstrained use while walking. Evaluation on this dataset shows that the step counting algorithm achieves remarkable performance with an error rate of less than 5% for 90% of all traces, outperforming nine different approaches. In addition to accurate step counting, the disclosed algorithm also outputs timing information for each single step.

[0387] To evaluate the performance of stride estimation, we implemented the disclosed algorithm on commodity hardware and conducted experiments. Eight users were asked to walk as usual while holding the device...

Claims

1. 1. A wireless monitoring system, comprising: a transmitter configured to transmit a series of probe signals at a location over a wireless multipath channel; A receiver comprising: receiving, via the wireless multipath channel between the transmitter and the receiver, the series of probe signals modulated by the wireless multipath channel and by objects moving at the location; a receiver configured to obtain a time series channel information (TSCI) of the wireless multipath channel from the series of probe signals modulated by the wireless multipath channel and the object; 1. A processor comprising: monitoring movement of the object relative to a map based on the TSCI; determining an incremental distance traveled by the object within an incremental time period based on the TSCI; a processor configured to calculate a next position of the object on the map at a next time based on at least one of a current position of the object at a current time, the incremental distance, and the direction of the movement during the incremental time period; and Wireless monitoring system including:

2. Calculating the next position of the object at the next time instant comprises: initializing an initial value of an initial candidate position of the object at an initial time; iteratively calculating a first dynamic number for a first candidate position of the object at the next time based on a second dynamic number for a second candidate position of the object at the current time; calculating the next location of the object at the next time based on at least one of the first dynamic number of a first candidate location at the next time and the second dynamic number of a second candidate location at the current time.

2. The wireless monitoring system of claim 1.

3. the object moves through an area displayed on the map during the incremental time period; the map is a multi-dimensional map represented as a multi-dimensional matrix A, the reachability of each location in the region being represented by a corresponding array element a whose logical value is between 0 and 1; The region location is unreachable and forbidden when the corresponding array element satisfies a=0; The location of the region is completely reachable if the corresponding array element satisfies a=1; the location of the region is partially reachable if the corresponding array element satisfies 0<a<1; each of the next position, the current position, the first dynamic number of a first candidate position, and the second dynamic number of a second candidate position is a point in the region and is represented as a corresponding array element a, with a>0; the direction of the movement of the object at any location is locally displayed as one of a number of allowed directions; 3. The wireless monitoring system of claim 2.

4. Calculating the next position of the object at the next time instant comprises: calculating a weight of the first dynamic number associated with each first candidate position, each weight being a function of at least one of the current position, the first candidate position, a corresponding second candidate position associated with the first candidate position, the direction of the movement, and a distance between the first candidate position and a first unreachable array element a in the direction; calculating the next position of the object based on the first dynamic number of a first candidate position and a weight of the associated first dynamic number; 4. The wireless monitoring system of claim 3, comprising:

5. each weight is a monotonically non-decreasing function of the distance between the first candidate location and the first unreachable array element a in the direction; each weight being a bounded function of the distance between the first candidate location and the first unreachable array element a in the direction; 5. The wireless monitoring system of claim 4.

6. The next position of the object at the next time is a weighted average of the first dynamic number of a first candidate location and one of the first candidate locations; 6. A wireless monitoring system according to claim 4 or 5.

7. and calculating the next position of the object at the next time further comprises normalizing the weights to generate normalized weights.

7. A wireless monitoring system according to any one of claims 4 to 6.

8. Calculating the next position of the object at the next time instant comprises: calculating a weighted cost of each first candidate location with respect to the remainder of the first dynamic number of first candidate locations based on the normalized weights, the weighted cost being a weighted sum of pair-wise distances between the first candidate location and each of the remainder of the first candidate locations; The wireless monitoring system of claim 7 , further comprising: selecting the first candidate location having a minimum weighted cost as the next location of the object.

9. Calculating the next position of the object at the next time instant comprises: calculating a predicted value for each of the second candidate positions of the object based on at least one of the second candidate positions, the incremental distance, and the incremental duration; generating a first candidate position of the object based on the predicted value of the second candidate position when the predicted value of the second candidate position is fully reachable with an associated array element having a=1; if the predicted value of the second candidate location is forbidden with an associated array element a=0, then do not create a first candidate location and label the second candidate location as "rejected" 9. The wireless monitoring system of claim 8.

10. Calculating the next position of the object at the next time instant comprises: generating a new first candidate location of the object by probabilistically taking the predicted values ​​of non-rejected second candidate locations in a probability distribution, the probability distribution comprising: a weight associated with said predicted value; a weight associated with the second candidate location; an array element of the multidimensional matrix A associated with the predicted value; and an array element of the multidimensional matrix A associated with the second candidate location; 10. The wireless monitoring system of claim 9.

11. Calculating the next position of the object at the next time instant comprises: generating a new first candidate position of the object by probabilistically taking the second candidate position that is not rejected, with a probability based on a weight associated with the expected value of the second candidate position that is not rejected.

11. A wireless monitoring system according to claim 9 or 10.

12. Calculating the next position of the object at the next time instant comprises:

12. The wireless monitoring system of claim 8, further comprising: calculating a tentative next position based on the first candidate position; and probabilistically generating a new first candidate position of the object in a vicinity of the tentative next position based on a probability distribution.

13. Calculating the next position of the object at the next time instant comprises: and probabilistically generating a new first candidate position of the object based on a predictor of sampled positions in a vicinity of the current position based on a probability distribution. A wireless monitoring system according to any one of claims 8 to 12.

14. the vicinity includes at least one of the second candidate locations that is not rejected; 14. The wireless monitoring system of claim 13.

15. maintaining a dynamic number of candidate locations at all times; Changing the dynamic number of candidate locations, initializing at least one candidate location; updating at least one candidate location; adding at least one candidate location; and and eliminating at least one candidate location. To change and 2. The wireless monitoring system of claim 1, further comprising: calculating the next location of the object at the next time based on at least one of the dynamic number of candidate locations at the next time and the dynamic number of candidate locations at another time.

16. adding at least one candidate location when the dynamic number of candidate locations is below a threshold.

16. The wireless monitoring system of claim 15.

17. the incremental period is equal to at least one of the following: a time difference between two respective probe signals of the series of probe signals, a time for the object to travel a distance related to a carrier frequency of the series of probe signals, and a time for the object to travel a distance equivalent to a distance between at least one of two antennas of the transmitter and two antennas of the receiver; the direction of the movement during the incremental time period is a function of at least one of the direction of the movement at the current time, the direction of the movement at the next time, and a change of direction, rotation, and a direction change angle calculated based on the TSCI, a characteristic function associated with the TSCI, a feature point of the characteristic function, an autocorrelation function (ACF) of the TSCI, an extreme value of the ACF, and a local maximum value of the ACF, and at least one of another direction of the movement at another time.

17. A wireless monitoring system according to any preceding claim.

18. 18. A wireless monitoring system according to any preceding claim, wherein the next position of the object on the map at the next time instant is calculated using a particle filter.

19. 1. A wireless monitoring system, comprising: a transmitter configured to transmit a series of probe signals at a location over a wireless multipath channel; A plurality of heterogeneous receivers, each of which comprises: receiving, via the wireless multipath channel, the series of probe signals modulated by the wireless multipath channel and a plurality of objects moving at the location; a plurality of heterogeneous receivers configured to obtain time series channel information (TSCI) of the wireless multipath channel from the sequence of probe signals modulated by the wireless multipath channel and the plurality of objects; 1. A processor comprising: monitoring movement of the plurality of objects relative to a map based on the TSCI acquired by the plurality of heterogeneous receivers; determining a respective incremental distance traveled by each object in each incremental time period based on the respective TSCI; and a processor configured to calculate a respective next position of the object on the map at a respective next time based on at least one of the respective current position of the object at a respective current time, the respective incremental distance, and a respective direction of movement of the respective object during the respective incremental time period.

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