Target identification using micro-Doppler features

Abstract

Systems and techniques for efficient joint communication and radio frequency (RF) sensing are provided. For example, a method for communication and sensing may include receiving a first signal based on reflections from a target, and generating a Doppler spectrum frame based on the first signal, where the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target. A micro-Doppler measurement report may be generated based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame.

Description

Technical Field

[0001] The present disclosure generally relates to detecting or identifying a target or object using wireless communications (eg, radio frequency (RF) sensing). For example, aspects of the present disclosure relate to systems and techniques for performing target or object detection or identification using micro-Doppler signatures. Background Art

[0002] Wireless communication systems are widely deployed to provide various types of communication content, such as voice, video, packet data, messaging and broadcasting. These systems may be able to support communication with multiple users by sharing available system resources (e.g., time, frequency and power). Examples of such multiple access systems include fourth generation (4G) systems (such as long term evolution (LTE) systems, advanced LTE (LTE-A) systems or LTE-A Pro systems) and fifth generation (5G) systems (which may be referred to as new radio (NR) systems). These systems may use techniques such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA) or discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM). A wireless multiple access communication system may include one or more base stations or one or more network access nodes, each base station or network access node simultaneously supporting communication for multiple communication devices, which may be further referred to as user equipment (UE). Some wireless communication systems may support communication between UEs, which may involve direct transmission between two or more UEs.

[0003] As larger bandwidths are allocated to wireless cellular communication systems (e.g., including 5G) and more use cases are introduced into cellular communication systems, reducing communication or signaling overhead may be necessary for future cellular systems. Summary of the invention

[0004] In some examples, systems and techniques for performing target (e.g., target object) detection or identification using micro-Doppler features are described. For example, systems and techniques can perform target detection and identification using micro-Doppler measurement reports with compression or reduced overhead. According to at least one illustrative example, a method for communication and sensing is provided. The method includes: receiving a first signal based on a reflection from a target; generating a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target; and generating a micro-Doppler measurement report based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame.

[0005] In another example, an apparatus for communication and sensing is provided, the apparatus comprising at least one memory (e.g., configured to store data) and at least one processor (e.g., implemented in a circuit), the at least one processor being coupled to the at least one memory. The at least one processor is configured and can: receive a first signal based on reflections from a target; generate a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target; and generate a micro-Doppler measurement report based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame.

[0006] In another example, a non-transitory computer-readable medium having instructions stored thereon is provided, which, when executed by one or more processors, causes the one or more processors to: receive a first signal based on reflections from a target; generate a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target; and generate a micro-Doppler measurement report based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame.

[0007] In another example, an apparatus for communication and sensing is provided. The apparatus includes: a component for receiving a first signal based on a reflection from a target; a component for generating a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target; and a component for generating a micro-Doppler measurement report based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame.

[0008] This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used in isolation to determine the scope of the claimed subject matter. The subject matter should be understood by reference to appropriate portions of the entire specification of this patent, any or all drawings, and each claim.

[0009] The foregoing and other features and aspects will become more apparent upon reference to the following description, claims and accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Exemplary aspects of the present application are described in detail below with reference to the following drawings:

[0011] Figure 1 is a block diagram illustrating an example of a computing system of an electronic device that may be employed by the disclosed system for radio frequency (RF) sensing according to some examples;

[0012] Figure 2is a diagram illustrating an example of a wireless device detecting a target in the form of a vehicle utilizing RF monostatic sensing techniques that may be employed by the disclosed system for RF sensing according to some examples;

[0013] Figure 3 is a diagram illustrating an example of a receiver in the form of a vehicle that detects a target in the form of a vehicle utilizing RF bistatic sensing techniques that may be employed by the disclosed system for RF sensing according to some examples;

[0014] Figure 4 is a diagram illustrating a geometry for bistatic (or monostatic) sensing according to some examples;

[0015] Figure 5 is a diagram illustrating bistatic ranges for bistatic sensing according to some examples;

[0016] Figure 6 is a diagram showing examples of waveforms that may be employed by the disclosed system for RF sensing according to some examples;

[0017] Figure 7 is a diagram depicting example micro-Doppler signatures associated with different target objects according to some examples;

[0018] Fig. 8A is a diagram depicting example radar signal measurements and multiple sliding windows according to some examples;

[0019] Figure 8B is a description based on some examples that can be based on Fig. 8A Illustration of an example micro-Doppler spectrum frame generated by a sliding window of;

[0020] Figure 8C is a diagram depicting an example micro-Doppler spectrum frame according to some examples, which in some examples may be used with Figure 8B The example micro copper spectrum frames are the same or similar;

[0021] Fig. 9 is a diagram depicting an example micro-Doppler frame compressed using one or more differential reports according to some examples;

[0022] Fig.10 is a diagram depicting example micro-Doppler spectrum frames and associated parameter model reporting parameters according to some examples;

[0023] Fig.11 is a diagram illustrating an example DTX-based selective reporting process according to some examples;

[0024] Fig.12is a diagram illustrating additional micro-Doppler time and angle measurement information according to some examples;

[0025] Fig.13 is a flow chart illustrating an example of a process for communication and sensing according to some examples; and

[0026] Fig.14 is a block diagram illustrating an example of a computing system for implementing certain aspects described herein. DETAILED DESCRIPTION

[0027] Certain aspects of the present disclosure are provided below. Some of these aspects can be applied independently, and some of them can be applied in combination, which is obvious to those skilled in the art. In the following description, specific details are set forth for explanation purposes to provide a thorough understanding of various aspects of the application. However, it is apparent that various aspects can be implemented without these specific details. Each drawing and description are not intended to be restrictive.

[0028] The following description provides only example aspects and is not intended to limit the scope, applicability or configuration of the present disclosure. On the contrary, the following description of the example aspects will provide a description that can be used to implement the example aspects to those skilled in the art. It should be understood that various changes can be made to the function and arrangement of elements without departing from the essence and scope of the present application as set forth in the appended claims.

[0029] As described herein, radio frequency (RF) sensing techniques (e.g., single-station RF sensing, dual-station RF sensing, etc.) can be used to detect the presence and location of targets such as objects, users (e.g., people), vehicles, etc. In some aspects, RF sensing can also be used to identify the type or category of objects that have been detected and / or located. In some cases, a particular type of object can be detected or identified based on the radar cross-section (RCS) of the particular type of object, as described herein. When detecting relatively small objects (e.g., objects with a size close to the minimum detectable RCS size and / or the minimum detectable RCS resolution of a given radar sensing system), RCS may be unreliable. For example, an air surveillance radar sensing system designed to detect an aircraft based on the aircraft's RCS may be unreliable, or even unable to detect much smaller objects, such as drones or unmanned aerial vehicles (UAVs).

[0030] Systems, devices, processes (also referred to as methods), and computer-readable media (collectively referred to as "systems and techniques") for performing Doppler spectrum measurements with reduced or compressed overhead are described herein. For example, these systems and techniques can be used to send or provide micro-Doppler spectrum measurements from a dual-station and / or multi-station radar receiving node with reduced overhead. In some aspects, a sliding window Fourier transform can be used to generate a micro-Doppler measurement report. In some cases, a sliding window fast Fourier transform (FFT) can be used to generate a micro-Doppler measurement report. The micro-Doppler measurement report can be associated with one or more time domain parameters for a sliding window FFT. In some examples, one or more of the time domain parameters for the sliding window FFT can be determined and / or signaled by a remote server (e.g., a sensing server) or a radar Tx node associated with a dual-station or multi-station radar Rx node.

[0031] Figure 1 1 is a block diagram illustrating an example of a computing system 170 of an electronic device 107 that may be employed to perform one or more RF sensing techniques according to some examples. The electronic device 107 is an example of a device that may include hardware and software for connecting and exchanging data with other devices and systems using a communication network (e.g., a 3rd generation partner network such as a 5th generation (5G) / new radio (NR) network, a 4th generation (4G) / long term evolution (LTE) network, a WiFi network, or other communication network). For example, the electronic device 107 may include or be a portion of a mobile device (e.g., a mobile phone), a wearable device (e.g., a web-connected or smartwatch), an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a personal computer, a laptop computer, a tablet computer, an Internet of Things (IoT) device, a wireless access point, a router, a vehicle or a component of a vehicle, a server computer, a robotic device, and / or other devices used by a user to communicate over a wireless communication network. In some cases, the device 107 may be referred to as a user equipment (UE), such as when referring to a device configured to communicate using 5G / NR, 4G / LTE, or other telecommunication standards. In some cases, the device may be referred to as a station (STA), such as when referring to a device configured to communicate using the Wi-Fi standard.

[0032] The computing system 170 includes software and hardware components that may be electrically or communicatively coupled via a bus 189 (or may communicate in other ways, as appropriate). For example, the computing system 170 includes one or more processors 184. The one or more processors 184 may include one or more CPUs, ASICs, FPGAs, APs, GPUs, VPUs, NSPs, microcontrollers, dedicated hardware, any combination thereof, and / or other processing devices and / or systems. The bus 189 may be used by the one or more processors 184 to communicate between cores and / or with one or more memory devices 186.

[0033] The computing system 170 may also include one or more memory devices 186, one or more digital signal processors (DSPs) 182, one or more subscriber identity modules (SIMs) 174, one or more modems 176, one or more wireless transceivers 178, one or more antennas 187, one or more input devices 172 (e.g., a camera, a mouse, a keyboard, a touch-sensitive screen, a touchpad, a keypad, a microphone or microphone array, etc.), and one or more output devices 180 (e.g., a display, a speaker, a printer, etc.).

[0034] One or more wireless transceivers 178 may receive wireless signals (e.g., signal 188) from one or more other devices via antenna 187, such as other user devices, network devices (e.g., base stations such as evolved Node B (eNB) and / or gNodeB (gNB)), WiFi access points (APs) such as routers, range extenders, etc.), cloud networks, etc. In some examples, computing system 170 may include an antenna array or multiple antennas that can facilitate simultaneous transmission and reception functionality. Antenna 187 may be an omnidirectional antenna so that RF signals can be received from all directions and RF signals can be transmitted in all directions. Wireless signal 188 may be sent via a wireless network. The wireless network may be any wireless network, such as a cellular or telecommunications network (e.g., 3G, 4G, 5G, etc.), a wireless local area network (e.g., a WiFi network), a Bluetooth TM network and / or other networks. In some examples, one or more wireless transceivers 178 may include an RF front end that includes one or more components such as an amplifier, a mixer for down-converting a signal (also referred to as a signal multiplier), a frequency synthesizer (also referred to as an oscillator) that provides a signal to the mixer, a baseband filter, an analog-to-digital converter (ADC), one or more power amplifiers, and other components. The RF front end may generally handle the selection of the wireless signal 188 and the conversion of the wireless signal to baseband or an intermediate frequency, and may convert the RF signal to the digital domain.

[0035] In some cases, computing system 170 may include a coding-decoding device (or CODEC) configured to encode and / or decode data transmitted and / or received using one or more wireless transceivers 178. In some cases, computing system 170 may include an encryption-decryption device or component configured to encrypt and / or decrypt data transmitted and / or received by one or more wireless transceivers 178 (e.g., in accordance with the Advanced Encryption Standard (AES) and / or Data Encryption Standard (DES) standards).

[0036] One or more SIMs 174 may each securely store an International Mobile Subscriber Identity (IMSI) number and associated keys assigned to a user of the electronic device 107. The IMSI and keys may be used to identify and authenticate a subscriber when accessing a network provided by a network service provider or operator associated with one or more SIMs 174. One or more modems 176 may modulate one or more signals to encode information for transmission using one or more wireless transceivers 178. One or more modems 176 may also demodulate signals received by one or more wireless transceivers 178 to decode the transmitted information. In some examples, one or more modems 176 may include a WiFi modem, a 4G (or LTE) modem, a 5G (or NR) modem, and / or other types of modems. One or more modems 176 and one or more wireless transceivers 178 may be used to communicate data of one or more SIMs 174.

[0037] The computing system 170 may also include (and / or be in communication with) one or more non-transitory machine-readable storage media or storage devices (e.g., one or more memory devices 186), which may include, but are not limited to, local and / or network accessible storage, disk drives, drive arrays, optical storage devices, solid-state storage devices (such as RAM and / or ROM), which may be programmable, flash-updatable, etc. Such storage devices may be configured to implement any suitable data storage, including, but not limited to, various file systems, database structures, etc.

[0038] In various aspects, the functionality may be stored as one or more computer program products (e.g., instructions or code) in the memory device 186 and executed by the one or more processors 184 and / or the one or more DSPs 182. The computing system 170 may also include software elements (e.g., located within the one or more memory devices 186) including, for example, an operating system, device drivers, executable libraries, and / or other code, such as one or more applications, which may include computer programs that implement the functionality provided by various aspects and / or may be designed to implement methods and / or configure systems, as described herein.

[0039] In some aspects, the electronic device 107 may include means for performing the operations described herein. The means may include one or more components of the computing system 170. For example, the means for performing the operations described herein may include one or more of the input device 172, the SIM 174, the modem 176, the wireless transceiver 178, the output device 180, the DSP 182, the processor 184, the memory device 186, and / or the antenna 187.

[0040] Figure 2 is a diagram illustrating an example of a wireless device 200 detecting a target 202 in the form of a vehicle using RF monostatic sensing techniques that may be employed by the systems and techniques described herein for RF sensing according to some examples. In particular, Figure 2 is a diagram illustrating an example of a wireless device 200 that utilizes RF sensing technology (e.g., monostatic sensing) to perform one or more functions, such as detecting the presence and location of a target 202 (e.g., an object, user, or vehicle), which in this figure is shown in the form of a vehicle.

[0041] In some examples, wireless device 200 may be a mobile phone, a tablet computer, a wearable device, a vehicle, an XR device, a computing device or component of a vehicle, or other device including at least one RF interface (e.g., Figure 1 In some examples, the wireless device 200 may be a user device (e.g., Figure 1 An electronic device 107) provides connectivity, such as a base station (e.g., gNB, eNB, etc.), a wireless access point (AP), or other device including at least one RF interface.

[0042] In some aspects, the wireless device 200 may include one or more components for transmitting RF signals. The wireless device 200 may include at least one processor 204 that can determine the signal to be transmitted (e.g., determine the waveform of the signal) and can process received signals. The signal to be transmitted is provided to the RF transmitter 206 for transmission. The RF transmitter 206 may be a Wi-Fi transmitter, a 5G / NR transmitter, a Bluetooth transmitter, or a wireless transmitter. TM transmitter or any other transmitter capable of transmitting RF signals.

[0043] The RF transmitter 206 may be coupled to one or more transmit antennas, such as a Tx antenna 212. In some examples, the transmit (Tx) antenna 212 may be an omnidirectional antenna capable of transmitting RF signals in all directions. For example, the Tx antenna 212 may be an omnidirectional Wi-Fi antenna capable of radiating Wi-Fi signals (e.g., 2.4 GHz, 5 GHz, 6 GHz, etc.) in a 360-degree radiation pattern. In another example, the Tx antenna 212 may be a directional antenna that transmits RF signals in a specific direction.

[0044] In some examples, the wireless device 200 may also include one or more components for receiving RF signals. For example, the receiver array in the wireless device 200 may include one or more receive antennas, such as a receive (Rx) antenna 214. In some examples, the Rx antenna 214 may be an omnidirectional antenna capable of receiving RF signals from multiple directions. In other examples, the Rx antenna 214 may be a directional antenna configured to receive signals from a specific direction. In another example, both the Tx antenna 212 and the Rx antenna 214 may include multiple antennas (e.g., elements) configured as an antenna array.

[0045] The wireless device 200 may also include an RF receiver 210 coupled to an Rx antenna 214. The RF receiver 210 may include a signal for receiving RF waveforms such as Wi-Fi signals, Bluetooth signals, TM The output of the RF receiver 210 may be coupled to at least one processor 204. The processor 204 may be configured to process a received waveform (e.g., Rx waveform 218).

[0046] In one example, the wireless device 200 may implement an RF sensing technique, such as a single-station sensing technique, by transmitting a Tx waveform 216 from the Tx antenna 212. Although the Tx waveform 216 is illustrated as a single line, in some cases, the Tx waveform 216 may be transmitted in all directions by the omnidirectional Tx antenna 212. In one example, the Tx waveform 216 may be a Wi-Fi waveform transmitted by a Wi-Fi transmitter in the wireless device 200. In some cases, the Tx waveform 216 may correspond to a Wi-Fi waveform transmitted simultaneously or nearly simultaneously with a Wi-Fi data communication signal or a Wi-Fi control function signal (e.g., a beacon transmission). In some examples, the Tx waveform 216 may be transmitted using the same or similar frequency resources as the Wi-Fi data communication signal or the Wi-Fi control function signal (e.g., a beacon transmission). In some aspects, the Tx waveform 216 may correspond to a Wi-Fi waveform transmitted separately from the Wi-Fi data communication signal and / or the Wi-Fi control signal (e.g., the Tx waveform 216 may be transmitted at a different time and / or using different frequency resources).

[0047] In some examples, Tx waveform 216 may correspond to a 5G NR waveform that is sent simultaneously or nearly simultaneously with a 5G NR data communication signal or a 5G NR control function signal. In some examples, Tx waveform 216 may be sent using the same or similar frequency resources as the 5G NR data communication signal or the 5G NR control function signal. In some aspects, Tx waveform 216 may correspond to a 5G NR waveform that is sent separately from the 5G NR data communication signal and / or the 5G NR control signal (e.g., Tx waveform 216 may be sent at a different time and / or using different frequency resources).

[0048] In some aspects, one or more parameters associated with the Tx waveform 216 may be modified, which may be used to increase or decrease the RF sensing resolution. These parameters may include frequency, bandwidth, number of spatial streams, number of antennas configured to transmit the Tx waveform 216, number of antennas configured to receive reflected RF signals (e.g., Rx waveform 218) corresponding to the Tx waveform 216, number of spatial links (e.g., number of spatial streams multiplied by number of antennas configured to receive RF signals), sampling rate, or any combination thereof. The transmitted waveform (e.g., Tx waveform 216) and the received waveform (e.g., Rx waveform 218) may include one or more radar RSs (e.g., also referred to as RF sensing RSs). In some examples, the Tx waveform 216 and / or the Rx waveform 218 may include Figure 6 Waveform 600. In some examples, Tx waveform 216 and / or Rx waveform 218 may additionally or alternatively include one or more OFDM waveforms.

[0049] In some aspects, the wireless device 200 can implement RF sensing techniques by performing alternating transmit and receive functions (e.g., performing half-duplex operation). For example, the wireless device 200 can alternately enable its RF transmitter 206 to transmit Tx waveform 216 when the RF receiver 210 is not enabled to receive (e.g., not receiving), and enable its RF receiver 210 to receive Rx waveform 218 when the RF transmitter 206 is not enabled to transmit (e.g., not transmitting). When the wireless device 200 performs half-duplex operation, the wireless device 200 can transmit an OFDM waveform containing a non-continuous radar RS or other waveforms described herein.

[0050] In other aspects, the wireless device 200 can implement RF sensing techniques by performing concurrent transmit and receive functions (e.g., performing full-duplex operation). For example, the wireless device 200 can enable its RF receiver 210 to receive at or substantially the same time as it enables its RF transmitter 206 to transmit the Tx waveform 216. When the wireless device 200 performs full-duplex operation, the wireless device 200 can transmit an OFDM waveform.

[0051] In some examples, the transmission of a sequence or pattern included in the Tx waveform 216 may be continuously repeated such that the sequence is transmitted a certain number of times or for a certain duration. In some examples, in the case where the RF receiver 210 is enabled after the RF transmitter 206, the repetition of the pattern in the transmission of the Tx waveform 216 may be used to avoid missing the reception of any reflected signals. In one example implementation, the Tx waveform 216 may include a sequence having a sequence length L (e.g., the length of one time slot of the waveform) transmitted two or more times, which may allow the RF receiver 210 to be enabled at a time less than or equal to L in order to receive reflections corresponding to the entire sequence without losing any information.

[0052] By implementing alternating or simultaneous transmit and receive functionality (e.g., half-duplex or full-duplex operation), the wireless device 200 may receive a signal corresponding to the Tx waveform 216. For example, the wireless device 200 may receive a signal reflected from an object or person within the range of the Tx waveform 216, such as the Rx waveform 218 reflected from the target 202. The wireless device 200 may also receive a leakage signal (e.g., Tx leakage signal 220) that is directly coupled from the Tx antenna 212 to the Rx antenna 214 without being reflected from any object. For example, the leakage signal may include a signal that is transmitted from a transmitter antenna (e.g., Tx antenna 212) on the wireless device and to a receive antenna (e.g., Rx antenna 214) on the wireless device without being reflected from any object. In some cases, the Rx waveform 218 may include multiple sequences corresponding to multiple copies of the sequence included in the Tx waveform 216. In some examples, the wireless device 200 may combine multiple sequences received by the RF receiver 210 to improve the signal-to-noise ratio (SNR).

[0053] The wireless device 200 may also implement RF sensing techniques by obtaining RF sensing data associated with each of the received signals corresponding to the Tx waveform 216. In some examples, the RF sensing data may include channel state information (CSI) data related to a direct path of the Tx waveform 216 (e.g., the leakage signal 220) and data related to a reflected path corresponding to the Tx waveform 216 (e.g., the Rx waveform 218).

[0054] In some aspects, the RF sensing data (e.g., CSI data) may include information that can be used to determine the manner in which an RF signal (e.g., Tx waveform 216) propagates from the RF transmitter 206 to the RF receiver 210. The RF sensing data may include data corresponding to the effects on the transmitted RF signal due to scattering, fading, and / or power attenuation with distance, or any combination thereof. In some examples, the RF sensing data may include imaginary data and real data (e.g., I / Q components) corresponding to each tone in the frequency domain over a particular bandwidth.

[0055] In some examples, the RF sensing data can be used by the processor 204 to calculate the distance and arrival angle corresponding to the reflected waveform, such as the Rx waveform 218. In other examples, the RF sensing data can also be used to detect motion, determine position, detect changes in position or motion patterns, or any combination thereof. In some cases, the distance and arrival angle of the reflected signal can be used to identify the size, position, movement, and / or orientation of a target (e.g., target 202) in the surrounding environment in order to detect target presence / proximity.

[0056] The processor 204 of the wireless device 200 may calculate the distance and arrival angle corresponding to the reflected waveform (e.g., the distance and arrival angle corresponding to the Rx waveform 218) by utilizing signal processing, machine learning algorithms, any other suitable techniques, or any combination thereof. In other examples, the wireless device 200 may send or transmit the RF sensing data to at least one processor of another computing device such as a server, which may perform calculations to obtain the distance and arrival angle corresponding to the Rx waveform 218 or other reflected waveforms.

[0057] In one example, the distance of the Rx waveform 218 may be calculated by measuring the time difference from when the leakage signal is received to when the reflected signal is received. For example, the wireless device 200 may determine a baseline distance zero based on the difference (e.g., propagation delay) from the time the wireless device 200 transmits the Tx waveform 216 to the time it receives the leakage signal 220. The processor 204 of the wireless device 200 may then determine the distance associated with the Rx waveform 218 based on the difference (e.g., time of flight) from the time the wireless device 200 transmits the Tx waveform 216 to the time it receives the Rx waveform 218, which may then be adjusted based on the propagation delay associated with the leakage signal 220. In doing so, the processor 204 of the wireless device 200 may determine the distance traveled by the Rx waveform 218, which may be used to determine the presence and movement of the target (e.g., target 202) causing the reflection.

[0058] In another example, the angle of arrival of the Rx waveform 218 may be calculated by the processor 204 by measuring the time difference of arrival of the Rx waveform 218 between individual elements of the receive antenna array, such as the antenna 214. In some examples, the time difference of arrival may be calculated by measuring the receive phase difference at each element in the receive antenna array.

[0059] In some cases, the distance and angle of arrival of the Rx waveform 218 may be used by the processor 204 to determine the distance between the wireless device 200 and the target 202 and the orientation of the target 202 relative to the wireless device 200. The distance and angle of arrival of the Rx waveform 218 may also be used to determine the presence, movement, proximity, identity, or any combination thereof of the target 202. For example, the processor 204 of the wireless device 200 may utilize the calculated distance and angle of arrival corresponding to the Rx waveform 218 to determine that the target 202 is moving toward the wireless device 200.

[0060] As noted above, the wireless device 200 may include a mobile device (e.g., an IoT device, a smart phone, a laptop, a tablet, etc.) or other type of device. In some examples, the wireless device 200 may be configured to obtain device location data and device orientation data in addition to the RF sensing data. In some instances, the device location data and device orientation data may be used to determine or adjust the distance and angle of arrival of a reflected signal such as the Rx waveform 218. For example, when the target 202 moves toward the wireless device 200 during RF sensing, the wireless device may be placed on a table facing the sky. In this instance, the wireless device 200 may use its location data and orientation data in addition to the RF sensing data to determine the direction in which the target 202 is moving.

[0061] In some examples, the wireless device 200 can collect device location data using techniques including round trip time (RTT) measurements, passive positioning, angle of arrival, received signal strength indication (RSSI), CSI data, using any other suitable technique, or any combination thereof. In other examples, device orientation data can be obtained from electronic sensors on the wireless device 200 such as a gyroscope, accelerometer, compass, magnetometer, barometer, any other suitable sensor, or any combination thereof.

[0062] Figure 3 304 is a diagram illustrating an example of a receiver 304 in the form of a vehicle that utilizes RF bistatic sensing techniques to perform one or more functions that may be employed by the techniques of the systems described herein for RF sensing. For example, the receiver 304 may use RF bistatic sensing to detect the presence and location of a target 302 (e.g., an object, user, or vehicle) in the form of a vehicle. Figure 3 It is illustrated in the form of a vehicle.

[0063] Figure 3 The bi-static radar system of includes a transmitter 300 (which is depicted in this figure, for example, in the form of a base station) and a receiver 304, the transmitter and the receiver being separated by a distance comparable to the expected target distance. Figure 2 Compared with the single-station system, Figure 3The transmitter 300 and the receiver 304 of the bistatic radar system are located far away from each other. In contrast, a monostatic radar is a radar system that includes transmitters (e.g., Figure 2 RF transmitter 206 of the wireless device 200) and receiver (e.g., Figure 2 The RF receiver 210 of the wireless device 200) is a radar system (e.g., Figure 2 system).

[0064] An advantage of a bistatic radar (or more generally, a multistatic radar with more than one receiver) over a monostatic radar is the ability to collect radar echoes returning from a scene at angles different than the angle at which the pulse was transmitted. This may be of interest in some applications (e.g., vehicle applications, scenes with multiple objects, military applications, etc.) where targets may reflect transmitted energy in many directions (e.g., where targets are specifically designed to reflect in many directions), which may minimize the energy reflected back to the transmitter. It should be noted that in one or more examples, a monostatic system may coexist with a multistatic radar system, such as when the transmitter also has a co-located receiver.

[0065] In some examples, Figure 3 The transmitter 300 and / or the receiver 304 may be a mobile phone, a tablet computer, a wearable device, a vehicle, or other device including at least one RF interface (e.g., Figure 1 In some examples, the transmitter 300 and / or the receiver 304 may be a user device (e.g., Figure 1 IoT device 107) provides connectivity to the device, such as a base station (e.g., gNB, eNB, etc.), a wireless access point (AP), or other device including at least one RF interface.

[0066] In some aspects, transmitter 300 may include one or more components for transmitting RF signals. Transmitter 300 may include at least one processor (e.g., Figure 2 The transmitter 300 may also include an RF transmitter (eg, Figure 2 The RF transmitter 206 may be a transmitter configured to transmit a cellular or telecommunication signal (e.g., a transmitter configured to transmit a 5G / NR signal, a 4G / LTE signal, or other cellular / telecommunication signal, etc.), a Wi-Fi transmitter, a Bluetooth transmitter, or a wireless transmitter. TM transmitter, any combination thereof, or any other transmitter capable of transmitting RF signals.

[0067] The RF transmitter may be coupled to one or more transmit antennas, such as a Tx antenna (e.g., coupled to Figure 2 TX antenna 212). In some examples, the Tx antenna may be an omnidirectional antenna capable of transmitting RF signals in all directions, or a directional antenna capable of transmitting RF signals in a specific direction. In some examples, the Tx antenna may include multiple antennas (e.g., elements) configured as an antenna array.

[0068] The receiver 304 may also include one or more components for receiving RF signals. For example, the receiver 304 may include one or more receiving antennas, such as an Rx antenna (e.g., Figure 2 In some examples, the Rx antenna may be an omnidirectional antenna capable of receiving RF signals from multiple directions, or a directional antenna configured to receive signals from a specific direction. In another example, the Rx antenna may include multiple antennas (e.g., elements) configured as an antenna array.

[0069] The receiver 304 may also include an RF receiver (eg, Figure 2 The RF receiver may include a RF receiver 210 for receiving RF waveforms (such as Wi-Fi signals, Bluetooth TM The output of the RF receiver may be coupled to at least one processor (e.g., Figure 2 The processor may be configured to process the received waveform (eg, Rx waveform 318).

[0070] In one or more examples, the transmitter 300 can implement an RF sensing technique (e.g., a bistatic sensing technique) by transmitting a Tx waveform 316 from a Tx antenna. It should be noted that although the Tx waveform 316 is illustrated as a single line, in some cases, the Tx waveform 316 can be transmitted in all directions by an omnidirectional Tx antenna.

[0071] In one or more aspects, one or more parameters associated with the Tx waveform 316 can be used to increase or decrease the RF sensing resolution. These parameters can include frequency, bandwidth, number of spatial streams, number of antennas configured to transmit the Tx waveform 316, number of antennas configured to receive reflected RF signals (e.g., Rx waveform 318) corresponding to the Tx waveform 316, number of spatial links (e.g., number of spatial streams multiplied by number of antennas configured to receive RF signals), sampling rate, or any combination thereof. The transmit waveform (e.g., Tx waveform 316) and the receive waveform (e.g., Rx waveform 318) can include one or more radar RSs (also referred to as RF sensing RSs). In some examples, the Tx waveform 316 and / or the Rx waveform 318 can include Figure 6 Waveform 600. In some examples, Tx waveform 316 and / or Rx waveform 318 may additionally or alternatively include one or more OFDM waveforms.

[0072] During operation, the receiver 304 may receive a signal corresponding to the Tx waveform 216. For example, the receiver 304 may receive a signal reflected from an object or person within the range of the Tx waveform 316, such as the Rx waveform 318 reflected from the target 302. In some cases, the Rx waveform 318 may include multiple sequences corresponding to multiple copies of the sequence included in the Tx waveform 316. In some examples, the receiver 304 may combine the received multiple sequences to improve the signal-to-noise ratio (SNR).

[0073] In some examples, at least one processor within receiver 304 can use the RF sensing data to calculate a distance, angle of arrival, or other characteristics corresponding to a reflected waveform, such as Rx waveform 318. In other examples, the RF sensing data can also be used to detect motion, determine position, detect a change in position or motion pattern, or any combination thereof. In some cases, the distance and angle of arrival of the reflected signal can be used to identify the size, position, movement, and / or orientation of a target (e.g., target 302) in the surrounding environment in order to detect target presence / proximity.

[0074] The processor of the receiver 304 may calculate the distance and angle of arrival corresponding to the reflected waveform (e.g., the distance and angle of arrival corresponding to the Rx waveform 318) by using signal processing, machine learning algorithms, any other suitable techniques, or any combination thereof. In other examples, the receiver 304 may send or transmit the RF sensing data to at least one processor of another computing device (such as a server), which may perform calculations to obtain the distance and angle of arrival corresponding to the Rx waveform 318 or other reflected waveforms.

[0075] In one or more examples, the angle of arrival of the Rx waveform 218 can be calculated by the processor of the receiver 304 by measuring the time difference of arrival of the Rx waveform 318 between individual elements of the receive antenna array of the receiver 304. In some examples, the time difference of arrival can be calculated by measuring the receive phase difference at each element in the receive antenna array.

[0076] In some cases, the distance and angle of arrival of the Rx waveform 318 may be used by the processor of the receiver 304 to determine the distance between the receiver 304 and the target 302 and the orientation of the target 302 relative to the receiver 304. The distance and angle of arrival of the Rx waveform 318 may also be used to determine the presence, movement, proximity, identity, or any combination thereof of the target 302. For example, the processor of the receiver 304 may use the calculated distance and angle of arrival corresponding to the Rx waveform 318 to determine that the target 302 is moving toward the receiver 304.

[0077] Figure 4 is a diagram illustrating examples of geometries for bistatic (or monostatic) sensing according to some examples. Although a bistatic radar example is shown, the same or similar operating principles may be applied to multistatic radars utilizing more than two transmitters / receivers. For example, a multistatic radar may utilize one transmitter and two receivers. In another example, a multistatic radar may utilize two transmitters and one receiver. A larger number of transmitters and / or receivers is also possible.

[0078] like Figure 4 As shown in FIG. 4 , the relationship between a transmitter 400, a target 402, and a receiver 404 of a radar system is shown. The transmitter 400 and the receiver 404 are separated by a baseline distance L, and the target 402 and the transmitter 400 are separated by a distance R. T , and the target 402 and the receiver 404 are separated by a distance R R .

[0079] In operation, the transmitter 400 transmits a transmit signal 408 which travels across a distance R T The transmitted signal 408 is reflected from the target 402 and becomes an echo signal 410, which travels a distance R R Arrives at receiver 404. The primary function served by the example bistatic radar system may be sensing the range or distance R from target 402 to receiver 404. R The system mainly senses the transmission signal 408 and the echo signal 410 through the total distance R 总和 The amount of time it takes to determine the range R R , the total distance is R T and R R The sum of (for example, R 总和 =RT +R R ).

[0080] Total distance R 总和 An ellipsoid (also referred to as an iso-range contour, for example) may be defined with its foci at the locations of the transmitter 400 and the receiver 404, respectively. The ellipsoid represents the distance at which the transmitter 400 and the receiver 404 are located. 总和 In the case of , all possible positions of the target 402 . Figure 4 An example of a bistatic radar system capable of measuring the distance R 总和 For example, in the case where perfect timing synchronization between the transmitter 400 and the receiver 404 can be assumed, the time duration T between the moment when the transmitter 400 transmits the transmit signal 408 and the moment when the receiver 404 receives the echo signal 410 is simply measured. 总和 It will be easy. 总和 Multiply the speed of the signal through free space (e.g., approximately c = 3*10 8 m / s) will give R 总和 Thus, the ellipsoids of all possible positions of the target 402 can be obtained by measuring the “time of flight” T of the bistatic radar signal. 总和 To obtain.

[0081] According to some examples, the distance R 总和 The measurements may be made without tight time synchronization between transmitter 400 and receiver 404. For example, line of sight (LOS) signal 412 may be transmitted from transmitter 400 to receiver 404. That is, at the same time that transmitter 400 transmits transmit signal 408 toward target 402, transmitter 400 may also transmit LOS signal 412 toward receiver 404. In some cases, transmit signal 408 may correspond to a main lobe of a transmit antenna beam pattern emitted from transmitter 400, while LOS signal 412 corresponds to a side lobe of the same transmit antenna beam pattern transmitted from transmitter 400.

[0082] Receiver 404 receives both echo signal 410 and LOS signal 412 and can use the reception timing of these two signals to measure the total distance R 总和 ,Right now Here, T Rx_echo is the reception time of the echo signal 410. RxLOS is the reception time of LOS signal 412. As previously described, c is the speed of a signal through free space (e.g., the speed of light). L is the baseline distance between transmitter 400 and receiver 404. Once R is found 总和 , which can be used to calculate the target range R R (i.e., the distance between target 402 and receiver 404), i.e.

[0083] Figure 4 The example bistatic radar system illustrated in FIG. 4 can also be used to determine the angle of arrival (AoA) θ at which the return signal 410 is received by the receiver 404. R For example, AoAθ may be estimated by using an antenna array at receiver 404 R In some cases, receiver 404 may utilize an antenna array including multiple antenna elements, where the antenna array may operate as a programmable directional antenna capable of sensing the angle of a received signal. In this example, using the antenna array, receiver 404 may sense the angle of arrival of echo signal 410. In another example, multilateration may be used to estimate AoAθ R Multilateration is the process of determining the intersection of two or more curves or surfaces that represent possible locations of an object. For example, Figure 4 The bistatic radar system illustrated in FIG. 4 may define a first ellipsoidal surface representing the possible locations of target 402, as previously described. A second bistatic radar system having a differently positioned transmitter and / or receiver may define a different second ellipsoidal surface also representing the possible locations of target 402. The intersection of the first ellipsoidal surface and the second ellipsoidal surface may reduce the possible locations of target 402. In three-dimensional space, four such ellipsoidal surfaces would typically be required to reduce the possible locations to a single point, thereby identifying the location of target 402. In two-dimensional space (e.g., assuming that all transmitters, receivers, and targets are confined to objects on the ground), three such ellipsoidal surfaces would typically be required (for two-dimensional space, the ellipsoidal surfaces are reduced to elliptical curves) to reduce the possible locations to a single point, thereby identifying the location of target 402. Multilateration may also be achieved in a similar manner using a multistatic radar system instead of multiple bistatic radar systems.

[0084] In some cases, the transmitter 400 may control the angle of departure (AoD) and / or spread angle of the TX beam (e.g., the transmit signal 408). For example, the transmitter 400 may include an antenna array that can be controlled by applying appropriate weights to the antenna elements of the antenna array. The AoD may also be referred to as a "line of sight direction," which is the direction of the central axis of the TX beam (e.g., the transmit signal 408). In some examples, the direction may be multi-dimensional and may include one or more parameters specified with reference to a coordinate system (e.g., a spherical coordinate system). For example, a particular AoD direction may include an azimuth value (e.g., an azimuth, i.e., a horizontal angle ranging from 0 to 360 degrees) and a vertex value (e.g., a vertex angle, i.e., a vertical angle ranging from 0 to 90 degrees).

[0085] Figure 4The example bistatic radar system illustrated in FIG. 4 can also be used to determine the Doppler frequency associated with target 402. From the perspective of receiver 404, the Doppler frequency represents the relative speed of target 402 (e.g., the speed at which target 402 is approaching / moving away from receiver 404). For a stationary transmitter 400 and a stationary receiver 404, the Doppler frequency of target 402 can be calculated as Here, f D is the Doppler frequency, v is the velocity of the target 402 relative to the fixed reference frame defined by the stationary transmitter 400 and the receiver 404. β is the angle formed between the transmitted signal 408 and the return signal 410 at the target 402. δ is the angle between the velocity vector v and the central ray (e.g., half angle) defined within the angle β. Figure 4 In the context of the example of , a fixed reference frame can be defined with respect to a stationary transmitter 400 and a stationary receiver 404. For example, a baseline of length L can be drawn between the transmitter 400 and the receiver 404. Figure 4 As depicted in , the baseline may extend beyond the transmitter 400 and the receiver 404 to form an extended baseline. One or more normal lines may be drawn perpendicular to the baseline. The transmission angle θ may be defined relative to the normal line drawn from the position of the transmitter 400. T The receiving angle θ may be defined relative to a normal line drawn from the position of the receiver 404. R , which is also referred to above as the angle of arrival (AoA). As previously mentioned, the operation Figure 4 The example bistatic radar system illustrated in FIG. 1 is used to sense targets in two-dimensional space or three-dimensional space. In the case of three-dimensional space, additional degrees of freedom are introduced. However, the same basic principles apply and similar calculations can be performed.

[0086] As mentioned earlier, in Figure 4 In the geometry of FIG. 4 , the bistatic angle β is the angle between the transmitter 400, the target 402, and the receiver 404 in the radar. When the bistatic angle is exactly zero (0), the radar is considered to be a monostatic radar; when the bistatic angle is close to zero, the radar is considered to be a pseudo-monostatic radar; and when the bistatic angle is close to 180 degrees, the radar is considered to be a forward scatter radar. Otherwise, the radar is simply considered to be and is referred to as a bistatic radar. The bistatic angle β can be used to determine the radar cross section of the target.

[0087] During operation, most radar systems send out pulses continuously in order to establish a distinguishable echo. The repetition rate of these pulses is determined by the system's mission. Different sensing schemes (e.g., monostatic sensing and bistatic sensing) have different requirements for the periodicity of radar reference signal (RS) transmission, which can be expressed as T 感测 (For example, as described below Figure 6 600).

[0088] For monostatic sensing (e.g., when the bistatic angle is equal to zero), the data period of the waveform can be expressed as T 感测 (For example, Figure 6 When performing single-station sensing, the radar system can determine to eliminate range ambiguity (e.g., to avoid aliasing problems), where R max is the maximum sensing range, T radar_RS is the duration of the radar RS (e.g., reference Figure 6 If the radar RS repetition frequency is too high (e.g., the radar RS period is too small), the echo signals (e.g., the reflected signals) from some targets may be reflected by the second radar RS (e.g., Figure 6 The radar RS 620b is an example of a second radar RS, and Figure 6 The radar RS 620a (which is an example of the first radar RS) arrives after it is transmitted, which causes ambiguity in the distance measurement. The distance of such an echo appears to be much shorter than the actual distance of the target. It should be noted that typically, the receiver can assume that the echo comes from the second radar RS, not the first radar RS.

[0089] For bistatic sensing, the data period of the waveform (e.g., reference Figure 6 The waveform 600) is also T 感测 The minimum radar RS period in the bi-static configuration is different from the mono-static case. To obtain an unambiguous solution, the leading and trailing edges of the radar RS from transmitter to target to receiver will follow an elliptical shape (e.g. Figure 5 Dual station range 510).

[0090] The leading edge of the radar RS can be determined as: R T +R R =L+cT 感测 The trailing edge of the radar RS can be determined as: R T +R R =L+c(T 感测 -T radar_RS ).

[0091] For bistatic sensing, the radar system can determine to eliminate range ambiguity (e.g., to avoid aliasing problems). It should be noted that the condition may include the surface of the maximum bistatic distance being smaller than the bistatic surface of the trailing edge of the radar RS. When L is set equal to zero, the above T for the bistatic scenario is 感测 The equation can also be used for single-station sensing to eliminate range ambiguity.

[0092] Figure 55 is a diagram illustrating an example of a bistatic range 510 for bistatic sensing according to some examples. In the figure, a transmitter (Tx) 500, a target 502, and a receiver (Rx) 504 of a radar are shown relative to each other. The transmitter 500 and the receiver 504 are separated by a baseline distance L, the target 502 and the transmitter 500 are separated by a distance Rtx, and the target 502 and the receiver 504 are separated by a distance Rrx.

[0093] Bistatic range 510 (shown as an ellipse) refers to the range measurements made by a radar having a separate transmitter 500 and receiver 504 (e.g., the transmitter 500 and receiver 504 are located far from each other). Receiver 504 measures the difference in arrival time from when transmitter 500 transmits a signal to when receiver 504 receives the signal from transmitter 500 via target 502. Bistatic range 510 defines an ellipse of constant bistatic range, called an equidistant contour, on which target 502 is located, with the focus centered on transmitter 500 and receiver 504. If target 502 is at range Rrx from receiver 504 and at range Rtx from transmitter 500, and receiver 504 and transmitter 500 are at a distance L from each other, then the bistatic range is equal to Rrx+Rtx-L. It should be noted that the motion of target 502 results in a rate of change of the bistatic range, which results in a bistatic Doppler shift.

[0094] Typically, the constant bistatic range point draws an ellipse with the transmitter 500 and receiver 504 positioned as the foci. The bistatic isometric contour is the location of the ground slice through the ellipse. When the ground is flat, the intercepts form an ellipse (e.g., representing the bistatic range 510). Note that these ellipses are not centered on the mirror point except when the two platforms have equal heights.

[0095] Figure 6 6 is a diagram showing an example of a waveform 600 that can be employed by the systems and techniques described herein for RF sensing according to some examples. In this figure, the waveform 600 includes multiple communication instances (communication signals) 610a, 610b and multiple radar reference signals (RS) 620a, 620b, where the communication instances 610a, 610b and the radar RS 620a, 620b alternate with each other. Each communication instance 610a, 610b includes a communication symbol (e.g., one OFDM symbol) formed by multiple bits.

[0096] Each radar RS 620a, 620b includes an RF sensing signal for RF sensing (eg, single static sensing or dual static sensing). The length (duration) of a single radar RS is T radar_RS , and the radar RS sends a period of T 感测 It should be noted that this waveform 600 is compatible with a receiver performing full-duplex operation.

[0097] As described above, RF sensing can be used to detect the presence and location of targets such as objects, users, vehicles, etc. In some aspects, RF sensing can also be used to identify the type or category of objects that have been detected and / or located. Also as described above, in some cases, a specific type of object can be detected or identified based on the radar cross-section (RCS) of the specific type of object. When detecting relatively small objects (e.g., objects with a size close to the minimum detectable RCS size and / or the minimum detectable RCS resolution of a given radar sensing system), RCS may be unreliable. For example, an air surveillance radar sensing system designed to detect an aircraft based on the aircraft's RCS may be unreliable, or even unable to detect much smaller objects, such as drones or unmanned aerial vehicles (UAVs).

[0098] Even if a radar sensing system is designed to be sensitive enough to detect relatively small objects, such as drones or UAVs, RCS information alone is generally insufficient to absolutely identify a drone or UAV. For example, the use of RCS information may be unreliable when multiple objects of different types have the same or similar RCS size (e.g., causing an object of a first type to be incorrectly identified as an object of a second type with a similar RCS, and vice versa). Birds have a similar physical size as many drones and UAVs, and birds may also fly at similar altitudes and / or speeds. Therefore, a radar sensing system may incorrectly identify a bird as a drone, and vice versa, because birds and drones may have similar RCS and / or similar general radar behavior patterns or characteristics.

[0099] In some examples, a radar sensing system (e.g., a single station, dual station, and / or multi-station) can identify a target based on one or more Doppler characteristics (e.g., Doppler domain characteristics) associated with the target. For example, the Doppler domain characteristics can be used to identify a given target and / or a given type of target. In an illustrative example, the one or more Doppler domain characteristics can include a micro-Doppler signature associated with the target. The micro-Doppler signature associated with a given type of target can include one or more special radar micro-Doppler properties of the given target type, as described below with respect to Figure 7 . In some cases, a micro-Doppler signature may include one or more characteristics (e.g., Doppler characteristics and / or micro-Doppler characteristics) for identifying a given target or a given type of target. Radar micro-Doppler information may be obtained from or generated using one or more radar signals returned from (e.g., reflected by) a target. Micro-Doppler information may be generated due to micro-motions of various components within the target. In the example of a drone or UAV, micro-Doppler may be generated due to the rotation of propeller blades. In the example of a bird, micro-Doppler may be generated due to the flapping of wings.

[0100] Figure 7 is a diagram depicting example micro-Doppler signatures associated with different types of targets. Figure 7 The micro-Doppler signatures depicted in are associated with different types of aerial (e.g., airborne or flying) targets, but it should be noted that micro-Doppler signatures may also be obtained for other types of targets, including terrestrial or non-aerial targets. As illustrated, a first micro-Doppler signature 700a may be associated with a bird, a second micro-Doppler signature 700b may be associated with a quadrotor or other unmanned aerial vehicle (UAV), and a third micro-Doppler signature 700c may be associated with a helicopter. Micro-Doppler signatures 700a-700c are illustrated as spectrograms that show how the micro-Doppler properties of the three target types differ in their modulation of the radar return signal. In some examples, the micro-Doppler signature of a given target type may represent a predictable modulation of the radar return signal caused by the unique micro-Doppler properties of the given target type.

[0101] In some examples, the use of micro-Doppler information and / or micro-Doppler signatures can reduce the false alarm rate (FAR) of a radar sensing system designed to detect only certain types or classes of target objects. For example, in a radar sensing system designed to detect drones or UAVs, the FAR triggered by birds can be reduced by analyzing the micro-Doppler information of the radar return signal against the micro-Doppler bird signature 700a and the micro-Doppler UAV signature 700b.

[0102] In some examples, the micro-Doppler signature can be used to distinguish between different types of objects (e.g., birds or UAVs). In some cases, the micro-Doppler signature can be used additionally or alternatively to distinguish between various sub-types of objects (e.g., UAVs of different types or configurations). For example, the micro-Doppler UAV signature can be different based on the presence of micro-Doppler characteristics, such as the number of propellers, propeller rotation rate, propeller blade size (e.g., blade length), etc. As previously described, the micro-Doppler signature and / or micro-Doppler information can be used to implement a radar sensing system with a reduced FAR. Additionally or alternatively, the micro-Doppler signature and / or micro-Doppler information can be used to implement a radar sensing system with improved target tracking (e.g., by determining a unique micro-Doppler signature for a given target, which can then be tracked and / or distinguished from other similar tracked targets based on its unique micro-Doppler signature).

[0103] In an example single-station radar sensing system, the transceiver and receiver are co-located or otherwise integrated into the same system or device. The single-station radar sensing system can perform improved target object detection based on micro-Doppler features by analyzing the (e.g., returned) radar signal reflected from the target. For example, the single-station radar sensing system can analyze the reflected radar signal to identify one or more micro-Doppler patterns based on the non-rigid movement of the target object. One or more micro-Doppler patterns can be extracted from the reflected radar signal and analyzed against one or more known micro-Doppler features and / or micro-Doppler characteristics of a set of different objects or object types. Based on the analysis, the detected target object can be identified or classified, in some cases with different confidence levels. Because the transceiver and receiver are co-located in the single-station radar sensing system, in some cases, micro-Doppler analysis and target identification can be performed with little or no additional signaling overhead.

[0104] In a dual-station radar sensing system or a multi-station radar sensing system, the transceiver and the receiver are separated from each other by a certain baseline distance. In some examples, the dual-station and / or multi-station radar sensing system may include a receiver (e.g., Rx node) that implements a receiving function but does not implement a signal processing function (or only implements a basic signal processing function). In such examples, the dual-station and / or multi-station Rx node can offload the signal processing task to a remote node for data fusion. For example, a dual-station or multi-station Rx node can report its measurement (e.g., received radar return signal) to one or more of a sensing server and a radar Tx node associated with the Rx node for further processing. In some cases, the sensing server may be included in a radar Tx node or co-located with a radar Tx node. In some examples, the sensing server may be located at a core network (e.g., a core network associated with an RF fusion system including dual-station / multi-station Tx and Rx nodes).

[0105] In some examples, as described above, the radar Tx node and Rx node may be included in the RF sensing system. For example, the radar Tx node may be implemented by one or more gNBs. In some cases, if the radar Rx node is a UE, the UE may report its radar signal measurements to a gNB that implements or includes the corresponding radar Tx node. In some examples, the radar Rx node may also be implemented by one or more gNBs, and the radar Rx gNB may report its radar signal measurements to the corresponding radar Tx gNB. In another example, one or more radar Tx nodes (e.g., implemented by one or more UEs and / or gNBs) may report radar signal measurements to a remote server (such as the sensing server described above).

[0106] In dual-station and multi-station radar sensing systems, the signaling and / or reporting of radar signal measurements from the radar Rx node to the radar Tx node and / or a remote server (e.g., a sensing server) introduces reporting overhead. In some cases, the radar Rx measurement reports may cause congestion in the network used to send the radar Rx measurement reports. For example, in an RF sensing system, a radar Rx node implemented by a UE may send the radar Rx measurement reports on the same wireless network used by the UE to communicate voice and / or data signals. For example, as discussed above, Figure 6 Depicts an example waveform that can be used for RF sensing. In such a waveform, only a certain number of communication time slots or total bandwidth can be allocated for the transmission of radar reference signals 620a, 620b. In some examples, the radar reference signal used by the RF sensing system may not be able to accommodate the reporting of a complete picture or time Doppler spectrum frame for target identification based on micro-Doppler signature analysis. For dual-station and / or multi-station radar Rx nodes, systems and techniques are needed to perform micro-Doppler signature reporting with reduced or compressed overhead.

[0107] Systems, devices, processes (also referred to as methods), and computer-readable media (collectively referred to as "systems and techniques") for performing Doppler spectrum measurements with reduced or compressed overhead are described herein. For example, these systems and techniques can be used to send or provide micro-Doppler spectrum measurements from a dual-station and / or multi-station radar Rx node with reduced overhead. In some aspects, a sliding window Fourier transform can be used to generate a micro-Doppler measurement report. In some examples, a sliding window fast Fourier transform (FFT) can be used to generate a micro-Doppler measurement report. The micro-Doppler measurement report can be associated with one or more time domain parameters for a sliding window FFT. In some cases, one or more of the time domain parameters for the sliding window FFT can be determined and / or signaled by a remote server (e.g., a sensing server) or a radar Tx node associated with a dual-station or multi-station radar Rx node.

[0108] In one illustrative example, systems and techniques can be used to compress micro-Doppler measurement reports before transmission. Compression can be performed in the time domain and / or the Doppler domain (e.g., frequency domain). In some examples, a Doppler domain (e.g., frequency domain) basis can be used to generate compressed reports for Doppler (e.g., micro-Doppler) spectrum measurements. In some examples, systems and techniques can generate time domain differential reports over multiple instances of Doppler (e.g., micro-Doppler) spectrum measurements. As will be explained in more depth below, the compressed micro-Doppler measurement report can include one or more reference instances for generating multiple related differential reports for neighboring instances.

[0109] In some aspects, systems and techniques may use parameter model reports to report only a portion of Doppler (e.g., micro-Doppler) spectrum measurements, where a reconstruction of the complete micro-Doppler spectrum measurement may be generated based on the parameter model report. The reconstruction of the complete micro-Doppler spectrum measurement may be used to perform target object identification based on micro-Doppler features. In some examples, a dual-station or multi-station radar Rx node may use one or more classification models to generate one or more classifications for a detected target object. For example, a dual-station or multi-station radar Rx node may use a classification model to generate one or more classifications based on Doppler (e.g., micro-Doppler) spectrum measurements of a target object received at or obtained by a radar Rx node. In some cases, a dual-station or multi-station radar Rx node may generate multiple classifications for a detected target object, and may generate corresponding confidence levels for multiple classifications. In an illustrative example, a micro-Doppler measurement report generated and / or sent by a dual-station or multi-station radar Rx node may include one or more generated classifications and confidence levels.

[0110] Figures 8A to 8C An example of a micro-Doppler measurement report (e.g., 800b, 800c) that can be generated using a radar signal measurement (e.g., 800a) is illustrated. The radar signal measurement 800a can be a time domain signal obtained from or measured by a radar Rx node. In some examples, the radar Rx node can be associated with a dual-station radar sensing system or a multi-station radar sensing system. As previously described, a dual-station or multi-station radar Rx node can be additionally or alternatively associated with an RF sensing system, where the radar Rx node can be implemented by a UE, a gNB, etc.

[0111] Fig. 8A The radar signal measurement 800a is depicted as being covered by a series of sliding windows 810, 820, 830, ..., 890 along the horizontal time axis t. Each sliding window may include a portion of the time domain radar measurement data. For example, the time domain radar measurement data 800a may be a signal with a time-varying amplitude (e.g., the amplitude of the reflected radar signal measured by the radar Rx node at discrete or continuous time values ​​along the horizontal time axis t).

[0112] As illustrated, each sliding window has a window size M. The window size M represents the length of the sliding window and thus represents the amount of time-domain radar measurement data included in each of the sliding windows 810, …, 890. The sliding window size can be an absolute time measurement (e.g., given in time units such as milliseconds (ms)) and / or can be the number of sequential measurement / reception time slots at the radar Rx node associated with the radar measurement data 800a. For example, the first sliding window 810 can include a portion of the radar measurement data 800a over the interval t = [0, 0+M]. In another example, in the case where the window size M is given as the number of reception time slots at the radar Rx node (e.g., 10 time slots), the first sliding window 810 can include a portion of the radar measurement data 800a over the interval [reception time slot 0, reception time slot 9]. In some examples, multiple sliding windows can include multiple different window sizes (e.g., M can take on multiple different values rather than a constant value). In some examples, a greater or fewer number of sliding windows can be utilized.

[0113] Adjacent (e.g., consecutive) sliding windows can be offset from each other by a predetermined displacement along the horizontal time axis t. For example, the second sliding window 820 is shown as being offset to the right relative to the first sliding window 810 (e.g., the second sliding window 820 is generated or obtained by “sliding” to the right from the first sliding window 810). Adjacent sliding windows can have an overlap size L, where L < M. The overlap size L can represent the portion of the radar measurement data 800a included in two adjacent sliding windows. For example, as Fig. 8A illustrated, the overlap size L represents the portion of the radar measurement data 800a included in both the first sliding window 810 and the second sliding window 820. The overlap size L can be given in the same unit as the window size M (e.g., in absolute time units or the number of radar Rx node reception time slots). In some examples, the same overlap size L can be used to generate multiple sliding windows 810, …, 890. In some cases, multiple different overlap sizes L can be utilized.

[0114] In one illustrative example, based on the window size M and the overlap size L, multiple sliding windows (e.g., 810, …, 890) can be generated for a given radar measurement data (e.g., radar measurement data 800a) with a step size of M-L between each pair of adjacent sliding windows. In some cases, sliding windows can be generated until the end of the time-domain radar measurement data 800a is reached, and the multiple sliding windows can thus include a greater or fewer number of sliding windows than Fig. 8A depicted therein.

[0115] In an illustrative example, a sliding window transform can be used to generate a frequency domain micro-Doppler measurement report (e.g., 800b, 800c) using the time domain radar measurement data 800a, as described in more depth below. The frequency domain may also be referred to as the Doppler domain. In some aspects, the sliding window transform may be based on or include a Fourier transform or a fast Fourier transform (FFT). For example, each sliding window 810, ..., 890 may include or be associated with a portion of the time domain radar measurement data 800a (e.g., as described above) and may be provided as an input to a time domain FFT. In some examples, the time domain FFT may be a Doppler FFT (e.g., transforming the time domain input of the radar measurement data into a Doppler domain output of the frequency / spectral data).

[0116] Based on receiving separate sliding windows 810, 820, 830, ..., 890 as input, a time domain FFT (e.g., a Doppler FFT) can generate multiple instantaneous Doppler spectrum instances 812b, 822b, 832b, ..., 892b. The instantaneous Doppler spectrum instance may also be referred to as a Doppler spectrum instance or a Doppler instance in this article. The Doppler spectrum instance 812b, ..., 892b is a frequency domain representation (e.g., a transform) of the time domain radar measurement data included in the corresponding sliding window used to generate each Doppler spectrum instance. In some examples, the number of Doppler spectrum instances 812b, ..., 892b can be equal to the number of sliding windows 810, ..., 890 (e.g., one Doppler spectrum instance is generated for each sliding window by applying the Doppler FFT).

[0117] In one illustrative example, multiple Doppler spectrum instances 812b, ..., 892b may be combined or otherwise used to generate a Doppler spectrum frame (also referred to herein as a Doppler frame or micro-Doppler frame). Figure 8B The micro-Doppler spectrum frame 800b is depicted as including a plurality of individual Doppler spectrum instances 812b, ..., 892b, and Figure 8C A plurality of individual Doppler spectrum instances 812c, ..., 892c associated with the micro-Doppler spectrum frame 800c are depicted.

[0118] In some cases, the micro-Doppler frame 800c may be identical to the micro-Doppler frame 800b (eg, Figure 8B A simplified representation illustrating an example construction process of a micro-Doppler frame using a sliding window FFT, and Figure 8C Illustrate an example of a full micro-Doppler frame that can be generated as output).

[0119] The micro-Doppler frames 800b, 800c may be associated with a frame size N (e.g., the length of the generated micro-Doppler frame). In some examples, the frame size N may be given in the same units as one or both of the overlap size L and the window size M. For example, the sliding window FFT parameters M, L, N may be time domain parameters (e.g., in absolute time units or the number of radar Rx node reception time slots). In some aspects, the parameters M, L, N may be provided relative to a sensing RS (e.g., a radar reference signal associated with the radar measurement data 800a). For example, the parameters M, L, N may be based on the number of periodicities used for periodic sensing RSs, etc.

[0120] In some aspects, the number of discrete results (e.g., the number of discrete Doppler instances 812b, ..., 892b) included in the Doppler spectrum frame 800b, 800c or the Doppler spectrum used to generate the Doppler spectrum frame can be expressed in terms of Given, note that, as previously described, the denominator ML may be used to represent the step size between adjacent sliding windows of the time-domain radar measurement data 800a.

[0121] In one illustrative example, one or more of the parameters M, L, and / or N may be remotely determined and / or signaled from a radar Rx node that obtains radar measurement data (e.g., radar measurement data 800a) and performs a sliding window FFT to generate one or more micro-Doppler frames (e.g., micro-Doppler frames 800b, 800c). In some aspects, some or all of the sliding window FFT parameters may be determined by a radar Tx node associated with a given radar Rx node. For example, the sliding window FFT parameters may be determined by a radar Tx node of a dual-station or multi-station radar sensing system and signaled to one or more radar Rx nodes of the same dual-station or multi-station radar sensing system. Additionally or alternatively, in some examples, one or more sliding window FFT parameters may be determined and / or signaled to the radar Rx node by a remote server such as a sensing server or a data fusion server (e.g., as previously described above). In some examples, the radar Rx node may be used to implement dual-station or multi-station RF sensing, and the radar Rx node may include a UE, a gNB, etc. In such examples, one or more of the sliding window FFT parameters may be acquired using an RF sensing network (e.g., Figure 6 One or more communication signals (as illustrated) are used to signal or send to the radar Rx node.

[0122] In some aspects, the window size M can be based at least in part on a Doppler resolution associated with given radar measurement data (e.g., radar measurement data 800a) obtained at the radar Rx node. The Doppler resolution can also be referred to as the Doppler spectrum resolution, and in some cases can be a resolution associated with one or more of the Doppler domain power and / or the Doppler domain amplitude.

[0123] The overlap size L may be selected to determine the resolution of the micro-Doppler spectrum frame (e.g., micro-Doppler frames 800b, 800c). For example, the width of each micro-Doppler instance (e.g., instance 812b, 822b, ., 892b and / or instance 812c, 822c, ., 892c) may be given in ML, as previously described, such that the width of each instance determines the resolution of the micro-Doppler feature in each frame 800b, 800c. In an illustrative example, the window size M may be determined based on the Doppler resolution, and the overlap size L may be determined based on the temporal resolution of the micro-Doppler feature. For example, the radar Tx node and / or the remote server may determine one or more of the sliding window FFT parameters (e.g., as discussed above) such that the window size M is large enough to accommodate the Doppler resolution, and the overlap size L is small enough to capture the change in the Doppler profile over time.

[0124] As previously described, in some cases, the overhead (e.g., signaling or transmission overhead) associated with sending a full micro-Doppler frame from a radar Rx node may be significant. In some examples, the overhead associated with sending a full micro-Doppler frame may exceed the transmission capacity and / or capability of one or more communication links available to the radar Rx node (e.g., which obtains radar measurement data 800a based on radar signals reflected from target objects and generates corresponding micro-Doppler frames 800b, 800c).

[0125] In an illustrative example, the systems and techniques described herein may be used to compress micro-Doppler frames (e.g., micro-Doppler frames 800b, 800c) for transmission. For example, the systems and techniques may be used to compress micro-Doppler frames for transmission by a radar Rx node to a corresponding radar Tx node, a remote server, a sensing server, a data fusion server, etc. in a dual-station or multi-station sensing system. In some cases, some or all of the micro-Doppler frames (e.g., micro-Doppler frames 800b, 800c) may be included in a micro-Doppler report sent by the radar Rx node. In some examples, the micro-Doppler frame and the micro-Doppler report may be the same.

[0126] In some aspects, compression may be performed for each micro-Doppler instance associated with or used to generate a micro-Doppler frame. For example, compression may be performed for one or more of the micro-Doppler instances 812b, 822b, ..., 892b and / or instances 812c, 822c, ..., 892c, where the resulting compressed micro-Doppler instance is associated with a compressed micro-Doppler frame (e.g., corresponding to uncompressed micro-Doppler frames 800b, 800c). In some examples, compression may be performed for each of the micro-Doppler instances associated with a micro-Doppler frame.

[0127] For example, for each instance of a Doppler spectrum (e.g., a micro-Doppler spectrum) to be reported as part of a micro-Doppler frame, a compressed report of the micro-Doppler spectrum can be generated using a selected Doppler domain basis (e.g., a frequency domain basis). The compressed reports generated for each instance (e.g., micro-Doppler instance 812b, ..., 892b and / or instance 812c, ..., 892c) can be combined or otherwise used to generate a compressed micro-Doppler frame, as will be explained in more depth below.

[0128] In some examples, the compressed report generated for each individual instance of the micro-Doppler spectrum may include information such as the basis selection for each micro-Doppler instance. For example, a Doppler domain basis vector or a Doppler domain basis matrix may be reported for each instance of the compressed micro-Doppler instance 812b, ..., 892b and / or 812c, ..., 892c. In some cases, the Doppler domain basis information and the compressed micro-Doppler instance may be included in the same micro-Doppler measurement report sent by the radar Rx node. Based at least in part on the Doppler domain basis information, the radar Tx node, the sensing server, the data fusion server, etc. that receive the micro-Doppler measurement report from the radar Rx node can reconstruct multiple micro-Doppler instances and thus reconstruct the corresponding micro-Doppler frame. In some cases, the compressed report may additionally or alternatively include correlation coefficient quantization information (e.g., amplitude and / or phase information) for compressing each micro-Doppler instance. For example, in some aspects, the selected Doppler domain basis for compressing a given micro-Doppler instance may be determined based at least in part on the version 18 Doppler CSI. In some cases, the Doppler domain basis set may be based on or include a discrete Fourier transform (DFT) basis set (eg, using a variation of the DFT as a basis for each micro-Doppler instance to be compressed).

[0129] In one illustrative example, the systems and techniques described herein can use one or more differential reports to compress multiple micro-Doppler instances (e.g., 812b, ..., 892b / 812c, ..., 892c) included in a micro-Doppler frame (e.g., 800b / 800c). In some cases, compression based on differential reports can be performed in the time domain and can be performed alone or in combination with the above-described compression using a Doppler domain basis (e.g., which is performed in the Doppler domain / frequency domain).

[0130] For example, Fig. 9 An example micro-Doppler frame 900 is illustrated using one or more differential report compressions generated at micro-Doppler reference instances 912 and 922. Each reference instance can be associated with one or more neighboring instances. For example, as illustrated, the micro-Doppler frame 900 includes a first differential report set 910 and a second differential report set 920, wherein the first differential report set 910 includes a first reference instance 912 and four neighboring instances 913, 915, 917, and 919. The second differential report set 920 includes a second reference instance 922 and four neighboring instances 923, 925, 927, and 929. In some aspects, the differential report sets 910 and 920 may include the same number of reference instances and / or the same number of neighboring instances. In some examples, one or more of the differential report sets may differ in the number of reference instances and / or neighboring instances included. In some cases, the differential report sets may be compared to one another. Fig. 9 The 1:4 ratio depicted in , with higher or lower ratios between reference instances and neighboring instances, is used for the differential reporting based compression described herein.

[0131] In some aspects, a separate Doppler spectrum report may be generated for each of the reference instances 912 and 922. For example, the reference instances 912 and 922 may be the same as Figure 8B and Figure 8C , 892b and / or 812c, ..., 892c illustrated in FIG. 9A and 9B are the same or similar to one or more of the uncompressed micro-Doppler instances 812b, ..., 892b and / or 812c, ..., 892c illustrated in FIG. 9B . A differential report may be generated or otherwise determined for each neighboring instance included in a given differential report set based on a reference instance included in each differential report set of the micro-Doppler frame 900. For example, a first differential report set 910 may include an independent Doppler spectrum report for a reference instance 912 and four differential reports for neighboring instances 913-919 (e.g., associated with the reference instance 912).

[0132] In some aspects, a separate differential report may be generated for each neighboring instance included in the differential report set (e.g., a first differential report may be generated for reference instance 912 and neighboring instance 913, a second differential report may be generated for reference instance 912 and neighboring instance 915, and so on). In an illustrative example, the differential report may include a differential or other difference determined between a given reference instance and neighboring instance pair, wherein each reference instance-neighboring instance pair is included in the same differential report set. In some aspects, the differential reports may be used to compress the micro-Doppler frame over time based on one or more reference instances and corresponding one or more differential reports on the reference instances, the one or more differential reports being differentially quantized (e.g., by a certain differential amount) from the reference instances. In some examples, the micro-Doppler frame reporting overhead may be reduced by compressing individual micro-Doppler instances of a frame in the Doppler domain using a selected Doppler domain basis (e.g., compressing in frequency) and compressing the micro-Doppler frame over time using the reference instances and the differential reports.

[0133] In some aspects, further compression can be performed for one or more portions of a micro-Doppler frame (e.g., micro-Doppler frames 800b, 800c) and / or for one or more micro-Doppler instances (e.g., micro-Doppler instances 812b, ..., 892b / 812c, ..., 89c) included in a micro-Doppler frame. For example, the systems and techniques described herein can determine one or more Doppler shift correlations. By reporting only Doppler shift correlations, further micro-Doppler reporting overhead reduction can be achieved.

[0134] For example, the Doppler shift correlation may be determined or obtained by averaging multiple individual instances of the Doppler spectrum results (e.g., by averaging individual instances 812b, ..., 892b / 812c, ..., 892c associated with the Doppler frame 800b / 800c). In some aspects, one or more Doppler shift correlations may be determined by averaging multiple micro-Doppler instances within a given micro-Doppler frame to calculate an expectation. For example, a principal component analysis (PCA) may be performed based at least in part on a correlation matrix of the Doppler shifts. In an illustrative example, the Doppler shift correlation may be determined as:

[0135]

[0136] Here, C(f d1 ,f d2 ) is the Doppler frequency shift f d1 ,f d2 The correlation matrix of (e.g., where M is the window size parameter associated with the sliding window Doppler FFT used to generate the micro-Doppler instances, as described above). The Doppler shift correlation matrix C(f d1 ,f d2 ) may be given in Hertz (Hz), where FFTwindowLength is given as a time value associated with each FFT window (e.g., in seconds). S(f,t) is the micro-Doppler spectrum at instance t=1, ..., Q. Q is the number of discrete instances of the micro-Doppler spectrum result associated with the micro-Doppler frame (e.g., Q is the number of micro-Doppler instances included in the plurality of micro-Doppler instances 812b, ..., 892b / 812c, ..., 892c), where

[0137] Doppler frequency shift correlation matrix C(f d1 ,f d2 ) can be obtained by averaging the correlations over time for each instance (e.g., for a given Doppler frequency f d1 ,f d2 , the correlation matrix C(f d1 ,f d2 ) can be determined over all Q instances of a given micro-Doppler frame using the averaging operation of equation (1). In some examples, the micro-Doppler report generated for a given micro-Doppler frame may include one or more Doppler frequency shift correlation matrices, which can be used to reconstruct the given micro-Doppler frame and / or the corresponding micro-Doppler instance (e.g., at a radar Tx node, a sensing server, a data fusion server, etc. that receives the micro-Doppler frame sent by the radar Rx node).

[0138] In another illustrative example, the systems and techniques described herein can generate a micro-Doppler report (e.g., for a given micro-Doppler data frame) using parameter model reporting. For example, the systems and techniques can determine one or more parameters of a parameter model representation for a given micro-Doppler data frame. Reporting overhead can be reduced by reporting (e.g., in a micro-Doppler report) some or all of the determined parameters of the micro-Doppler frame without reporting the underlying data of the micro-Doppler frame itself. In some examples, parameter model reporting can be used instead of the above description of FIG. 8A to FIG. 8C The sliding window Doppler FFT data described above (eg, a micro-Doppler report may include one or more determined parameter model parameters but not the sliding window Doppler FFT data in compressed or uncompressed form).

[0139] Fig.10 Depicts an example Doppler spectrum 1000 (eg, a micro-Doppler spectrum) and parameter model reporting parameters. In some cases, the example micro-Doppler spectrum 1000 may be associated with Figure 8CFor example, the micro-Doppler spectrum described above may be the same as or similar to that described in FIG. 8A to FIG. 8C The same or similar sliding window Doppler FFT as described above is used to generate the micro-Doppler spectrum 1000. In one illustrative example, a parameter model reporting parameters determined for, for example, the micro-Doppler spectrum 1000 may include one or more of Doppler spread, average Doppler shift, and Doppler pattern periodicity.

[0140] In some aspects, Doppler spread may be determined as the width of a micro-Doppler signature as measured in the Doppler domain (eg, frequency domain). Fig.10 As illustrated, for example, the Doppler spread of the micro-Doppler spectrum 1000 may be determined to be between a first frequency 1010 and a second frequency 1020. In some examples, when the micro-Doppler spectrum 1000 is obtained based on a radar signal reflected by a target object (which is a drone or UAV), the Doppler spread may be proportional to the tip speed of the propeller of the drone / UAV (e.g., the Doppler spread may be proportional to the UAV rotor tip speed). In some cases, the Doppler spread may be proportional to the rotor tip speed, as determined by the propeller blade length / radius and the rotational angular velocity (e.g., reported in meters per second).

[0141] In some aspects, the average Doppler shift can be determined as the center frequency of the micro-Doppler spectrum 1000. Fig.10 As illustrated, the average Doppler shift of the micro-Doppler spectrum 1000 can be determined as the first frequency 1010. In some examples, the average Doppler shift and the Doppler spread can be measured or determined using the same center frequency 1010 of the micro-Doppler spectrum 1000 (e.g., as described above). The average Doppler shift can represent a displacement (e.g., in frequency or along a frequency axis) compared to a zero-value frequency baseline, and therefore, the average Doppler shift can be simply reported as the center frequency 1010 of the micro-Doppler spectrum 1000. In some aspects, when the micro-Doppler spectrum 1000 is obtained based on a radar signal reflected by a drone or UAV, the center frequency 1010 of the micro-Doppler spectrum 1000 (and therefore the average Doppler shift) is determined by the velocity of the body of the UAV. For example, the average Doppler shift can represent the detected linear velocity of the UAV, as measured along the axis of the UAV sensing antenna (e.g., the antenna of the radar Rx node).

[0142] In some aspects, the Doppler pattern periodicity may be determined as the periodicity included within the micro-Doppler spectrum 1000. For example, Fig.10As illustrated, adjacent peaks in micro-Doppler spectrum 1000 are associated with approximately constant period 1035. In some aspects, when micro-Doppler spectrum 1000 is obtained based on radar signals reflected by an unmanned aerial vehicle or UAV, the Doppler pattern periodicity (e.g., period 1035) included in the micro-Doppler spectrum can be determined by the rotational angular velocity of the propeller of the UAV (e.g., the Doppler pattern periodicity can be based at least in part on the rotational period of the propeller of the UAV).

[0143] In some aspects, the parameter model report may additionally or alternatively include one or more measurement uncertainty determinations of one or more of the Doppler spread, average Doppler shift, and Doppler pattern periodicity determinations described above. For example, the measurement uncertainty determination may include the estimated error of the Doppler parameter determination described above. In an illustrative example, the Doppler parameter measurement uncertainty may be determined based on the receiving side signal-to-noise ratio (SNR). For example, the Doppler parameter measurement uncertainty may be determined based on the SNR estimate at the radar Rx node used to obtain the micro-Doppler spectrum 1000. The Doppler parameter measurement uncertainty and / or the radar Rx SNR estimate may be included in the parameter model reported to the corresponding radar Tx node (e.g., in a dual-station or multi-station sensing system), a sensing server, a data fusion server, etc.

[0144] In some cases, the parameter model report may additionally or alternatively include or report the carrier frequency f c For example, the carrier frequency f c The carrier frequency f may be the carrier frequency of the radar signal transmitted by the radar Tx node and received by the radar Rx node as a reflection from the target object. c The width of the Doppler spectrum (e.g., Doppler spread) and the average Doppler shift can be affected because Doppler behavior is frequency and velocity based. In some cases, the carrier frequency f can be omitted from the parameter model report. c , because the carrier frequency f c It is already known on the transmitting side (eg, already known by a radar Tx node associated with a radar Rx node for obtaining the micro-Doppler spectrum 1000 , a sensing server, a data fusion server, etc.).

[0145] In some examples, the systems and techniques described herein can perform local classification of detected targets and report classification information to a radar Tx node, a sensing server, a data fusion server, or other remote server associated with a radar Rx node for obtaining radar measurement data and performing local classification of detected targets. In an illustrative example, the radar Rx node may include one or more classification algorithms and / or pre-trained machine learning (ML) classification models that can be used to classify detected targets. In some aspects, the reporting overhead from the radar Rx node can be reduced by using the local classification model of the radar Rx node to report one or more classifications determined for the detected target. For example, by reporting the determined classification information associated with the detected target, the radar Rx node can omit reporting one or more portions of the detailed Doppler spectrum information and / or reporting some or all of the micro-Doppler frames.

[0146] In some examples, systems and techniques may perform local classification to determine whether a detected target is a drone / UAV. In some aspects, the Radar Rx node may perform local classification based at least in part on configuring one or more predetermined classifications or categorizations of UAVs with different quantization value ranges. For example, for different UAV configurations such as the number of propellers, propeller blade lengths, propeller speeds, etc., the Radar Rx node may include or otherwise utilize predetermined classifications with different quantization value ranges, and in some examples, the predetermined UAV classifications may include different quantization value ranges for the parameters reported by the above-mentioned parameter model, wherein different quantization value ranges and / or different combinations of various quantization value ranges include predetermined labels for a certain type or category of UAV configuration.

[0147] For example, the Doppler spread of the micro-Doppler spectrum frame can be proportional to the tip speed of the UAV's propeller, as determined by the propeller blade length and the angular velocity of rotation; the average Doppler shift can be determined by the approximate linear velocity of the UAV along the sensing axis of the radar Rx node antenna; the Doppler pattern periodicity can be proportional to the angular velocity of rotation of the UAV's propeller. In one example, a first predetermined quantized value range of the Doppler spread can be associated with a relatively short UAV propeller blade length classification, and a second predetermined quantized value range of the Doppler spread can be associated with a relatively long UAV propeller blade length classification, and so on. In another example, a first quantized value range of the Doppler pattern periodicity can be associated with a relatively slow UAV propeller angular frequency, and a second quantized value range of the Doppler pattern periodicity can be associated with a relatively fast UAV propeller angular frequency, and so on.

[0148] Based at least in part on analyzing a given micro-Doppler spectrum frame and / or one or more Doppler parameter modeling parameters determined from the micro-Doppler frame against predetermined classification information at the radar Rx node, the radar Rx node may determine one or more classifications of the detected UAV target object and report only the classification or category information (e.g., based on a combination of sensing values ​​at the radar Rx node). In some examples, the target classification report information may include one or more confidence levels or confidence determinations associated with each of the one or more UAV target object classifications. In some aspects, the target classification report information may include multiple classifications of a given target object when the confidence level is low (e.g., when the confidence level of the classification drops below at least a first threshold, the radar Rx node may report the top two or more potential classifications of the target object). As previously described, in some examples, the radar Rx node may include one or more pre-trained ML models that may be used to perform classification of detected target objects locally at the radar Rx node. In some cases, the pre-trained ML model may include one or more neural networks or other classification models trained on training data pairs that each include one or more predetermined classifications and one or more parameters associated with or corresponding to the predetermined classifications for a given training data pair example, sensed values, Doppler parameter modeling parameters, micro-Doppler signature characteristics, etc. In some aspects, one or more confidence levels and / or confidence determinations may be additional output parameters of the trained ML model and / or neural network classifier pre-trained and provided at the radar Rx node.

[0149] Fig.11 1 is a diagram of an example non-continuous transmission (e.g., DTX-based) selective reporting process 1100. In some examples, when a target object (e.g., a drone or UAV) is detected in a given micro-Doppler spectrum frame, the systems and techniques described herein can use DTX-based selective reporting to report only relevant micro-Doppler spectrum results. For example, the reporting overhead from the radar Rx node to the radar Tx node (or other remote server in a dual-station or multi-station sensing system) can be reduced by omitting the transmission of any micro-Doppler information for micro-Doppler frames in which no target object is detected (e.g., the radar Rx node transmits micro-Doppler information when a UAV or other selected target object is identified or potentially identified in a micro-Doppler frame, but does not transmit micro-Doppler information when the target object cannot even be potentially identified in the micro-Doppler frame).

[0150] In some aspects, the Radar Rx node and the Radar Tx node (or other remote servers in a dual-station or multi-station sensing system) can implement the same or similar target object detection process for analyzing micro-Doppler information associated with the micro-Doppler frame. In some examples, the Radar Rx node can implement a lighter weight target object detection process in order to meet one or more latency targets or performance constraints associated with the Radar Rx node. For example, the Radar Rx node can be a UE and the Radar Tx node is a gNB - as a UE, the Radar Rx node can implement a target object detection process that is less powerful and less computationally intensive than the Radar Tx gNB.

[0151] In one illustrative example, the radar Rx node may utilize a lower target object detection threshold than the radar Tx node or remote server. This approach may allow for a higher false alarm rate (FAR) in the radar Rx node's decision to report micro-Doppler information to the radar Tx node for further analysis, where the final target object detection may be performed at a lower FAR (e.g., may be performed with higher accuracy).

[0152] In some aspects, the reporting overhead can be further reduced by sending only the areas in the micro-Doppler frame that the Radar Rx node identifies as potentially relevant for further analysis (e.g., by the Radar Tx node or other remote server). For example, as described above, when the Radar Rx node exceeds a predetermined target object (e.g., UAV) detection threshold, only the Radar Rx node implementing DTX-based selective reporting can be triggered to send a report with micro-Doppler spectrum information. Instead of sending a report with complete micro-Doppler spectrum information, the Radar Rx node can generate a report that includes only the areas of the micro-Doppler frame that are identified as relevant or potentially relevant to the target object detected by the Radar Rx node.

[0153] For example, the micro-Doppler report sent by the radar Rx node may include only areas of the micro-Doppler frame (e.g., in the Doppler domain) that are identified as at least potentially associated with the detected UAV. In some aspects, a micro-Doppler report that includes only the relevant areas of the micro-Doppler frame may also be referred to as a target-specific micro-Doppler report. In some examples, a target-specific micro-Doppler report may be generated to include the same area of ​​the micro-Doppler frame that is associated with or otherwise used by the radar Rx node when performing a locally implemented target object detection process. In some examples, a separate analysis may be used to generate a target-specific micro-Doppler report to determine the relevant portion of the micro-Doppler frame for further analysis at the radar Tx node or other remote server associated with the radar Rx node (e.g., a sensing server, a data fusion server, etc. included in the same dual-station or multi-station sensing system as the radar Rx node). In some aspects, a target-specific micro-Doppler frame may be automatically generated by cropping the full micro-Doppler frame to one or more predetermined areas. The predetermined area may be specified in absolute terms or relative terms. In some aspects, the target-specific micro-Doppler frame may be used in or combined with any other micro-Doppler reporting technique described herein.

[0154] In some examples, one or more of the micro-Doppler reporting and / or micro-Doppler reporting techniques described herein may include one or more of a detected angle measurement and a detected time measurement associated with a detected target object. For example, the detected angle measurement may include an angle of arrival (AoA) associated with a detected target object and / or may include an angle of departure (AoD) associated with a detected target object. In some examples, the detected time measurement may include a relative time difference on a first line of sight (LOS) path (e.g., such as Fig.12 (illustrated in diagram 1200).

[0155] In one illustrative example, detected time and / or angle measurement information associated with a detected target object (e.g., a UAV) may be included in a micro-Doppler report generated for the detected target object. In some examples, detected time and / or angle measurement information associated with a detected target object may be linked to a micro-Doppler report generated for or associated with the same detected target object.

[0156] In some cases, in response to a determination that there is a detected target object (e.g., as described above with respect to DTX-based selective reporting), additional time and / or angle measurement information may be included in or associated with the generated micro-Doppler report. For example, the additional time and / or angle measurement information may be used to locate or otherwise determine the position and / or position information of a detected target object (such as a UAV). If the UAV is not detected, in some cases, the micro-Doppler report (e.g., if generated or sent first) may omit the additional time and / or angle information. In some aspects, if the radar Rx node has a large antenna array, the AoA associated with the detected target may be included in the micro-Doppler report generated in association with the detected target. For example, when the radar Rx node is a gNB or a dedicated UE, etc., the AoA associated with the detected target may be included in the micro-Doppler report. In some cases, when the radar Rx node is a dedicated UE reference device as described in Release 17 positioning, the AoA associated with the detected target may be included in the micro-Doppler report. In some cases, when a wideband radar reference signal (RS) is used, the relative time difference on the first LOS path may be included in the micro-Doppler report.

[0157] In some examples, when the associated radar Tx node transmits a multi-port sensing RS (e.g., when the associated radar Tx node is a multiple-input multiple-output (MIMO) radar), AoD information associated with the detected target may be included in a micro-Doppler report generated for the detected target. In some cases, some or all of the AoD information and / or AoA information may be included in the micro-Doppler report, and the AoD may be jointly estimated with the AoA at the radar Rx side (e.g., jointly estimated between the radar Rx side and the radar Tx side).

[0158] Fig.13 is a flow chart illustrating an example of a process 1300 for communication and sensing. At block 1302, the process 1300 includes receiving a first signal based on a reflection from a target. For example, the first signal may be a time domain signal received by a radar receiving node included in a multi-station or bi-station sensing system. In some examples, the first signal may be received by a receiver node associated with a target. Figure 2 The radar receiving node 210 illustrated in Figure 4 The radar receiving node 404 and / or Figure 5 One or more of the radar receiving nodes 504 illustrated in FIG. 5 are the same or similar radar receiving nodes.

[0159] At block 1304, process 1300 includes generating a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target. For example, the Doppler spectrum frame may include Figure 8B The micro-Doppler spectrum frame 800b illustrated in Figure 8C The micro-Doppler spectrum frame 800c illustrated in Fig. 9 The micro-Doppler spectrum frame 900 and / or Fig.10 In some examples, the micro-Doppler spectrum frame may include one or more Doppler domain characteristics, such as a micro-Doppler signature of a target. For example, the micro-Doppler signature of a target may be associated with Figure 7 One or more of the example micro-Doppler signatures 700a, 700b, 700c illustrated in FIG. 7 are the same or similar.

[0160] In some cases, generating the Doppler spectrum frame may include determining one or more sliding window parameters of the Doppler spectrum frame and generating a plurality of sliding windows using the first signal. In some examples, each sliding window in the plurality of sliding windows includes a portion of the first signal based on the one or more sliding window parameters. For example, the first signal may be generated based on a plurality of sliding windows such as Figure 8B The sliding windows 812b, 822b, 832b, ..., 892b and / or Figure 8C The Doppler spectrum frame is generated by using multiple sliding windows including the sliding windows 812c, 922c, 832c, ..., 892c illustrated in FIG.

[0161] Generating a Doppler spectrum frame may also include generating a plurality of Doppler spectrum instances based on a plurality of sliding windows. For example, a Doppler spectrum frame may be generated based on a plurality of Doppler spectrum instances. In some examples, each Doppler spectrum instance in the plurality of Doppler spectrum instances may be a frequency domain signal. Generating a plurality of Doppler spectrum instances may include determining a fast Fourier transform (FFT) of each sliding window.

[0162] In some examples, one or more sliding window parameters may include one or more of the sliding window sizes for generating each sliding window in a plurality of sliding windows and / or may include an overlap size between adjacent sliding windows (e.g., in a plurality of sliding windows). For example, the sliding window size may be the width of each sliding window and may be given in units such as time (e.g., ms). In some examples, adjacent sliding window pairs may include a shared portion of the first signal determined based on the sliding window size and the overlap size. In some cases, one or more sliding window parameters are time domain parameters. In some examples, each of the one or more sliding window parameters may include an absolute time value or a radar sensing reference signal (RS) periodicity value.

[0163] At block 1306, process 1300 includes generating a micro-Doppler measurement report based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame. For example, generating the micro-Doppler measurement report may include generating one or more compressed portions of the Doppler spectrum frame by compressing each of a plurality of Doppler spectrum instances. In some examples, the plurality of Doppler spectrum instances may be compressed based on determining a Doppler domain basis selection for each Doppler spectrum instance and determining one or more coefficient quantizations for compressing each Doppler spectrum instance. Each Doppler spectrum instance may then be compressed using the Doppler domain basis selection and the one or more coefficient quantizations (e.g., to generate one or more compressed portions of the Doppler spectrum frame). In some examples, the micro-Doppler measurement report may include one or more of the Doppler domain basis selection and the one or more coefficient quantizations determined for each Doppler spectrum instance.

[0164] In some examples, compressing multiple Doppler spectrum instances includes generating one or more differential reports based on multiple Doppler spectrum instances. For example, a Doppler spectrum report can be obtained for one or more reference instances selected from multiple Doppler spectrum instances. In some cases, the neighboring instances associated with each reference instance can be determined so that the neighboring instances do not include the one or more reference instances. For each reference instance (e.g., one or more reference instances), a differential report can be generated between the corresponding reference instance and the corresponding neighboring instance associated with the corresponding reference instance. In some examples, the differential report may include differential quantization. In some cases, each reference instance is associated with the corresponding one or more neighboring instances. Each reference instance and each corresponding one or more neighboring instances may be a continuous Doppler spectrum instance included in the multiple Doppler spectrum instances.

[0165] In some examples, process 1300 may also include determining, based on the micro-Doppler measurement report, at least one characteristic of the target based on information in the micro-Doppler measurement report. For example, the at least one characteristic may be identifying the target as a drone or unmanned aerial vehicle (UAV).

[0166] In some cases, the micro-Doppler measurement report may be generated using a radar receiving node (e.g., the same or similar to a radar receiving node that may be associated with receiving a first signal based on a reflection from a target, as described above with respect to block 1302). In some examples, process 1300 may also include sending the micro-Doppler measurement report to a remote processing node using the radar receiving node, wherein the remote processing node is included in the same multi-station sensing system as the radar receiving node.

[0167] In some examples, the processes described herein (e.g., process 1300 and / or any other process described herein) can be performed by a computing device, apparatus, or system. In one example, process 1300 can be performed by a computer having Fig.14 The computing device or system of the computing device architecture 1400 may be executed. The computing device, apparatus or system may include any suitable device, such as a mobile device (e.g., a mobile phone), a desktop computing device, a tablet computing device, a wearable device (e.g., a VR headset, an AR headset, an AR glasses, a networked watch or smart watch, or other wearable device), a server computer, an autonomous vehicle or a computing device of an autonomous vehicle, a robotic device, a laptop computer, a smart TV, a camera and / or any other computing device with resource capabilities to perform the processes described herein (including process 700 and / or other processes described herein). In some cases, the computing device or apparatus may include various components, such as one or more input devices, one or more output devices, one or more processors, one or more microprocessors, one or more microcomputers, one or more cameras, one or more sensors, and / or other components configured to perform the steps of the processes described herein. In some examples, the computing device may include a display, a network interface configured to communicate and / or receive data, any combination thereof and / or other components. The network interface may be configured to communicate and / or receive data based on an Internet Protocol (IP) or other types of data.

[0168] The components of the computing device may be implemented in circuits. For example, the components may include and / or may be implemented using electronic circuits or other electronic hardware, which may include one or more programmable electronic circuits (e.g., microprocessors, graphics processing units (GPUs), digital signal processors (DSPs), central processing units (CPUs), and / or other suitable electronic circuits), and / or may include and / or may be implemented using computer software, firmware, or any combination thereof for performing the various operations described herein.

[0169] The process 1300 is illustrated as a logic flow diagram, the operations of which represent a sequence of operations that can be implemented in hardware, computer instructions, or a combination thereof. In the context of computer instructions, each operation represents a computer-executable instruction stored on one or more computer-readable storage media that, when executed by one or more processors, performs the described operation. Generally speaking, computer-executable instructions include routines, programs, objects, components, data structures, etc. that perform specific functions or implement specific data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations may be combined in any order and / or in parallel to implement the process.

[0170] Additionally, process 1300 and / or any other process described herein may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed together on one or more processors, through hardware, or a combination thereof. As noted above, the code may be stored on a computer-readable or machine-readable storage medium, for example, in the form of a computer program that includes a plurality of instructions that can be executed by one or more processors. The computer-readable or machine-readable storage medium may be non-transitory.

[0171] Diagram 1400 illustrates an example computing device architecture 1400 of an example computing device that may implement the various techniques described herein. In some examples, the computing device may include a mobile device, a wearable device, an extended reality device (e.g., a virtual reality (VR) device, an augmented reality (AR) device, or a mixed reality (MR) device), a personal computer, a laptop computer, a video server, a vehicle (or a computing device of a vehicle), or other device. The components of the computing device architecture 1400 are shown as being in electrical communication with each other using a connection 1405 such as a bus. The example computing device architecture 1400 includes a processing unit (CPU or processor) 1410 and a computing device connection 1405 that couples various computing device components including a computing device memory 1415 (such as a read-only memory (ROM) 1420 and a random access memory (RAM) 1425) to the processor 1410.

[0172] The computing device architecture 1400 may include a cache of high-speed memory directly connected to the processor 1410, very close to the processor, or integrated as part of the processor. The computing device architecture 1400 may copy data from the memory 1415 and / or the storage device 1430 to the cache 1412 for fast access by the processor 1410. In this way, the cache can provide a performance improvement that avoids the processor 1410 from delaying when waiting for data. These engines and other engines can control or be configured to control the processor 1410 to perform various actions. Other computing device memories 1415 are also available for use. The memory 1415 may include a variety of different types of memory with different performance characteristics. The processor 1410 may include any general-purpose processor and hardware or software services (such as service 1 1432, service 2 1434, and service 3 1436 stored in the storage device 1430) configured to control the processor 1410, as well as a special-purpose processor in which software instructions are incorporated into the processor design. The processor 1410 may be a self-contained system, including multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors can be symmetric or asymmetric.

[0173] To enable user interaction with the computing device architecture 1400, the input device 1445 may represent any number of input mechanisms, such as a microphone for voice, a touch-sensitive screen for gesture or graphic input, a keyboard, a mouse, motion input, voice, etc. The output device 1435 may also be one or more of a plurality of output mechanisms known to those skilled in the art, such as a display, a projector, a television, a speaker device, etc. In some instances, a multi-modal computing device may enable a user to provide multiple types of input to communicate with the computing device architecture 1400. The communication interface 1440 may generally control and manage user input and computing device output. There is no limitation to operating on any particular hardware arrangement, and thus the underlying features herein may be easily replaced to obtain improved hardware or firmware arrangements as they are developed.

[0174] The storage device 1430 is a non-volatile memory and may be a hard disk or other type of computer readable medium capable of storing computer accessible data, such as a magnetic tape cartridge, a flash memory card, a solid-state memory device, a digital versatile disk, a cassette tape, a random access memory (RAM) 1425, a read-only memory (ROM) 1420, and hybrids thereof. The storage device 1430 may include services 1432, 1434, 1436 for controlling the processor 1410. Other hardware or software modules or engines are contemplated. The storage device 1430 may be connected to the computing device connector 1405. In one aspect, a hardware module that performs a particular function may include a software component stored in a computer readable medium connected to the necessary hardware components (such as the processor 1410, connector 1405, output device 1435, etc.) to perform the function.

[0175] Aspects of the present disclosure are applicable to any suitable electronic device (such as a security system, smartphone, tablet, laptop, vehicle, drone, or other device) that includes or is coupled to one or more active depth sensing systems. Although described below with respect to a device having or coupled to one light projector, aspects of the present disclosure are applicable to devices having any number of light projectors and are therefore not limited to a particular device.

[0176] The term "device" is not limited to one or a specific number of physical objects (such as a smart phone, a controller, a processing system, etc.). As used herein, a device can be any electronic device having one or more parts that can implement at least some parts of the present disclosure. Although the following description and examples use the term "device" to describe various aspects of the present disclosure, the term "device" is not limited to a specific configuration, type, or number of objects. In addition, the term "system" is not limited to multiple components or specific aspects. For example, a system can be implemented on one or more printed circuit boards or other substrates and can have movable or static components. Although the following description and examples use the term "system" to describe various aspects of the present disclosure, the term "system" is not limited to a specific configuration, type, or number of objects.

[0177] Specific details are provided in the above description to provide a detailed understanding of the aspects and examples provided herein. However, it will be understood by those skilled in the art that these aspects can also be practiced without these specific details. For clarity, in some cases, the present technology may be presented as including separate functional blocks, including functional blocks comprising devices, device components, steps or routines in the method embodied in software or a combination of hardware and software. Additional components other than those components shown in the accompanying drawings and / or described herein may be used. For example, circuits, systems, networks, processes and other components may be shown as components in block diagram form to avoid confusing these aspects in unnecessary details. In other instances, known circuits, processes, algorithms, structures and techniques may be shown without unnecessary details to avoid confusing various aspects.

[0178] Various aspects may be described above as a process or method, which is depicted as a flow chart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flow chart may describe an operation as a sequential process, many operations in the operation may be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. The process is terminated when the operation of the process is completed, but the process may have additional steps not included in the accompanying drawings. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, the termination of the process may correspond to the function returning to a calling function or a main function.

[0179] The processes and methods according to the above examples can be implemented using computer executable instructions stored or otherwise obtained from a computer readable medium. Such instructions may include, for example, instructions and data that cause or otherwise configure a general-purpose computer, a special-purpose computer, or a processing device to perform a certain function or group of functions. Portions of the computer resources used may be accessed over a network. Computer executable instructions may be, for example, binary files, intermediate format instructions (such as assembly language), firmware, source code, etc.

[0180] The term "computer-readable medium" includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other media capable of storing, containing, or carrying instructions and / or data. Computer-readable media may include non-transient media in which data may be stored and which do not include carrier waves and / or transient electronic signals that are propagated wirelessly or over a wired connection. Examples of non-transient media may include, but are not limited to, disks or tapes, optical storage media (such as flash memory), memory or memory devices, disks or optical disks, flash memory, USB devices provided with non-volatile memory, network storage devices, compact discs (CDs) or digital versatile discs (DVDs), any suitable combination thereof, and the like. Computer-readable media may store thereon code and / or machine-executable instructions that may represent procedures, functions, subroutines, programs, routines, subroutines, modules, engines, software packages, classes, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, independent variables, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or sent via any suitable means, including memory sharing, message passing, token passing, network sending, etc.

[0181] In some aspects, computer readable storage devices, media, and memories may include cables or wireless signals containing bit streams, etc. However, when referred to, non-transitory computer readable storage media specifically excludes media such as energy, carrier signals, electromagnetic waves, and signals themselves.

[0182] The equipment implementing the processes and methods according to these disclosures may include hardware, software, firmware, middleware, microcode, hardware description language, or any combination thereof, and may take any of a variety of form factors. When implemented in software, firmware, middleware, or microcode, program code or code segments (e.g., computer program products) for performing necessary tasks may be stored in a computer-readable or machine-readable medium. The processor may perform the necessary tasks. Typical examples of form factors include laptop computers, smart phones, mobile phones, tablet devices, or other small form factor personal computers, personal digital assistants, rack-mounted devices, stand-alone devices, etc. The functionality described herein may also be embodied in peripheral devices or plug-in cards. By way of additional examples, such functionality may also be implemented on circuit boards among different chips or different processes executed in a single device.

[0183] Instructions, media for delivering such instructions, computing resources for executing the instructions, and other structures for supporting such computing resources are example means for providing the functionality described in this disclosure.

[0184] In the above description, various aspects of the present application are described with reference to their specific aspects, but those skilled in the art will recognize that the present application is not limited thereto. Thus, although the exemplary aspects of the present application have been described in detail herein, it is to be understood that each inventive concept can be implemented and adopted in various other ways, and the appended claims are not intended to be interpreted as including these variations, unless limited by the prior art. Various features and aspects of the above-mentioned applications can be used individually or in combination. In addition, various aspects can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of this specification. Therefore, the description and the accompanying drawings should be considered as illustrative rather than restrictive. For the purpose of illustration, each method is described in a specific order. It should be understood that, in alternative aspects, each method can be performed in a different order than described.

[0185] One of ordinary skill in the art will appreciate that the less than ("<") and greater than (">") symbols or terms used herein may be replaced by less than or equal to ("≤") and greater than or equal to ("≥") symbols, respectively, without departing from the scope of this specification.

[0186] Where a component is described as being “configured to” perform certain operations, such configuration may be achieved, for example, by designing electronic circuits or other hardware to perform the operations, by programming programmable electronic circuits (e.g., a microprocessor or other suitable electronic circuits) to perform the operations, or any combination thereof.

[0187] The phrase "coupled to" means that any component is directly or indirectly physically connected to another component and / or any component is in direct or indirect communication with another component (e.g., connected to another component via a wired or wireless connection and / or other suitable communication interface).

[0188] Claim language or other language stating "at least one of" a set and / or "one or more of" a set indicates that one member of the set or multiple members of the set (in any combination) satisfies the claim. For example, claim language stating "at least one of A and B" or "at least one of A or B" means A, B, or A and B. In another example, claim language stating "at least one of A, B, and C" or "at least one of A, B, or C" means A, B, C, or A and B, or A and C, or B and C, or A and B and C. The language "at least one of" a set and / or "one or more of" a set does not limit the set to the items listed in the set. For example, claim language stating "at least one of A and B" or "at least one of A or B" may mean A, B, or A and B, and may additionally include items not listed in the set of A and B.

[0189] Various exemplary logic blocks, modules, engines, circuits and algorithmic steps described in conjunction with aspects disclosed herein can be implemented as electronic hardware, computer software, firmware or a combination thereof. In order to clearly illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, circuits and steps have been generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints proposed to the entire system. Technicians can implement the described functionality in different ways for each specific application, but such specific implementation decisions should not be interpreted as making it depart from the scope of the present application.

[0190] The technology described herein can also be implemented in electronic hardware, computer software, firmware, or any combination thereof. Such technology can be implemented in any of a variety of devices, such as general-purpose computers, wireless communication device mobile phones, or integrated circuit devices with multiple uses, including applications in wireless communication device mobile phones and other devices. Any features described as modules or components can be implemented together in an integrated logic device or separately as discrete but interoperable logic devices. If implemented in software, these technologies can be implemented at least in part by a computer-readable data storage medium including a program code, the program code including instructions, which execute one or more of the above methods when executed. Computer-readable data storage media can form a part of a computer program product, which can include packaging materials. Computer-readable media may include memory or data storage media, such as random access memory (RAM) (such as synchronous dynamic random access memory (SDRAM)), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), flash memory, magnetic or optical data storage media, etc. Additionally or alternatively, the technology may be implemented at least in part by a computer-readable communication medium that carries or communicates program code in the form of instructions or data structures and that can be accessed, read, and / or executed by a computer, such as a propagated signal or wave.

[0191] The program code may be executed by a processor, which may include one or more processors, such as one or more digital signal processors (DSPs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Such processors may be configured to perform any of the techniques described in this disclosure. A general-purpose processor may be a microprocessor; however, in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. Therefore, the term "processor" as used herein may refer to any of the foregoing structures, any combination of the foregoing structures, or any other structure or device suitable for implementing the techniques described herein.

[0192] Illustrative aspects of the present disclosure include:

[0193] Aspect 1: A method for communication and sensing, the method comprising: receiving a first signal based on reflection from a target; generating a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target; and generating a micro-Doppler measurement report based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed parts of the Doppler spectrum frame.

[0194] Aspect 2: A method according to Aspect 1, wherein generating the Doppler spectrum frame comprises: determining one or more sliding window parameters of the Doppler spectrum frame; generating a plurality of sliding windows using the first signal, wherein each sliding window of the plurality of sliding windows comprises a portion of the first signal based on the one or more sliding window parameters; and generating a plurality of Doppler spectrum instances based on the plurality of sliding windows.

[0195] Aspect 3: A method according to Aspect 2, wherein: the first signal is a time domain signal; each of the multiple Doppler spectrum instances is a frequency domain signal; and generating the multiple Doppler spectrum instances includes determining a fast Fourier transform (FFT) of each sliding window in the multiple sliding windows.

[0196] Aspect 4: A method according to any one of Aspects 2 to 3, wherein the one or more sliding window parameters include: a sliding window size, the sliding window size being used to generate each sliding window among the multiple sliding windows; and an overlap size between adjacent sliding windows among the multiple sliding windows, wherein the adjacent sliding windows include a shared portion of the first signal determined based on the sliding window size and the overlap size.

[0197] Aspect 5: A method according to any one of Aspects 2 to 4, wherein: the one or more sliding window parameters are time domain parameters; and each of the one or more sliding window parameters includes an absolute time value or a radar sensing reference signal (RS) periodicity value.

[0198] Aspect 6: The method according to any one of aspects 2 to 5, wherein generating the micro-Doppler measurement report comprises: generating the one or more compressed parts of the Doppler spectrum frame by compressing the plurality of Doppler spectrum instances.

[0199] Aspect 7: A method according to Aspect 6, wherein compressing the multiple Doppler spectrum instances includes: determining a Doppler domain basis selection for each Doppler spectrum instance in the multiple Doppler spectrum instances; determining one or more coefficient quantizations for compressing each Doppler spectrum instance in the multiple Doppler spectrum instances; and compressing each Doppler spectrum instance using the Doppler domain basis selection and the one or more coefficient quantizations.

[0200] Aspect 8: The method according to aspect 7, wherein the micro-Doppler measurement report includes one or more of the Doppler domain basis selection and the one or more coefficient quantization determined for each Doppler spectrum instance in the plurality of Doppler spectrum instances.

[0201] Aspect 9: A method according to any one of Aspects 6 to 8, wherein compressing the multiple Doppler spectrum instances includes generating one or more differential reports based on the multiple Doppler spectrum instances by: obtaining Doppler spectrum reports of one or more reference instances selected from the multiple Doppler spectrum instances; determining neighboring instances associated with each of the one or more reference instances, wherein the neighboring instances do not include the one or more reference instances; and for each of the one or more reference instances, generating a differential report between the corresponding reference instance and the corresponding neighboring instance associated with the corresponding reference instance, wherein the differential report includes differential quantization.

[0202] Aspect 10: The method according to Aspect 9, wherein: each reference instance is associated with corresponding one or more neighboring instances; and each reference instance and each corresponding one or more neighboring instances are consecutive Doppler spectrum instances included in the multiple Doppler spectrum instances.

[0203] Aspect 11: The method according to any one of aspects 1 to 10, the method further comprising: determining, based on the micro-Doppler measurement report, at least one characteristic of the target based on information in the micro-Doppler measurement report.

[0204] Aspect 12: The method according to Aspect 11, wherein: the at least one characteristic is to identify the target as a drone or an unmanned aerial vehicle (UAV).

[0205] Aspect 13: A method according to any one of Aspects 1 to 12, wherein: the first signal is received by a radar receiving node included in a multi-station or dual-station sensing system; and the micro-Doppler measurement report is generated using the radar receiving node.

[0206] Aspect 14: The method according to aspect 13, the method further comprising: using the radar receiving node to send the micro-Doppler measurement report to a remote processing node, wherein the remote processing node and the radar receiving node are included in the same multi-station sensing system.

[0207] Aspect 15: The method according to any one of aspects 1 to 14, wherein the one or more Doppler domain characteristics include a micro-Doppler signature of the target.

[0208] Aspect 16: A device for communication and sensing, the device comprising: a memory; and one or more processors coupled to the memory, the one or more processors being configured to: receive a first signal based on a reflection from a target; generate a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame comprises one or more Doppler domain characteristics for identifying the target; and generate a micro-Doppler measurement report based on the Doppler spectrum frame, wherein the micro-Doppler measurement report comprises one or more compressed portions of the Doppler spectrum frame.

[0209] Aspect 17: An apparatus according to Aspect 16, wherein in order to generate the Doppler spectrum frame, the one or more processors are configured to: determine one or more sliding window parameters of the Doppler spectrum frame; generate a plurality of sliding windows using the first signal, wherein each sliding window of the plurality of sliding windows includes a portion of the first signal based on the one or more sliding window parameters; and generate a plurality of Doppler spectrum instances based on the plurality of sliding windows.

[0210] Aspect 18: An apparatus according to Aspect 17, wherein: the first signal is a time domain signal; each of the multiple Doppler spectrum instances is a frequency domain signal; and generating the multiple Doppler spectrum instances includes determining a fast Fourier transform (FFT) of each sliding window in the multiple sliding windows.

[0211] Aspect 19: An apparatus according to any one of Aspects 17 to 18, wherein the one or more sliding window parameters include: a sliding window size, the sliding window size being used to generate each sliding window among the multiple sliding windows; and an overlap size between adjacent sliding windows among the multiple sliding windows, wherein the adjacent sliding windows include a shared portion of the first signal determined based on the sliding window size and the overlap size.

[0212] Aspect 20: An apparatus according to any one of Aspects 17 to 19, wherein: the one or more sliding window parameters are time domain parameters; and each of the one or more sliding window parameters comprises an absolute time value or a radar sensing reference signal (RS) periodicity value.

[0213] Aspect 21: An apparatus according to any one of Aspects 17 to 20, wherein, in order to generate the micro-Doppler measurement report, the one or more processors are configured to: generate the one or more compressed parts of the Doppler spectrum frame by compressing the multiple Doppler spectrum instances.

[0214] Aspect 22: An apparatus according to Aspect 21, wherein in order to compress the multiple Doppler spectrum instances, the one or more processors are configured to: determine a Doppler domain basis selection for each Doppler spectrum instance in the multiple Doppler spectrum instances; determine one or more coefficient quantizations for compressing each Doppler spectrum instance in the multiple Doppler spectrum instances; and compress each Doppler spectrum instance using the Doppler domain basis selection and the one or more coefficient quantizations.

[0215] Aspect 23: The apparatus according to Aspect 22, wherein the micro-Doppler measurement report includes one or more of the Doppler domain basis selection and the one or more coefficient quantization determined for each Doppler spectrum instance of the plurality of Doppler spectrum instances.

[0216] Aspect 24: An apparatus according to any one of Aspects 21 to 23, wherein in order to compress the multiple Doppler spectrum instances, the one or more processors are configured to generate one or more differential reports based on the multiple Doppler spectrum instances by: obtaining Doppler spectrum reports of one or more reference instances selected from the multiple Doppler spectrum instances; determining neighboring instances associated with each of the one or more reference instances, wherein the neighboring instances do not include the one or more reference instances; and for each of the one or more reference instances, generating a differential report between the corresponding reference instance and the corresponding neighboring instance associated with the corresponding reference instance, wherein the differential report includes differential quantization.

[0217] Aspect 25: An apparatus according to Aspect 24, wherein: each reference instance is associated with corresponding one or more neighboring instances; and each reference instance and each corresponding one or more neighboring instances are consecutive Doppler spectrum instances included in the multiple Doppler spectrum instances.

[0218] Aspect 26: An apparatus according to any one of Aspects 16 to 25, wherein the one or more processors are further configured to: determine, based on the micro-Doppler measurement report, at least one characteristic of the target based on information in the micro-Doppler measurement report.

[0219] Aspect 27: An apparatus according to Aspect 26, wherein: the at least one characteristic is to identify the target as a drone or unmanned aerial vehicle (UAV).

[0220] Aspect 28: An apparatus according to any one of Aspects 16 to 27, wherein: the first signal is received by a radar receiving node included in a multi-station or bi-station sensing system; and the micro-Doppler measurement report is generated using the radar receiving node.

[0221] Aspect 29: An apparatus according to Aspect 28, wherein the one or more processors are further configured to: use the radar receiving node to send the micro-Doppler measurement report to a remote processing node, wherein the remote processing node and the radar receiving node are included in the same multi-station sensing system.

[0222] Aspect 30: The apparatus according to any one of aspects 16 to 29, wherein the one or more Doppler domain characteristics include a micro-Doppler signature of the target.

[0223] Aspect 31: A non-transitory computer-readable medium having instructions stored thereon, the instructions, when executed by one or more processors, causing the one or more processors to perform the operations according to any one of aspects 1 to 30.

[0224] Aspect 32: An apparatus comprising means for performing any of the operations according to aspects 1 to 30.

Claims

1. A method for communication and sensing, the method comprising: receiving a first signal based on a reflection from a target; generating a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target; as well as A micro-Doppler measurement report is generated based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame.

2. The method of claim 1 , wherein generating the Doppler spectrum frame comprises: determining one or more sliding window parameters of the Doppler spectrum frame; generating a plurality of sliding windows using the first signal, wherein each sliding window of the plurality of sliding windows comprises a portion of the first signal based on the one or more sliding window parameters; as well as A plurality of Doppler spectrum instances are generated based on the plurality of sliding windows.

3. The method according to claim 2, wherein: The first signal is a time domain signal; Each Doppler spectrum instance of the plurality of Doppler spectrum instances is a frequency domain signal; and Generating the plurality of Doppler spectrum instances includes determining a Fast Fourier Transform (FFT) of each sliding window of the plurality of sliding windows.

4. The method of claim 2, wherein the one or more sliding window parameters include: a sliding window size, the sliding window size being used to generate each sliding window among the plurality of sliding windows; and An overlap size between adjacent sliding windows of the plurality of sliding windows, wherein the adjacent sliding windows include a shared portion of the first signal determined based on the sliding window size and the overlap size.

5. The method according to claim 2, wherein: The one or more sliding window parameters are time domain parameters; and Each of the one or more sliding window parameters comprises an absolute time value or a radar sensing reference signal (RS) periodicity value.

6. The method of claim 2, wherein generating the micro-Doppler measurement report comprises: The one or more compressed portions of the Doppler spectrum frame are generated by compressing the plurality of Doppler spectrum instances.

7. The method of claim 6, wherein compressing the plurality of Doppler spectrum instances comprises: determining a Doppler domain basis selection for each Doppler spectrum instance in the plurality of Doppler spectrum instances; determining one or more coefficient quantizations for compressing each Doppler spectrum instance of the plurality of Doppler spectrum instances; as well as Each Doppler spectrum instance is compressed using the Doppler domain basis selection and the one or more coefficient quantization.

8. The method of claim 7, wherein the micro-Doppler measurement report comprises one or more of the Doppler domain basis selection and the one or more coefficient quantization determined for each of the plurality of Doppler spectrum instances.

9. The method of claim 6, wherein compressing the plurality of Doppler spectrum instances comprises generating one or more difference reports based on the plurality of Doppler spectrum instances by: obtaining Doppler spectrum reports for one or more reference instances selected from the plurality of Doppler spectrum instances; determining a neighboring instance associated with each of the one or more reference instances, wherein the neighboring instance does not include the one or more reference instances; as well as For each reference instance of the one or more reference instances, a difference report is generated between the corresponding reference instance and a corresponding neighboring instance associated with the corresponding reference instance, wherein the difference report includes a difference quantization.

10. The method according to claim 9, wherein: Each reference instance is associated with a corresponding one or more neighboring instances; and Each reference instance and each corresponding one or more neighboring instances are consecutive Doppler spectrum instances included in the plurality of Doppler spectrum instances.

11. The method according to claim 1, further comprising: Based on the micro-Doppler measurement report, at least one characteristic of the target based on information in the micro-Doppler measurement report is determined.

12. The method according to claim 11, wherein: The at least one characteristic identifies the target as a drone or unmanned aerial vehicle (UAV).

13. The method of claim 1, wherein: The first signal is received by a radar receiving node included in a multistatic or bistatic sensing system; and The micro-Doppler measurement report is generated using the radar receiving node.

14. The method according to claim 13, further comprising: The micro-Doppler measurement report is sent using the radar receiving node to a remote processing node, wherein the remote processing node is included in the same multi-station sensing system as the radar receiving node.

15. The method of claim 1, wherein the one or more Doppler domain characteristics include a micro-Doppler signature of the target.

16. An apparatus for communication and sensing, the apparatus comprising: Memory; and one or more processors coupled to the memory, the one or more processors configured to: receiving a first signal based on a reflection from a target; generating a Doppler spectrum frame based on the first signal, wherein the Doppler spectrum frame includes one or more Doppler domain characteristics for identifying the target; as well as A micro-Doppler measurement report is generated based on the Doppler spectrum frame, wherein the micro-Doppler measurement report includes one or more compressed portions of the Doppler spectrum frame.

17. The apparatus of claim 16, wherein to generate the Doppler spectrum frame, the one or more processors are configured to: determining one or more sliding window parameters of the Doppler spectrum frame; generating a plurality of sliding windows using the first signal, wherein each sliding window of the plurality of sliding windows comprises a portion of the first signal based on the one or more sliding window parameters; and A plurality of Doppler spectrum instances are generated based on the plurality of sliding windows.

18. The device according to claim 17, wherein: The first signal is a time domain signal; Each Doppler spectrum instance of the plurality of Doppler spectrum instances is a frequency domain signal; and Generating the plurality of Doppler spectrum instances includes determining a Fast Fourier Transform (FFT) of each sliding window of the plurality of sliding windows.

19. The apparatus of claim 17, wherein the one or more sliding window parameters include: a sliding window size, the sliding window size being used to generate each sliding window among the plurality of sliding windows; and An overlap size between adjacent sliding windows of the plurality of sliding windows, wherein the adjacent sliding windows include a shared portion of the first signal determined based on the sliding window size and the overlap size.

20. The apparatus of claim 17, wherein: The one or more sliding window parameters are time domain parameters; and Each of the one or more sliding window parameters comprises an absolute time value or a radar sensing reference signal (RS) periodicity value.

21. The apparatus of claim 17, wherein to generate the micro-Doppler measurement report, the one or more processors are configured to: The one or more compressed portions of the Doppler spectrum frame are generated by compressing the plurality of Doppler spectrum instances.

22. The apparatus of claim 21, wherein to compress the plurality of Doppler spectrum instances, the one or more processors are configured to: determining a Doppler domain basis selection for each Doppler spectrum instance in the plurality of Doppler spectrum instances; determining one or more coefficient quantizations for compressing each Doppler spectrum instance of the plurality of Doppler spectrum instances; as well as Each Doppler spectrum instance is compressed using the Doppler domain basis selection and the one or more coefficient quantization.

23. The apparatus of claim 22, wherein the micro-Doppler measurement report comprises one or more of the Doppler domain basis selection and the one or more coefficient quantization determined for each of the plurality of Doppler spectrum instances.

24. The apparatus of claim 21, wherein to compress the plurality of Doppler spectrum instances, the one or more processors are configured to generate one or more differential reports based on the plurality of Doppler spectrum instances by: obtaining Doppler spectrum reports for one or more reference instances selected from the plurality of Doppler spectrum instances; determining a neighboring instance associated with each of the one or more reference instances, wherein the neighboring instance does not include the one or more reference instances; as well as For each reference instance of the one or more reference instances, a difference report is generated between the corresponding reference instance and a corresponding neighboring instance associated with the corresponding reference instance, wherein the difference report includes a difference quantization.

25. The apparatus of claim 24, wherein: Each reference instance is associated with a corresponding one or more neighboring instances; and Each reference instance and each corresponding one or more neighboring instances are consecutive Doppler spectrum instances included in the plurality of Doppler spectrum instances.

26. The apparatus of claim 16, wherein the one or more processors are further configured to: Based on the micro-Doppler measurement report, at least one characteristic of the target based on information in the micro-Doppler measurement report is determined.

27. The apparatus of claim 26, wherein: The at least one characteristic identifies the target as a drone or unmanned aerial vehicle (UAV).

28. The apparatus of claim 16, wherein: The first signal is received by a radar receiving node included in a multistatic or bistatic sensing system; and The micro-Doppler measurement report is generated using the radar receiving node.

29. The apparatus of claim 28, wherein the one or more processors are further configured to: The micro-Doppler measurement report is sent using the radar receiving node to a remote processing node, wherein the remote processing node is included in the same multi-station sensing system as the radar receiving node.

30. The apparatus of claim 16, wherein the one or more Doppler domain characteristics include a micro-Doppler signature of the target.

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