Method and apparatus for presence detection using UWB radar

Abstract

A method of presence detection using an ultra wide band (UWB) radar includes obtaining at least one centroid based on UWB radar measurements. The method comprises the following steps: classifying at least one centroid as human motion or non-human motion based on at least one feature of the at least one centroid and human motion conditions; and determining a two-dimensional (2D) position of the human motion based on the UWB radar measurements when the at least one center of mass is classified as the human motion. The method includes updating, based on the classification of the at least one centroid, a current state value indicating whether the presence of a person is detected within a boundary of the space.

Description

Technical Field

[0001] The present disclosure generally relates to radar systems. More specifically, the present disclosure relates to presence detection using ultra-wideband (UWB) radar. Background Art

[0002] Presence detection is a useful function in smart home devices. Presence detection provides a basis for other functions such as intelligent control of lighting, heating, ventilation, and air conditioning. Currently, this presence detection function is implemented in different ways, including cameras or infrared (IR) sensors, but each way has its own drawbacks (such as privacy, sensitivity to lighting conditions). Summary of the Invention

[0003] The present disclosure provides presence detection using ultra-wideband (UWB) radar.

[0004] In an embodiment, a method for presence detection using UWB radar is provided. The method may include: obtaining at least one centroid based on ultra-wideband (UWB) radar measurement results. The method may include: classifying at least one centroid as human movement or non-human movement based on at least one feature of the at least one centroid and human movement conditions. The method may include: when at least one centroid is classified as human movement, determining a two-dimensional (2D) position of the human movement based on UWB radar measurement results. The method may include: updating a current state value indicating whether a person is detected within a boundary of a three-dimensional (3D) space based on the classification of at least one centroid.

[0005] The method may include: determining a boundary of a movement area, the movement area being a plane within a boundary of a 3D space. The 2D position of the human movement may be within the plane. The UWB radar measurement results may be generated by at least two antennas adjacent to each other, at least one of the at least two antennas being parallel or coplanar with the plane.

[0006] The current state value may represent a current state of a person's presence within a boundary of a 3D space. Updating the current state value may include: updating the current state value to one of the following based on whether the 2D position is within the boundary of the 3D space: a first current state value indicating that a person is detected within the boundary of the 3D space, such that the current state is non-empty; or a second current state value indicating that a person is not detected within the boundary of the 3D space, such that the current state is empty.

[0007] The method may include: in response to determining that at least one centroid is classified as human movement and the current state is non-empty, the method may include: resetting a no-movement time count. The method may include: resetting a CIR time window defined by a sliding window of a series of channel impulse responses (CIR) inputs.

[0008] The method may include: in response to determining that the 2D position of the human movement is within the boundary of the movement area in the 3D space, mapping the latest channel impulse response (CIR) identifier (ID) to the coordinates of the 2D position of the human movement. The method may include: recording the latest CIR ID in the CIR time window. The method may include: when determining a new 2D position of the human movement, updating the CIR time window by recording the new CIR ID mapped to the coordinates of the new 2D position of the human movement. The CIR window may include a series of CIR IDs.

[0009] The method may include: in response to determining that none of the at least one centroid is classified as a human movement, the method may include: for each new CIR before the expiration of the timeout period, incrementing the no-movement time count, updating the channel impulse response (CIR) time window by adding the new CIR, and not changing the current state. The method may include: after the expiration of the timeout period, performing a respiration detection algorithm to determine whether a respiration signal is detected within a proximity distance to the latest 2D position of the human movement.

[0010] Performing the respiration detection algorithm may include: detecting a peak of the average energy based on the historical UWB radar measurements corresponding to the CIR window; determining whether a respiration signal is detected based on whether the significance of each peak satisfies a threshold significance condition; calculating the 2D position of the respiration corresponding to each peak that satisfies the threshold significance condition; and determining that a respiration signal is detected based on determining that the 2D position of the respiration is within a proximity distance to the latest 2D position of the human movement.

[0011] The method may include: in response to determining that a respiration signal is detected, adding an affirmative indicator to the historical register of the respiration detection result. The method may include: in response to determining that a respiration signal is not detected, adding a negative indicator to the historical register of the respiration detection result. The method may include: determining whether a human presence is detected within the boundary of the 3D space based on the count of affirmative indicators in the historical register of the respiration detection result.

[0012] The method may include: determining that a human presence is detected within the boundary of the 3D space based on the count of affirmative indicators in the historical register of the respiration detection result exceeding a threshold.

[0013] Obtaining at least one centroid may include: calculating a range-Doppler map (RDM) based on UWB radar measurement results; for each cell under test (CUT) in the RDM, calculating an adaptive threshold power level of the CUT based on the energy levels of adjacent cells of the CUT; and determining that the CUT corresponds to a potential target based on the power level of the CUT exceeding the adaptive threshold power level; and generating a cell-averaging constant false alarm rate (CA-CFAR) hit map, the CA-CFAR hit map including hit cells mapped to each CUT corresponding to a potential target in the RDM; filtering the CA-CFAR hit map by applying erosion and dilation of morphological processing; and applying a clustering algorithm to the filtered CA-CFAR hit map, wherein the centroid of each corresponding cluster of adjacent cells represents the cluster, and wherein each corresponding cluster represents a corresponding target.

[0014] In an embodiment, an electronic device for presence detection is provided. The electronic device may include: a memory storing instructions; and at least one processor configured to cause the electronic device to perform operations when executing the instructions. The operations may include: obtaining at least one centroid based on ultra-wideband (UWB) radar measurement results. The operations may include: classifying the at least one centroid as human motion or non-human motion based on at least one feature of the at least one centroid and human motion conditions. The operations may include: when the at least one centroid is classified as human motion, determining a two-dimensional (2D) position of the human motion based on UWB radar measurement results. The operations may include: updating a current state value indicating whether a human presence is detected within the boundaries of a three-dimensional (3D) space based on the classification of the at least one centroid.

[0015] In an embodiment, a non-transitory computer-readable storage medium is provided. The non-transitory computer-readable storage medium may store instructions that, when executed by at least one processor of an electronic device, cause the electronic device to perform operations. The operations may include: obtaining at least one centroid based on ultra-wideband (UWB) radar measurement results. The operations may include: classifying the at least one centroid as human motion or non-human motion based on at least one feature of the at least one centroid and human motion conditions. The operations may include: when the at least one centroid is classified as human motion, determining a two-dimensional (2D) position of the human motion based on UWB radar measurement results. The operations may include: updating a current state value indicating whether a human presence is detected within the boundaries of a three-dimensional (3D) space based on the classification of the at least one centroid.

[0016] Other technical features may be apparent to those skilled in the art according to the following drawings, description, and claims. Description of the Drawings

[0017] To more fully understand the present disclosure and its advantages, reference is now made to the following description taken in conjunction with the accompanying drawings, in which like reference numerals represent like components:

[0018] Figure 1 Shows an example communication system according to an embodiment of the present disclosure;

[0019] Figure 2 Shows an example electronic device according to an embodiment of the present disclosure;

[0020] Figure 3 Shows a three-dimensional view of an example electronic device including a plurality of ultra-wideband (UWB) antenna modules according to an embodiment of the present disclosure;

[0021] Figure 4 Shows an example architecture of a monostatic radar in an electronic device 400 according to an embodiment of the present disclosure;

[0022] Figure 5 Shows an overview of a room-level presence detection algorithm according to an embodiment of the present disclosure;

[0023] Figure 6 Shows example channel impulse response (CIR) data according to an embodiment of the present disclosure;

[0024] Figure 7 Shows an example instantaneous human motion detection module according to an embodiment of the present disclosure;

[0025] Figure 8 Shows an example process of calculating a range-Doppler map (RDM) from an input CIR window according to an embodiment of the present disclosure;

[0026] Figure 9 Shows an example RDM according to an embodiment of the present disclosure;

[0027] Figure 10A Shows an example cell-averaging constant false alarm rate (CA-CFAR) hit map generated based on the RDM according to an embodiment of the present disclosure; Figure 9 of;

[0028] Figure 10B and Figure 10C Shows additional features that can be extracted from a CIR window according to an embodiment of the present disclosure;

[0029] Figure 11 Shows an example erosion-type morphological processing according to an embodiment of the present disclosure;

[0030] Figure 12 Shows an example dilation-type morphological processing according to an embodiment of the present disclosure;

[0031] Figure 13Shows an example CA-CFAR hit map generated based on RDM according to an embodiment of the present disclosure;

[0032] Figure 14 Shows the erosion-dilation filtered CA-CFAR hit map generated by processing the CA-CFAR hit map through the CFAR hit map filter via Figure 7 ; Figure 13 the CA-CFAR hit map;

[0033] Figure 15 , Figure 16 , Figure 17 and Figure 18 shows the RDM features, spectrogram features, power-weighted Doppler features, bandwidth features, and density features extracted from the same CIR window according to an embodiment of the present disclosure;

[0034] Figure 19 Shows an example event processor module according to an embodiment of the present disclosure;

[0035] Figure 20 Shows an example human motion coordinate recording module according to an embodiment of the present disclosure;

[0036] Figure 21 Shows an example respiration detection module according to an embodiment of the present disclosure;

[0037] Figure 22 Shows an example CIRSUM after applying clutter cancellation and low-pass filtering according to an embodiment of the present disclosure;

[0038] Figure 23 Shows an example graph of the average energy per range bin according to an embodiment of the present disclosure; and

[0039] Figure 24 Shows a method for presence detection using a UWB radar according to an embodiment of the present disclosure. Detailed Description

[0040] Before proceeding with the following detailed description, it may be advantageous to set forth definitions of certain words and phrases used throughout this patent document. The terms "send," "receive," and "communicate" and their derivatives include both direct and indirect communication. The terms "comprise" and "include" and their derivatives mean inclusion without limitation. The term "or" is inclusive and means and / or. The phrase "associated with" and its derivatives mean including, being included within, interconnecting with, containing, being contained within, connected to or being connected with, coupled to or being coupled with, capable of communicating with, cooperating with, interlacing, juxtaposing, being proximate to, bound to or being bound with, having, having the attribute of, having a relationship to or being related to, and the like.

[0041] In addition, the various functions described below can be implemented or supported by one or more computer programs, each of which is formed of computer-readable program code and embodied in a computer-readable medium. The terms "application" and "program" refer to one or more computer programs, software components, instruction sets, procedures, functions, objects, classes, instances, related data, or portions thereof suitable for implementation in appropriate computer-readable program code. The phrase "computer-readable program code" includes any type of computer code, including source code, object code, and executable code. The phrase "computer-readable medium" includes any type of medium that can be accessed by a computer, such as read-only memory (ROM), random access memory (RAM), hard disk drive, compact disk (CD), digital video disk (DVD), or any other type of memory. A "non-transitory" computer-readable medium does not include a wired, wireless, optical, or other communication link that transmits transitory electrical or other signals. A non-transitory computer-readable medium includes a medium in which data can be permanently stored and a medium in which data can be stored and later rewritten, such as a rewritable optical disk or an erasable memory device.

[0042] Terms and phrases such as “have”, “may have”, “include”, or “may include” used herein, which refer to features (such as numbers, functions, operations, or components such as parts), indicate the presence of the feature without excluding the presence of other features. In addition, phrases such as “A or B”, “at least one of A and / or B”, or “one or more of A and / or B” used herein may include all possible combinations of A and B. For example, “A or B”, “at least one of A and B”, and “at least one of A or B” may indicate any of the following cases: (1) including at least one A, (2) including at least one B, or (3) including at least one A and at least one B. In addition, terms such as “first” and “second” used herein may modify various components regardless of importance and do not limit these components. These terms are only used to distinguish components from each other. For example, a first user device and a second user device may indicate different user devices from each other, regardless of the importance or order of the devices. Without departing from the scope of the present disclosure, the first component may be represented as the second component, and vice versa.

[0043] It will be understood that when an element (e.g., a first element) is referred to as being “coupled (operatively or communicatively) to another element (e.g., a second element) / coupled with another element (e.g., a second element)” or “connected to another element (e.g., a second element) / connected with another element (e.g., a second element)”, the element may be directly coupled or connected to the other element, or may be coupled or connected to the other element via a third element. In contrast, it will be understood that when an element (e.g., a first element) is referred to as being “directly coupled to another element (e.g., a second element) / directly coupled with another element (e.g., a second element)” or “directly connected to another element (e.g., a second element) / directly connected with another element (e.g., a second element)”, no other element (e.g., a third element) is interposed between the element and the other element.

[0044] The phrase “configured (or set) to” used herein may be interchangeably used with phrases such as “suitable for”, “capable of”, “designed to”, “adapted to”, “manufactured to”, or “able to...”, as appropriate. The phrase “configured (or set) to” does not inherently mean “specially designed in hardware”. Rather, the phrase “configured to” may mean that a device is capable of performing an operation together with another device or component. For example, the phrase “a processor configured (or set) to perform A, B, and C” may represent a general-purpose processor (e.g., a CPU or an application processor) that can perform operations by executing one or more software programs stored in a memory device, or a dedicated processor (e.g., an embedded processor) for performing the operations.

[0045] The terms and phrases used herein are for describing only some embodiments of the present disclosure and do not limit the scope of other embodiments of the present disclosure. It will be understood that the singular forms "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. All terms and phrases used herein (including technical and scientific terms and phrases) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of the present disclosure pertain. It will also be understood that terms and phrases such as those defined in a common dictionary should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and should not be interpreted as ideal or overly formal unless expressly so defined herein. In some cases, the terms and phrases defined herein may be interpreted to exclude embodiments of the present disclosure.

[0046] Definitions for certain other words and phrases may be provided throughout this patent document. One of ordinary skill in the art should understand that in many cases, if not most cases, such definitions apply to both the prior and future use of such defined words and phrases.

[0047] The following discussion of Figures 1 to 24 and the various embodiments used in this patent document to describe the principles of the present disclosure are for illustration only and should not be construed in any way as limiting the scope of the present disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any appropriately arranged wireless communication device.

[0048] Ultra-wideband (UWB) technology is gradually being incorporated into mobile and consumer products for various applications, including device-to-device positioning and indoor positioning. Next-generation UWB chips are also equipped with radar capabilities, enabling devices including the UWB chip to detect motion within the radar field of view (FOV) and the location of such motion. The present disclosure presents a method of using a UWB chip equipped with radar capabilities to determine the room occupancy status using a single device.

[0049] Figure 1 An example communication system in accordance with an embodiment of the present disclosure is shown. Figure 1 The embodiment of the communication system 100 shown is for illustration only. Other embodiments of the communication system 100 may be used without departing from the scope of the present disclosure.

[0050] The communication system 100 includes a network 102 that facilitates communication between various components in the communication system 100. For example, the network 102 may transfer IP packets, frame relay frames, asynchronous transfer mode (ATM) cells, or other information between network addresses. The network 102 includes one or more local area networks (LANs), metropolitan area networks (MANs), wide area networks (WANs), all or a portion of a global network such as the Internet, or any other communication system at one or more locations.

[0051] In this example, network 102 facilitates communication between server 104 and various client devices 106-114. Client devices 106-114 can be, for example, smart phones, tablet computers, laptop computers, personal computers, wearable devices, head-mounted displays, etc. Server 104 can represent one or more servers. Each server 104 includes any suitable computing or processing device that can provide computing services for one or more client devices (such as client devices 106-114). For example, each server 104 can include one or more processing devices, one or more memories for storing instructions and data, and one or more network interfaces that facilitate communication via network 102.

[0052] Each of client devices 106-114 represents any suitable computing or processing device that interacts with at least one server (such as server 104) or other computing devices via network 102. Client devices 106-114 include desktop computer 106, mobile phone or mobile device 108 (such as a smart phone), PDA 110, laptop computer 112, and tablet computer 114. However, any other or additional client devices (such as a hub device) can be used in communication system 100. A smart phone represents a type of mobile device 108, which is a handheld device that runs a mobile operating system and integrates mobile broadband cellular network connectivity for voice, short message service (SMS), and Internet data communication.

[0053] In this example, some of client devices 108 and 110-114 communicate with network 102 indirectly. For example, mobile device 108 and PDA 110 communicate via one or more base stations 116 (such as a cellular base station or eNodeB (eNB) or gNodeB (gNB)). Additionally, laptop computer 112 and tablet computer 114 communicate via one or more wireless access points 118 (such as an IEEE 802.11 wireless access point). Note that these examples are for illustration only, and each of client devices 106-114 can communicate directly with network 102 or can communicate indirectly with network 102 via any suitable intermediate device or network. In certain embodiments, any one of client devices 106-114 securely and effectively sends information to another device, such as server 104.

[0054] Although Figure 1 an example of communication system 100 is shown, various changes can be made to Figure 1 it. For example, communication system 100 can include any number of various components with any suitable arrangement. Generally, computing and communication systems have a wide variety of configurations, andFigure 1 The scope of the present disclosure is not limited to any particular configuration. Although Figure 1 an operating environment is shown in which various features disclosed in this patent document can be used, these features can be used in any other suitable system.

[0055] Figure 2 An example electronic device in accordance with an embodiment of the present disclosure is shown. Specifically, Figure 2 an example electronic device 200 is shown, and the electronic device 200 can represent Figure 1 the server 104 or one or more client devices 106 - 114 in Figure 1 The electronic device 200 can be a mobile communication device, such as a mobile station, user station, wireless terminal, desktop computer (similar to Figure 1 the desktop computer 106 in

[0056] ), portable electronic device (similar to Figure 2 the mobile device 108, PDA 110, laptop computer 112 or tablet computer 114 in

[0057] As shown, the electronic device 200 includes a transceiver 210, a transmit (TX) processing circuit 215, a microphone 220, and a receive (RX) processing circuit 225. The transceiver 210 can include, for example, an RF transceiver, a BLUETOOTH transceiver, a WiFi transceiver, a ZIGBEE transceiver, an infrared transceiver, and various other wireless communication signals. The electronic device 200 also includes a speaker 230, a processor 240, an input / output (I / O) interface (IF) 245, an input device 250, a display 255, a memory 260, and a sensor 275. The memory 260 includes an operating system (OS) 261 and one or more applications 262.The transceiver 210 may include an antenna array 205, and the antenna array 205 includes a plurality of antennas. The antennas of the antenna array may include radiation elements formed of a conductive material or a conductive pattern formed in or on a substrate. The transceiver 210 transmits signals or power to the electronic device 200, or receives signals or power from the electronic device 200. The transceiver 210 receives input signals transmitted from an access point (e.g., a base station, a WiFi router, or a BLUETOOTH device) or other devices on the network 102 (e.g., WiFi, BLUETOOTH, cellular network, 5G, 6G, LTE, LTE-A, WiMAX, or any other type of wireless network). The transceiver 210 down-converts the input RF signal to generate an intermediate frequency or baseband signal. The intermediate frequency or baseband signal is sent to the RX processing circuit 225, and the RX processing circuit 225 generates a processed baseband signal by filtering, decoding, and / or digitizing the baseband or intermediate frequency signal. The RX processing circuit 225 sends the processed baseband signal to the speaker 230 (e.g., for voice data) or to the processor 240 for further processing (e.g., for web browsing data).

[0058] The TX processing circuit 215 receives analog or digital voice data from the microphone 220 or other output baseband data from the processor 240. The output baseband data may include web data, emails, or interactive video game data. The TX processing circuit 215 encodes, multiplexes, and / or digitizes the output baseband data to generate a processed baseband signal or an intermediate frequency signal. The transceiver 210 receives the output processed baseband signal or intermediate frequency signal from the TX processing circuit 215 and up-converts the baseband signal or intermediate frequency signal to a transmission signal.

[0059] The processor 240 may include one or more processors or other processing devices. The processor 240 may execute instructions (e.g., the OS 261) stored in the memory 260 to control the overall operation of the electronic device 200. For example, according to well-known principles, the processor 240 may control the reception of downlink (DL) channel signals and the transmission of uplink (UL) channel signals through the transceiver 210, the RX processing circuit 225, and the TX processing circuit 215. The processor 240 may include any suitable number and type of processors or other devices arranged in any suitable manner. For example, in some embodiments, the processor 240 includes at least one microprocessor or microcontroller. Example types of the processor 240 include microprocessors, microcontrollers, digital signal processors, field programmable gate arrays, application specific integrated circuits, and discrete circuits. In some embodiments, the processor 240 may include a neural network.

[0060] The processor 240 is also capable of executing other processes and programs residing in the memory 260, such as operations for receiving and storing data. The processor 240 can move data into or out of the memory 260 according to the requirements of the executing processes. In some embodiments, the processor 240 is configured to execute one or more applications 262 based on the OS 261 or in response to signals received from an external source or an operator. For example, the applications 262 may include a multimedia player (such as a music player or a video player), a phone call application, a virtual personal assistant, etc.

[0061] According to an embodiment of the present disclosure, the application 262 may include a room-level presence detection system 500 ( Figure 5 ), which uses UWB radar signals and radar measurement results to determine whether a person is present within the boundaries of a defined space (e.g., a room; a movement area), and updates the occupancy status 263 based on this presence detection. The occupancy status 263 is used for intelligent control of lighting, heating, ventilation, and air conditioning (HVAC), or other household appliances. The memory 260 includes a long-term CIR window 264, a respiration detection result register 265, and a human body movement coordinate register 266, which will be further described below.

[0062] The processor 240 is also coupled to an I / O interface 245, which provides the electronic device 200 with the ability to connect to other devices (such as client devices 106 - 114). The I / O interface 245 is a communication path between these accessories and the processor 240.

[0063] The processor 240 is also coupled to an input device 250 and a display 255. An operator of the electronic device 200 can use the input device 250 to enter data or input into the electronic device 200. The input device 250 can be a keyboard, a touch screen, a mouse, a trackball, a voice input, or other devices capable of serving as a user interface to allow a user to interact with the electronic device 200. For example, the input device 250 may include speech recognition processing, thereby allowing a user to input voice commands. In another example, the input device 250 may include a touch panel, a (digital) pen sensor, a keypad, or an ultrasonic input device. The touch panel can recognize touch inputs of, for example, at least one scheme, such as a capacitive scheme, a pressure-sensitive scheme, an infrared scheme, or an ultrasonic scheme. The input device 250 may be associated with sensors 275, a camera, etc., thereby providing additional input to the processor 240. The input device 250 may also include control circuitry. In a capacitive scheme, the input device 250 can recognize touches or proximities.

[0064] The display 255 can be a liquid crystal display (LCD), a light emitting diode (LED) display, an organic LED (OLED), an active matrix OLED (AMOLED), or other displays capable of rendering text and / or graphics such as from websites, videos, games, images, etc. The display 255 can be a single display screen or multiple display screens capable of creating a stereoscopic display. In some embodiments, the display 255 is a head-up display (HUD).

[0065] The memory 260 is coupled to the processor 240. A portion of the memory 260 can include RAM, and another portion of the memory 260 can include flash memory or other ROM. The memory 260 can include persistent memory (not shown), which represents any structure capable of storing information (e.g., data, program code, and / or other suitable information) and facilitating information retrieval. The memory 260 can contain one or more components or devices that support long-term data storage, such as read-only memory, hard disk drives, flash memory, or optical disks.

[0066] The electronic device 200 also includes one or more sensors 275, which can measure physical quantities or detect the activation state of the electronic device 200 and convert the measured or detected information into an electrical signal. For example, the sensors 275 can include one or more buttons for touch input, cameras, gesture sensors, optical sensors, video cameras, one or more inertial measurement units (IMUs) (e.g., gyroscopes or gyro sensors), and accelerometers. The sensors 275 can also include barometric pressure sensors, magnetic sensors or magnetometers, grip sensors, proximity sensors, ambient light sensors, biophysical sensors, temperature / humidity sensors, illuminance sensors, ultraviolet (UV) sensors, electromyogram (EMG) sensors, electroencephalogram (EEG) sensors, electrocardiogram (ECG) sensors, IR sensors, ultrasonic sensors, iris sensors, fingerprint sensors, color sensors (e.g., red green blue (RGB) sensors), etc. The sensors 275 can also include control circuits for controlling any of the sensors included therein. Any of these sensors 275 can be located within the electronic device 200 or within an auxiliary device operatively connected to the electronic device 200.

[0067] The electronic device 200 used in this document may include a transceiver that can both send and receive radar signals. For example, the transceiver 210 includes a radar transceiver 270, which will be described in more detail below. In this embodiment, one or more of the transceivers in the transceiver 210 is the radar transceiver 270, and the radar transceiver 270 is configured to send and receive signals for detection and ranging purposes. For example, the radar transceiver 270 can be any type of transceiver, including but not limited to a WiFi transceiver, such as an 802.11ay transceiver. The radar transceiver 270 can operate both radar and communication signals simultaneously. The radar transceiver 270 includes two or more antenna arrays or antenna pairs, each of which includes a transmitter (or transmitting antenna) and a receiver (or receiving antenna). The radar transceiver 270 can transmit signals of various frequencies. For example, the radar transceiver 270 can transmit signals of the following frequencies, including but not limited to 6 GHz, 7 GHz, 8 GHz, 28 GHz, 39 GHz, 60 GHz, and 77 GHz. In some embodiments, the signals transmitted by the radar transceiver 270 can include but are not limited to millimeter-wave (mmWave) signals or ultra-wideband (UWB) signals. The radar transceiver 270 can receive signals that were initially transmitted from the radar transceiver 270 and then bounced or reflected off a target object in the surrounding environment of the electronic device 200. In some embodiments, the radar transceiver 270 can be associated with the input device 250 to provide additional input to the processor 240.

[0068] In certain embodiments, the radar transceiver 270 is a monostatic radar. A monostatic radar includes a transmitter of radar signals and a receiver that receives the delayed echo of the radar signals, where the transmitter and the receiver are located at the same or similar positions. For example, the transmitter and the receiver can use the same antenna, or are located at almost the same position when using separate but adjacent antennas. A monostatic radar is considered coherent such that the transmitter and the receiver are synchronized via a common time reference. The following Figure 4 shows an example monostatic radar.

[0069] In certain embodiments, the radar transceiver 270 can include a transmitter and a receiver. In the radar transceiver 270, the transmitter can transmit UWB signals. In the radar transceiver 270, the receiver can receive the UWB signals that were initially transmitted from the transmitter and then bounced or reflected off a target object in the surrounding environment of the electronic device 200. The processor 240 can analyze the time difference between when the UWB signals are transmitted and received to measure the distance between the target object and the electronic device 200.

[0070] Although Figure 2 shows one example of the electronic device 200, various changes can be made to Figure 2 it. For example,Figure 2 The various components in Figure 2 Although the electronic device 200 is shown as being configured as a mobile phone, a tablet computer, or a smart phone, the electronic device 200 can be configured to operate as other types of mobile or stationary devices.

[0071] Figure 3 FIG. shows a three-dimensional view of an example electronic device 300 including a UWB antenna module 302 according to an embodiment of the present disclosure. The electronic device 300 can represent Figure 1 one or more of the client devices 106 - 114 in Figure 2 or the electronic device 200 in Figure 3 The embodiments of the electronic device 300 shown in

[0072] As used herein, the term "module" can include units implemented in hardware, software, or firmware, and can be used interchangeably with other terms (e.g., "logic", "logic block", "component", or "circuit"). A module can be a single integrated component or its smallest unit or a part thereof suitable for performing one or more functions. For example, according to an embodiment, a module can be implemented in the form of an application specific integrated circuit (ASIC).

[0073] The first antenna module 302a and the second antenna module 302b are located at the left and right edges of the electronic device 300. For the sake of brevity, the first antenna module 302a and the second antenna module 302b are collectively referred to as the antenna module 302. In some embodiments, the antenna module 302 includes an antenna panel, a circuit connecting the antenna panel to a processor (e.g., Figure 2 the processor 240 in

[0074] The electronic device 300 can be equipped with multiple antenna elements. For example, the first antenna module 302a and the second antenna module 302b are disposed in the electronic device 300, where each antenna module 302 includes one or more antenna elements. When the electronic device 300 attempts to establish a connection with a base station (e.g., the base station 116), the electronic device 300 performs beamforming using the antenna module 302.

[0075] Figure 4 FIG. shows an example architecture of a monostatic radar in an electronic device 400 according to an embodiment of the present disclosure. Figure 4The embodiments of the architecture of the monostatic radar shown are for illustrative purposes only, and other embodiments may be used without departing from the scope of the present disclosure.

[0076] The electronic device 400 includes a processor 402, a transmitter 404, and a receiver 406. The electronic device 400 may be similar to Figure 1 any one of the client devices 106 - 114 in Figure 2 the electronic device 200 in Figure 3 or the electronic device 300 in Figure 2 . The processor 402 is similar to Figure 2 the processor 240 in

[0077] . Additionally, the transmitter 404 and the receiver 406 may be included within

[0078] the radar transceiver 270 of

[0079] . The radar can be used to detect the distance, speed, and / or angle of the target object 408. When operating at UWB frequencies, the radar can be used for applications such as proximity sensing, gesture recognition, vital or respiration detection, UWB occlusion detection, etc. Figure 4Only the UWB radar antenna pairs 414 and 416 are shown, but it can be understood that the RX antenna 416 includes at least two RX antennas that are adjacent to each other and parallel or coplanar with a plane (e.g., the floor of a room) within the boundary of a defined space (e.g., a movement area). Similarly, the TX antenna 414 can include at least two TX antennas that are adjacent to each other and parallel or coplanar with the RX antennas in the same plane.

[0080] Figure 5 An overview of a room-level presence detection system 500 according to an embodiment of the present disclosure is shown. Figure 5 The embodiment of the room-level presence detection system 500 shown is for illustrative purposes only, and other embodiments can be used without departing from the scope of the present disclosure. The room-level presence detection system 500 can be or can include an algorithm executed by an electronic device including a transceiver configured to transmit and receive UWB radar signals, such as Figure 1 the client devices 106 - 114 in Figure 2 the electronic device 200 including a radar transceiver 270, the electronic device 300 including a plurality of antenna modules 302a - 302b, and Figure 4 any one of the electronic devices 400 including a monostatic radar architecture in

[0081] The room-level presence detection system 500 includes a plurality of sub-algorithms, which are referred to as modules. In this particular example, the room-level presence detection system 500 includes an instantaneous human detection module 510, a room boundary determination module 520, an event processor module 530, and a respiration detection module 540. These modules 510, 520, 530, and 540 process the UWB radar measurement results generated by the UWB module. In the present disclosure, the human motion detection module 510 can also be referred to as a human motion detector (HMD) 510. In the present disclosure, the event processor module 530 includes an event processor that is triggered to operate when the occurrence of human motion is detected and a different event processor that is triggered to operate when human motion is not detected.

[0082] A central device that includes a UWB module and implements a room-level presence detection system 500 can be located within and operate within a room. The UWB module is capable of detecting movement within an area and the location of the detected movement, where the area is defined by the maximum detectable range and the field of view (FOV) of the UWB module. This functionality of the UWB module enables the room-level presence detection system 500 to detect whether a room is unoccupied (i.e., no one is in the room; the room state is "empty") or occupied (i.e., there is at least one person in the room; the room state is "present"). This "occupied / unoccupied" state is also referred to as the "person occupancy" state or the "present / empty" room state. The person occupancy state is a parameter that stores a first value representing the unoccupied state or a second value representing the occupied state. When detecting the presence of people in a room, there are some challenges in implementing this technology for detecting the occupied / unoccupied state in a defined area (such as a room) where people may move, as further described below.

[0083] The first challenge is to avoid false detection of the presence of people, such as when the occupied state is falsely detected. Specifically, if the occupied / unoccupied state of a room is based solely on detected movement, the central device may have many false detections because movement can come not only from human activities (e.g., a person walking, sitting, exercising, etc.) but also from household appliances and pets (e.g., dogs, cats, etc.). Examples of movement of household appliances include a fan oscillating, fan blades rotating, and the navigation of a cleaning robot performing sweeping, vacuuming, or mopping. In the room-level presence detection system 500, the HMD 510 incorporates technologies to address this first challenge, which are further described below.

[0084] The second challenge is to avoid false detection of a room being vacant (i.e., falsely detecting the unoccupied state). Specifically, in some cases, even when a person is present in a room and stays in the room, the person may remain stationary with little or no movement, such as when a person is sleeping or watching a movie on a couch. A presence detection system based solely on instantaneous movement detection will produce unnecessary flickering, resulting in a poor user experience and, in some cases, may also be harmful. Unnecessary flickering includes: the person occupancy state quickly switching between unoccupied and occupied. As an automatic response to the unnecessary flickering, a poor user experience includes: the lights in the room constantly turning on and off. As another example, the unnecessary flickering may control another harmful automatic response, such as the HVAC system being turned on and off. In the room-level presence detection system 500, both the event processor module 530 and the respiration detection module 540 incorporate technologies to address this second challenge, which are further described below.

[0085] The third challenge is to define the physical space that differentiates the hub device (e.g., the presence detection system) from the coverage area of the hub device. The coverage range of the hub device is generally substantially different from the physical space of the room or area to be monitored. More specifically, movement detected within the coverage area of the hub device may not be within the desired area / room, and thus, a person moving outside the desired area / room may also trigger the movement detection of the hub device. Such movement detection outside the boundary of the desired monitoring area may also result in a poor user experience, e.g., the lights in a room being turned on when a person stays (e.g., is located) in an adjacent room. In the room-level presence detection system 500, both the HMD 510 and the room boundary determination module 520 incorporate techniques to address this third challenge, which are further described below.

[0086] As a solution to the above challenges, an electronic device according to an embodiment of the present disclosure is equipped with a UWB radar module and at least two RX antennas (e.g., two RX antennas and one TX antenna; or two TX-RX antenna pairs), and performs the room-level presence detection system 500. The UWB radar module can transmit high-bandwidth pulses, receive signals reflected back from an object, and calculate the channel impulse response (CIR), which is the signature of the surrounding environment. Movement within the coverage area of the UWB radar module can be displayed in a range-Doppler map (RDM) of the measurement results sensed by the UWB radar module, including position and velocity.

[0087] As a solution to the above first challenge, the room-level presence detection system 500 includes two parts of determination: First, it includes a classifier that differentiates human movement from non-human movement based on a set of features of each centroid; Second, once classified as human movement, the two-dimensional (2D) position of the human movement is determined based on the UWB signal. The two-dimensional (2D) position of the human movement is displayed in the form of Cartesian coordinates (e.g., x, y coordinates) on a map of the coverage area of the UWB radar module. For example, the HMD Figure 4 Figure 4 510 can perform this two-part determination. For each moving target detected by the UWB radar (e.g., target 408 in

[0088] ), a radar spectrogram is calculated, and a set of features of each centroid is extracted from the radar spectrogram. For each corresponding centroid, this set of features is then input into a classification algorithm to determine the category of the movement, such as human movement, pet movement, and other movements. This classification algorithm avoids false detection caused by non-human movement and lays a foundation for subsequent room state determination. Figure 4the azimuth (e.g., of the target 408) and the angle of arrival (e.g., the target 408 exhibits movement). In addition, the 2D position of the movement within the room / area to be monitored can also be determined. That is, the room-level presence detection system 500 enables the central device to track the last measured position (e.g., 2D position) of the target object (i.e., a person), and in the case where the last position of the moving target is known (due to tracking), the respiration detection module 540 can focus on the area around the last measured position of the movement. As a technical advantage, the electronic device according to an embodiment of the present disclosure uses two RX antennas of the UWB radar and the steering vector (e.g., in Equation 11) to enhance the received signal, so that respiration can be detected even when the target 408 is at a distance of 4-5 meters from the UWB radar. When the electronic device includes only one TX-RX antenna pair (or only uses one antenna pair), respiration can be detected only when the target 408 is at a distance of 1-2 meters from the UWB radar.

[0089] As a solution to the third challenge, the room boundary determination module 520 determines the boundaries of the room / area to be monitored, and the HMD 510 obtains the determined boundaries from the room boundary determination module 520. To ensure that the system 500 only accepts movements within the room / area to be monitored, the boundaries of the room / area to be monitored are compared with the 2D position of the human movement determined by the HMD 510. According to an embodiment of the present disclosure, the central device (e.g., Figure 2 in 200) can learn the boundaries of the physical room or the area boundaries in different ways: (i) an active calibration phase performed by the user; (ii) an adaptive learning process, i.e., recording the history of movements, and the central device sometimes prompts (asks) the user to indicate whether the detection is within or outside the room / area boundary.

[0090] In a first embodiment of the room boundary determination module 520, the active calibration phase occurs during the setup phase before use, during which the central device requests the user to walk around the boundary of the room / area while the central device records all movements. Once the user finishes walking around the boundary of the room / area, the central device collects all the coordinates of the points along the boundary of the room / area to define the perimeter of the room / area.

[0091] In a second embodiment of the room boundary determination module 520, the central device gradually learns the boundaries of the room / area based on user feedback. First, all movements within the coverage area of the UWB radar of the central device are accepted as movements within the room. During use, in the case where the user moves outside the room / area but still within the coverage area of the UWB radar, the user can actively disable the incorrect movement detection.

[0092] In a third embodiment of the room boundary determination module 520, the central device may occasionally query the user and request that the user input feedback indicating whether the user is within the boundary of a room / area. In the second and third embodiments, the central device may utilize these additional inputs of user feedback to gradually adjust the acceptable region of human movement to be closer to the boundary of the physical space of the room or the desired area.

[0093] As a solution to the second challenge, as part of the room-level presence detection system 500, the detected respiration signal is verified. Specifically, the detected respiration signal is verified by analyzing the most recent human movement position (e.g., obtained from the HMD 510), thereby reducing false detections and saving resources. Whenever the UWB radar does not detect movement, the CIR data (e.g., Figure 6 the CIR data 600 in Figure 2 is accumulated into a long buffer called the long-term CIR window (

[0094] Figure 6 264 in Figure 6 ). When the long-term CIR window has accumulated sufficient data, the accumulated CIR data in the long buffer is analyzed to determine whether a human respiration signal is detected in the accumulated data. If a respiration signal is detected, the 2D position of the respiration signal is compared with the last recorded human movement position to improve the reliability of respiration detection. Even if the UWB radar does not detect any movement within the room, if the respiration signal is verified, then the occupancy status of the person remains as occupied. Figure 4 the distance 412 in

[0095] In an IR-UWB radar system, a UWB pulse (e.g., Figure 4 the signal 410 in Figure 4 is transmitted from a transmitter (TX) antenna, scattered by an object in the environment (e.g., Figure 4The intensity or amplitude received by the RX antenna 416 (in ) typically depends on the relative size of the object and the distance of the object, i.e., the distance from the antenna (e.g., Figure 4 the TX antenna 414 and the RX antenna 416 in ) to the object position.

[0096] The firmware of the UWB radar module estimates the channel impulse response (CIR) by performing a channel estimation method. The original CIR can be expressed by Equation 1, where h[n,m] represents the CIR at the nth slow-time index and the mth range bin on the RX antenna, and where N r represents the number of range bins.

[0097] ...(1)

[0098] Figure 6 The exemplary CIR data 600 in is obtained from a UWB module including a TX-RX antenna pair. With one antenna pair, the UWB module can determine the distance from the radar to a moving target, but the UWB module cannot determine the angle of arrival of the target because it does not have at least two TX-RX antenna pairs.

[0099] To determine such an angle of arrival, the UWB module requires at least two RX antennas that are arranged adjacent to each other in the same plane as the moving area of the target, and such an architecture is used to determine whether motion occurs in a room. In other words, one TX antenna and two RX antennas can determine both the distance and the AOA. For ease of explanation, the examples in the present disclosure refer to a setup scenario having the following characteristics: Most of the movements of people in a room are movements occurring on the horizontal plane defined by the floor of the room (or a plane parallel to the same room floor); and the two RX antennas of the UWB module are also placed on the same horizontal plane as the room floor (or a plane parallel to the room floor). The hub device housing the UWB module may be located in a corner or an edge of the room, so the distance alone (e.g., the distance measured by radar) is not sufficient to determine whether the position of the motion is inside the room. However, when the radar measures both the distance and the angle of arrival, the 2D position of the motion can be compared with the space within the room boundary to determine whether the position of the human motion is inside or outside the room boundary and more accurately indicate the state as indoor or outdoor. The angle of arrival of the reflected signal corresponding to the reflection from the target motion can be determined by comparing the phases of the received signals at the two RX antennas.

[0100] Embodiments of the present disclosure are not limited to 2D position tracking. In some embodiments of the present disclosure, implementation Figure 5The system of the room-level presence detection system 500 can be extended (e.g., further configured) to track the movement of a target object in 3D space by using additional antennas (e.g., a third antenna pair) in the vertical dimension. Tracking the movement of a target in 3D space includes: tracking the movement of a person within a horizontal plane (e.g., the same horizontal plane as the room floor) and the movement of the person not parallel to the horizontal plane, such as when the person goes up and / or down the stairs. In both cases of 2D space or 3D space, Figure 5 the ability to determine both the distance and the angle of arrival of a moving target is utilized in the room-level presence detection system 500.

[0101] Figure 7 FIG. shows an example instantaneous human motion detection module 700 according to an embodiment of the present disclosure. Figure 7 The embodiment of the HMD 700 shown in is for illustration only, and other embodiments may be used without departing from the scope of the present disclosure. Figure 7 The instantaneous human motion detection module 700 of Figure 5 is the same as the HMD 510 of

[0102] and may be referred to as the HMD 700. The HMD 700 reduces false detections caused by movements other than human movements. In addition, the HMD 700 calculates the position of human movements and determines the indoor / outdoor state by comparing the calculated position with the physical room boundaries.

[0103] The HMD 700 operates based on a moving window. That is, each time, the latest CIR window 702 from two TX-RX antenna pairs is formed and input into the processing flow. A CIR ID is assigned to the CIR window 702. The CIR window 702 includes a first CIR window corresponding to the first RX antenna RX1 and a second CIR window corresponding to the second RX antenna RX2 located on the same horizontal plane as RX1. The CIR windows 702 are captured simultaneously during the same time window and thus correspond to the same time window. The occupancy status of the room for the current time window is updated at the end of the processing, e.g., at blocks ********. The CIR window size is a design parameter that can be selected to be long enough to capture human movements. For example, for a UWB module with a sampling rate of 200 Hz, the window size can be 256 samples, making the window length approximately 1 second. It should be noted that, for faster implementation of the Fourier transform, the window size is usually selected to be a power of 2.

[0104] The processing flow of the HMD 700 can start from block 704. At block 704, a range-Doppler map (RDM) 706 is calculated based on the input CIR window 702. Refer to the following for Figure 8Further describe this calculation of the RDM 706. In the present disclosure, the RDM 706 is an array, where each element is called a cell.

[0105] At block 708, a cell-average constant false alarm rate (CA-CFAR) hit map 710 is generated based on the RDM 706. The following is with reference to Figure 10A Further describe this process of generating the CA-CFAR hit map 710.

[0106] At block 712, the CA-CFAR hit map 710 is filtered by a CFAR hit map filter to generate a filtered hit map 714. The filtered hit map 714 is processed by a target localization algorithm 716. The target localization algorithm may include a clustering algorithm, such as DBSCAN. The target localization algorithm identifies a set 718 of target objects, such as {T1, T2, … T N}. The set 718 of targets is also referred to as the set of centroids. In the set 718 of target objects, each target is indexed from 1 to N.

[0107] Each target in the set 718 of target objects is processed by block 720 to determine the motion type of the target. The motion type of the target is a classification such that the motion type is classified as human motion or classified as non-human motion. At block 722, if the motion type of the target is classified as non-human motion, the processing flow proceeds to block 724. At block 724, if the HMD 700 determines that the set 718 of targets includes one or more targets that have not been processed by blocks 720 and 722, then as part of a processing loop (also referred to as a centroid loop), the next unprocessed target in the set 718 of targets is processed by blocks 720 and 722. On the other hand, if the motion type of the target is classified as human motion, the processing flow proceeds to block 726. The following is with reference to Figures 15 - 18 Further describe this process of determining the motion type.

[0108] At block 726, the position of the target (also referred to as the position of the human motion) is determined. For example, the position 728 of the motion can be 2D coordinates (such as (x, y)), or can be 3D coordinates (such as (x, y, z)). The 2D coordinates are calculated based on distance measurement results and angle-of-arrival estimates.

[0109] At block 730, the position of the motion is compared with the boundaries of the defined space (such as the boundaries of a room or the boundaries of a motion area) to determine whether the position 728 of the motion is inside the room. If the position 728 of the motion is outside the room, the processing flow proceeds to block 724.

[0110] At block 730, in response to determining that the position 728 of the movement is within the room, the position 728 of the movement is recorded as a parameter (recorded_human_movement_xy), and the human_movement_detected flag is set to TRUE. The recorded_human_movement_xy parameter stores the latest, most recent coordinates and is updated by recording the position 728 of the human movement. For each target in the set 718 of targets, the human_movement_detected flag is set to the default value FALSE until it is determined that the position 728 of the human target is within the room. The processing flow continues from block 730 to block 740, thereby exiting the centroid loop portion of the algorithm (the block that returns from 724 to block 720). In some embodiments, exiting the centroid loop before all N centroids in the set 718 of targets have been processed by blocks 720 - 722 will skip (e.g., stop) processing the remaining unprocessed centroids in the set 718 of targets by blocks 720 - 722, such that fewer centroids than the whole of the set 718 of targets are processed by the centroid loop.

[0111] At block 740, an event processor is selected and the HMD 700 outputs the flag human_movement_detected. Based on the value of the flag human_movement_detected after the centroid loop has ended (either after exiting the centroid loop or after all centroids in the set 718 of targets have been processed), different event processors are called: the event processor 750 for detecting a human or the event processor 760 for not detecting a human. More specifically, the event processor 750 for detecting a human is selected based on determining that the set 718 of targets includes at least one target corresponding to the flag human_movement_detected set to TRUE. Alternatively, the event processor 760 for not detecting a human is selected based on determining that the set 718 of targets does not include any target corresponding to the flag human_movement_detected set to TRUE. In other words, the event processor 760 for not detecting a human is selected based on determining that all targets in the set 718 of targets have the flag human_movement_detected set to FALSE.

[0112] Figure 8 An example flow for calculating the RDM from an input CIR window according to an embodiment of the present disclosure is shown. Figure 7 The CIR window 702 received as input in Figure 8 can be represented by the CIR window 802 shown in Figure 8 The flow 804 shown inFigure 7 Details of the process executed at the middle block 704. Figure 7 The RDM 706 in can be represented by Figure 8 the RDM 806 shown in. Figure 8 The embodiment of processing the CIR window 802 into the RDM 806 shown in is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure.

[0113] It should be understood that this process of calculating the RDM 806 is executed for two CIR windows received from the first RX antenna RX1 and the second RX antenna RX2. Therefore, to avoid repeated description, Figure 8 the process shown in will be described as calculating the RDM 806 according to the CIR window 802 included in the input CIR window received from the first RX antenna RX1.

[0114] The CIR window 802 can be the raw CIR data of a certain time window (e.g., several seconds, e.g., 3 - 4 seconds). The CIR window 802 is represented as a 2D array, where the time dimension 808 includes this time window and is divided into multiple time sub - blocks, which are indexed by the slow - time index (n). This 2D array includes a range - bin dimension 810, which is similar to Figure 6 the range - bin dimension shown on the x - axis of the CIR data 600 in. That is, the CIR window 802 includes m rows and n columns, such that each row corresponds to the slow - time index (using the index n), and each column corresponds to a tap (using the index m). The tap is also called a range - bin, which represents the measured distance from the radar to the target. The time - dimension arrow indicates that the slow - time index (n) increases from the bottom to the top of the array. The range - bin arrow indicates that the range - bin index (m) increases from left to right. As an example, the first row of the CIR window 802 can represent 0 to 100 nanoseconds, the second row of the CIR window 802 can represent 101 - 200 nanoseconds, and the time rows continue to stack until the CIR window 802 is full.

[0115] At block 812, a Fourier transform (FT) is applied on the time dimension 808 of the CIR window. Specifically, the FT is applied to each column of the CIR window 802 to obtain the corresponding column of the RDM 806. For example, the FFT is applied to the m - th column 814 of the CIR window 802 to calculate the m - th column of the RDM 806. The RDM 806 is a 2D graph, where one dimension is the range - bin dimension 816 (i.e., the distance from the radar of the central device to the target), and the other dimension is the Doppler - frequency dimension 818 (i.e., the speed of the target).

[0116] Embodiments of the present disclosure are not limited to the application of FT, and other variants of FT include the Fast Fourier Transform (FFT) or FFT with zero Doppler nulling. That is, in some embodiments, the input CIR window 802 of size N is converted to the RDM 806 by applying the FFT over the slow-time index n, as shown in Equation 2. FFT as shown in Equation 2.

[0117] ...(2)

[0118] According to zero Doppler nulling, the zero-frequency component of the RDM is set to 0 (zero nulling) to eliminate non-moving clutter components. That is, any Doppler cell corresponding to zero velocity is set to be equal to zero, thereby ignoring stationary objects. In this example, the RDM 806 includes a whole row 820 that is nulled to zero, and the cells 822 - 824 represent potential targets. The cells 822 - 824 representing potential targets are not (moving or non-moving) clutter components. Among the cells 822 - 824 representing potential targets, the energy level of the moving target cell 822 is higher than that of the potential target cell 824 and is more likely to represent a moving target. Each cell in the RDM806 representing a potential target (including the moving target cell) is referred to as a Cell Under Test (CUT). Each CUT can be identified by an associated range cell (tap m) and an associated Doppler cell (velocity k).

[0119] Figure 9 An example RDM 906 according to an embodiment of the present disclosure is shown. Figure 9 The embodiment of the RDM 906 shown is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure. Figure 9 The RDM 906 in Figure 7 can be the same as or similar to the RDM 706 in Figure 8 or the RDM 806 in

[0120] In this example RDM 906, the Doppler frequency dimension is measured in centimeters per second (cm / s), and the RDM 906 is calculated based on the input CIR window starting 5.82 seconds after the start of the operation session.

[0121] Figure 10A An embodiment based on the present disclosure is shown Figure 9The example unit average constant false alarm rate (CA-CFAR) hit map generated by the RDM 906 hits FIG. 1000. Figure 10A The embodiments of the hit map 1000 shown in Figure 10A are for illustration only, and other embodiments may be used without departing from the scope of the present disclosure.

[0122] During Figure 7 the CA-CFAR detection process performed at block 708 of Figure 7 , for each CUT in the calculated RDM 906, the power level of the CUT is compared with a threshold to determine whether it belongs to a potential target.

[0123] During Figure 8 the CA-CFAR detection process performed at block 708 of Figure 8 , an adaptive threshold is calculated for each CUT based on the energy levels of neighboring cells. An example radar target detection method is cell average constant false alarm rate (CA-CFAR), where an adaptive threshold is calculated for each cell based on the energy levels of neighboring cells, which provides a constant false alarm probability. The adaptive threshold level for a particular CUT is calculated by computing the average power level of a block of cells surrounding the CUT. In some embodiments, the average power level of neighboring cells is the average power level of adjacent cells. These neighboring cells closest to the CUT are referred to as guard cells. In some embodiments, the guard cells are ignored to avoid power from the CUT itself disrupting the estimation of the adaptive threshold level. That is, neighboring cells can be cells adjacent to the guard cells, such as the second ring starting from the CUT.

[0124] Based on determining that the energy level of the CUT itself exceeds the corresponding adaptive threshold, the HMD 700 determines that the CUT corresponds to a potential target and assigns a hit value (e.g., binary value 1) to the CUT. If the power level of the CUT is greater than the local average power, the CUT is declared a hit. Based on determining that the energy level of the CUT does not exceed the corresponding adaptive threshold, the HMD 700 determines that the CUT does not correspond to a potential target and assigns a miss value (e.g., binary value 0) to the CUT.

[0125] The output of the CA-CFAR detection is the hit map 1000, which is a 2D map having the same size and dimensions as the RDM 906. In the hit map 1000, each cell is a hit 1002 or a miss 1010. Cells that are misses 1010 are represented by a darker shade than the hit cells, and the hit cells are represented by a lighter shade.

[0126] For some moving objects (e.g., a moving fan), since the frequency of oscillation or rotation is fixed, the CFAR hit map (e.g., Figure 10AThe feature on the hit map 1000) is the single-line unit 1002A. In contrast, the features of human movement on the CFAR hit map often include adjacent frequencies. Therefore, the features of human movement on the CFAR hit map are a set of adjacent units 1002B in both the frequency dimension (e.g., the velocity dimension) and the distance dimension. To eliminate the movement of detected non-human objects, morphological processing (which is a method used in image processing) can be used to filter the CFAR hit map. Specifically, Figure 11 and Figure 12 illustrate two morphological processing operations used in a CFAR hit map filter (e.g., Figure 7 block 712 in Figure 10A ), where the units in the hit map (e.g.,

[0127] Figure 10B and Figure 10C illustrate additional features that can be extracted from the CIR window according to embodiments of the present disclosure. That is, Figures 9 - 10C illustrates the features extracted from the same CIR window. Figure 10B illustrates a graph of the distance distribution with respect to the distance unit dimension. Figure 10C illustrates a graph of the frequency distribution with respect to the frequency unit dimension.

[0128] Figure 11 illustrates an example erosion-type morphological processing 1100 according to an embodiment of the present disclosure. Figure 11 The embodiment of the erosion 1100 shown in

[0129] is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure. Erosion shrinks the image by stripping a layer of pixels from both the inner and outer boundaries of the region. The holes and gaps between different regions become larger, and small details are eliminated. For example, in the hit map 1110, the unhit units are represented by a darker shade than the hit units, while the hit units are represented by a lighter shade.

[0130] For each CUT in the hit map 1110, the guard units adjacent to the CUT are filtered out by the erosion 1100, so that the eroded-filtered hit map 1120 includes fewer hits than the original hit map 1110. In some embodiments, the erosion 1100 can convert each unhit guard unit of a specific CUT to an unhit. In another embodiment, the erosion 1100 can convert each unhit guard unit of a specific CUT to an unhit, unless the guard unit is adjacent to a hit.

[0131] Figure 12 illustrates an example dilation-type morphological processing according to an embodiment of the present disclosure. Figure 12The embodiment of dilation 1200 shown is for illustration only, and other embodiments may be used without departing from the scope of the present disclosure. Dilation adds a layer of hit cells to both the inner and outer boundaries of the region.

[0132] In the illustrated embodiment, dilation 1200 is applied to the same hit map 1110, which is the same as Figure 11 and Figure 12 as shown. Compared with the erosion 1100 of Figure 11 , the dilation 1200 of Figure 12 is the opposite process. For each CUT in the hit map input to dilation 1200, each guard cell that is unhit for that particular CUT is converted to a hit, such that the dilated-filtered hit map 1220 includes more hit cells than the hit map input to (e.g., received by) dilation 1200.

[0133] In other embodiments, as Figure 13 and Figure 14 shown, Figure 7 the CFAR hit map filtering at block 712 of Figure 13 includes: applying erosion 1100 to the hit map and then applying dilation 1200 to the eroded-filtered hit map. Figure 9 shows an example CA-CFAR hit map 1300 generated based on RDM (e.g., RDM 906 in Figure 14 shows an example eroded-dilated-filtered CA-CFAR hit map 1400 generated by processing the CA-CFAR hit map 1300 through the CFAR hit map filter at block 712 of Figure 7 . The eroded-filtered CA-CFAR hit map can be generated by applying the erosion-type morphological processing 1100 of Figure 11 to the CA-CFAR hit map 1300 of Figure 13 . Subsequently, the dilated-filtered hit map can be generated by applying the dilation-type morphological processing 1200 of Figure 12 to the eroded-filtered CA-CFAR hit map. Figure 13 and Figure 14 The embodiments of the CA-CFAR hit map 1300 and the eroded-dilated-filtered hit map 1400 shown are for illustration only, and other embodiments may be used without departing from the scope of the present disclosure. Unhit cells are represented by a darker shade than hit cells, and hit cells are represented by a lighter shade.

[0134] Figure 13 The hit map 1300 of Figure 14In the filtered hit map 1400 of erosion-dilation filtering, they are filtered out or discarded. By first applying erosion 1100 and then applying dilation 1200, the movement of detected non-human objects can be filtered out, so that in the set of target cells and potential target cells, only the hit cells 1402 related to human movement are retained, as Figure 14 shown.

[0135] The filtered hit map 1400 is input into Figure 7 the target localization algorithm 716 therein. From the filtered hit map 1400, the remaining hit cells 1402 related to human movement are input into a clustering algorithm (e.g., DBSCAN). The clustering algorithm groups adjacent cells into a group representing a target. The clustering algorithm determines the centroid for each group, so that each group is represented by the centroid of the group. That is, each target is represented by the centroid of the group representing the target. The centroid is a point whose coordinates are equal to the average of the coordinates of all cells in the group. In this case, the coordinates m,k are the coordinates according to the dimensions of the range cell (m) and the Doppler cell (k). The centroid of each group is indexed from 1 to N as the set 718 of target objects {T1,T2,…T N}.

[0136] For the next step in the HMD 700, return to reference Figure 7 blocks 720 and 722 therein to determine whether the movement around each centroid is indeed human movement. To make this determination, additional features are extracted from the same CIR window 702. Figure 15 , Figure 16 , Figure 17 and Figure 18 show the features extracted from the same CIR window according to an embodiment of the present disclosure. Figure 15 shows an example RDM 1500. Figure 16 shows a spectrogram 1600. Figure 17 shows the power-weighted Doppler (PWD) 1700 and the bandwidth 1750 as a function of time. Figure 18 shows the density 1800 as a function of time.

[0137] The HMD 700 extracts a plurality of range cells around the centroid, from the starting range cell ID (start_rbid) 1502 to the ending range cell ID (end_rbid) 1504 in the Figure 15 RDM 1�00. The HMD 700 calculates the cir_sum value according to these range cells from start_rbid 1502 to end_rbid 1504 as the sum of all CIR time series.

[0138] The HMD 700 calculates the spectrogram 1600 (also known as spectrogram_integrated) based on the cir_sum value, as Figure 16 shown. In some embodiments, the time dimension of the spectrogram 1600 can be extended to the duration of the CIR window. After obtaining the spectrogram 1600, the spectrogram 1600 can be used as the input to a machine learning classifier that is trained to determine whether the spectrogram 1600 is from human motion. In another embodiment, a threshold-based classifier can be used for human motion detection, i.e., to determine whether the spectrogram 1600 is from human motion.

[0139] From the spectrogram 1600, additional features can be extracted, including Figure 17 the PWD 1700 and the bandwidth 1750 in

[0140] ...(3)

[0141] ...(4)

[0142] As Figure 18 shown, the density 1800 as a function of time is a feature extracted from the spectrogram 1600. The density 1800 at time step j can be calculated as shown in Equation 5, where min_freq_cell represents the hit cell with the lowest frequency in this j-th time step, and max_freq_cell represents the hit cell with the highest frequency in this j-th time step, hits represents the number of hit cells between min_freq_cell and max_freq_cell, and Total represents the total number of cells between min_freq_cell and max_freq_cell.

[0143] ...(5)

[0144] From Figure 15 the RDM 1500, additional features are extracted, including the average energy E c ,j c corresponding to the target centroid (i av ), measured in dB. The average energy E av is determined based on the cells within the boundary region around the target centroid. The boundary region extends from i cExtend to a certain number (K) of units, from j in the Doppler dimension c Extend to K units. The average energy E av Is an estimate of the intensity of the radar signal from the target centroid. The average energy can be calculated according to Equation 6, where (i c , j c ) represents the coordinates of the target centroid in the RDM 1500, and the boundary region around the target centroid is defined as [i c -K: i c +K, j c -K: j c +K].

[0145] ...(6)

[0146] If the features corresponding to the target centroid satisfy the following three conditions represented by Equation 7, Equation 8, and Equation 9, then it is determined that the target centroid (i c , j c ) represents human motion. In Equation 7, human_average_energy_db_thres represents the distance-related threshold for a specific range cell ID rbid. Different range cell IDs correspond to different distance-related thresholds respectively. In Equation 8, PWDs_thres represents the threshold specified for the PWD 1700 and is compared with the maximum absolute value of the PWD 1700; bws_thres represents the threshold specified for the bandwidth 1750 and is compared with the maximum absolute value of the bandwidth 1750. In Equation 9, the window represents the time period from the start time (denoted as t_start) to the end time (denoted as t_end) of the spectrogram 1600.

[0147] ...(7)

[0148] ...(8)

[0149] ...(9)

[0150] Figure 19 Shows an example event processor module 1900 according to an embodiment of the present disclosure. Figure 19 The embodiment of the event processor module 1900 shown in is only for illustration, and other embodiments can be used without departing from the scope of the present disclosure. Figure 19 The event processor module 1900 shown in is the same as the event processor module 530 of Figure 5 and can be abbreviated as EHM 1900.

[0151] The HMD 700 generates outputs for each CIR window (702), and the time intervals between the outputs of the HMD 700 are much faster than the time it takes for a person to enter or leave a room. The EHM 1900 can be a state machine that prevents Figure 2 unnecessary flickering of the occupancy status 263 of the person, which is used for intelligent control of lighting, HVAC, or other household appliances. Before switching the occupancy status 263 of the person from the occupied state to the unoccupied state, the EHM 1900 determines whether the EHM 1900 detects a stationary person in the room (e.g., a person may be sleeping in bed or on a sofa), such that the breathing movement is the only human movement detected. The EHM 1900 receives the flag human_movement_detected from the HMD 700 and updates Figure 2 the occupancy status 263 of the person based on comparing the current occupancy status 263 with the flag human_movement_detected output by the HMD 700 and based on breathing detection.

[0152] The EHM 1900 begins by identifying the current occupancy status, e.g., Figure 2 the value of the current occupancy status 263. At block 1902, the EHM 1900 determines whether the current occupancy status 263 is the unoccupied state (shown as empty).

[0153] At block 1904, in response to determining that the current occupancy status 263 is the unoccupied state, the EHM 1900 determines whether to change the current occupancy status 263 based on comparing the flag human_movement_detected (obtained from the HMD 700) with the current occupancy status 263. If both the current occupancy status 263 and the flag human_movement_detected indicate the unoccupied state, the EHM 1900 determines not to change the current occupancy status 263, as shown at block 1906. If the flag human_movement_detected indicates an occupied state different from the current unoccupied state (263), the EHM 1900 determines to update and change the current occupancy status 263 to correspond to the occupied state indicated by the flag human_movement_detected, as shown at block 1908.

[0154] Alternatively, in response to determining that the current occupancy status 263 is an occupied status (shown as "person present"), the EHM 1900 makes a determination at block 1906 by performing the same comparison process as that performed at block 1904. If both the current occupancy status 263 and the flag human_movement_detected indicate an occupied status, the EHM 1900 determines not to change the current occupancy status 263, as shown at block 1912. At block 1912, the EHM 1900 maintains the current occupancy status as the occupied status, resets the no-movement time count, and resets the long-term CIR window (e.g., Figure 2 264 in

[0155] If the flag human_movement_detected indicates an unoccupied status different from the current occupied status (263), the EHM 1900 performs a respiration detection at block 1914 to determine whether to change the current occupancy status 263 to correspond to the unoccupied status indicated by the flag human_movement_detected.

[0156] The respiration detection process of block 1914 includes blocks 1916 - 1926 described further below. If respiration is detected at block 1914, the EHM 1900 determines not to change the current occupancy status 263; then at block 1928, in response to this determination, the EHM 1900 updates the current occupancy status 263 and maintains it as the occupied status. If respiration is not detected at block 1914, the EHM 1900 determines to update and change the current occupancy status 263 to correspond to the unoccupied status indicated by the flag human_movement_detected; then at block 1930, in response to this determination, the EHM 1900 switches the occupancy status 263 from the occupied status to the unoccupied status.

[0156] At block 1916, the no-movement time count is incremented, and the long-term CIR window is updated ( Figure 2in 264). Even if the flag human_movement_detected indicates no presence of a person for the current CIR window 702, the current CIR window 702 is added or accumulated into the long-term CIR window 264 so that the current CIR window 702 can be used as part of the next step in the respiration detection process. The room state does not immediately switch to the "empty" state, but rather the long-term CIR window 264 is repeatedly updated to accumulate each new CIR window 702 until the timeout period has elapsed. This timeout period is a design parameter and can be the minimum size of the long-term CIR window, which is chosen to be long enough to capture the human respiration signal in the accumulated CIR data. For example, the timeout can be 20 seconds, corresponding to 20 seconds of CIR data accumulated in the long-term CIR window 264.

[0157] At block 1918, if it is determined that the timeout period has not elapsed (i.e., sufficient CIR data has been accumulated in the long-term CIR window), the EHM 1900 maintains the current occupancy state 263, as shown at block 1920. Alternatively, if the timeout period has elapsed, at block 1922, the EHM 1900 invokes a respiration detection algorithm to process the long-term CIR window 264. That is, at block 1922, the long-term CIR window 264 is input into the respiration detection algorithm and processed, which determines whether a human respiration signal is detected within the long-term CIR window 264 and outputs a respiration detection result. If a human respiration signal is detected, the respiration detection result is a positive indicator; while if no human respiration signal is detected, the respiration detection result is a negative indicator.

[0158] At block 1924, the respiration detection result is added to a history register of respiration detection results (e.g., Figure 2 the respiration detection result register 265 in). At block 1926, the EHM 1900 determines whether a person's presence is still detected in the room based on whether the count of positive indicators or the count of negative indicators in the respiration detection result register 265 meets a threshold condition. For example, if the count of positive indicators is 20% or more of the total number of results in the respiration detection result register 265, the threshold condition is not met. If the threshold condition is not met, the EHM 1900 determines that human respiration is sufficiently detected in the room, and then the occupancy state 263 remains unchanged at block 1928. For example, if the count of positive indicators is more than 80% of the total number of results in the respiration detection result register 265, the threshold condition is met, and the EHM 1900 determines that human respiration is not sufficiently detected, and then the occupancy state 263 changes at block 1930.

[0159] Figure 20Shows an example human movement coordinate recording module (position recorder) 2000 according to an embodiment of the present disclosure. Figure 20 The embodiment of the position recorder 2000 shown is for illustration only, and other embodiments may be used without departing from the scope of the present disclosure.

[0160] To improve the accuracy of the respiration signal detection module, the room-level presence detection system 500 maintains a history of the recorded human movement coordinates (recorded_human_movement_xy) and stores it as Figure 2 the human movement coordinate register 266 in. The human movement coordinate register 266 is a mapping table that maps coordinate points (x, y) to the most recent CIR ID of the movement at that coordinate point. This history in the human movement coordinate register 266 is maintained in two ways: The first way is to run when the HMD 700 detects a new human movement, as shown in blocks 2010 - 2020; and the second way is to run for each new CIR window 702 input to the HMD 700, as shown in block 2030.

[0161] For example, at Figure 7 block 750, in response to detecting a new human movement with coordinates (x, y) and a CIR ID of cirid, the position recorder 2000 modifies the human movement coordinate register 266 at block 2010 by deleting the previously recorded entries that are within a close proximity to the new coordinates (x, y).

[0162] At block 2020, the position recorder 2000 updates the human movement coordinate register 266, as shown in Equation 10.

[0163] ...(10)

[0164] At block 2030, for each new CIR window 702 (with a CIR ID assigned), the position recorder 2000 modifies the human movement coordinate register 266 by deleting the entries in recorded_human_movement_xy that have an overly old CIR ID. In some embodiments, the deletion process at block 2030 can be performed upon receiving each CIR window 702, thereby updating when receiving a CIR ID corresponding to a non-human movement and when receiving a CIR ID cirid corresponding to a new human movement detected at the coordinates (x, y). In the shown embodiment, to reduce the consumption of computing resources, the deletion process at block 2030 is triggered by the update performed at block 2020, thereby updating recorded_human_movement_xy at a lower frequency, for example, only after detecting a new human movement (750).

[0165] Figure 21 An example respiratory detector 2100 that executes a respiratory detection algorithm according to an embodiment of the present disclosure is shown. Figure 21 The embodiment of the respiratory detector 2100 shown is for illustrative purposes only, and other embodiments may be used without departing from the scope of the present disclosure. Figure 21 The respiratory detector 2100 in Figure 19 is called by the EHM 1900 at block 1922. The respiratory detector 2100 generates, outputs, and records a respiratory detection result (recorded in the respiratory detection result register 265 of Figure 2 ), and the respiratory detection result is a positive indicator 2110 indicating that a human respiratory signal is detected or a negative indicator 2120 indicating that a human respiratory signal is not detected.

[0166] When two long-term CIR windows 702 (from two RX antennas) have accumulated sufficient samples (e.g., 20 seconds), the respiratory detector 2100 is triggered to operate. Before block 2102, the respiratory detector 2100 may perform some preprocessing to obtain the Figures 22 - 23 extracted features shown. Since Figure 21 the respiratory detector 2100 of Figure 22 analyzes the CIRSUM 2200 of Figure 23 and the graph 2300 of Figure 21 , Figure 22 and Figure 23 will be described together. Figure 22 An example CIRSUM 2200 after applying clutter cancellation and low-pass filtering according to an embodiment of the present disclosure is shown. Figure 23 An example graph 2300 of the average energy per range bin according to an embodiment of the present disclosure is shown. Figures 22 - 23 The embodiments of the CIRSUM 2200 and the graph 2300 shown are for illustrative purposes only, and other embodiments may be used without departing from the scope of the present disclosure.

[0167] Referring to Figure 22 , for each angle of arrival θ, the cirsum value is calculated according to Equation 11. In practice, the cirsum value is calculated by combining the signals received by the two RX antennas of the UWB radar module with the steering vector for each angle of arrival. This combination enhances the signal received at the radar (e.g., increases its amplitude), and thus can detect subtle movements such as respiratory movements at a greater distance (4 - 5 meters) from the radar compared to the case where the radar receives signals from only one RX antenna (i.e., can only detect respiration up to 2 meters away).

[0168] ...(11)

[0169] Analyze the CIRSUM 2200 for each angle of arrival θ to determine if there is a respiratory signal. To obtain the CIRSUM 2200, first process the cirsum values through a clutter cancellation process and a low-pass filter (e.g., cut-off frequency = 1 Hz) to remove frequency components outside the respiratory signal frequency range (e.g., 0.1 Hz to 1 Hz). Then, calculate the average energy per range bin based on the CIRSUM 2200 (e.g., Figure 23 of the graph 2300).

[0170] Refer to Figure 21 , at block 2102, the respiration detector 2100 detects peaks 2302A - 2302B on the average energy of the filtered long-term CIR window, as Figure 23 shown. More specifically, the respiration detector 2100 detects a set 2104 of peaks on the average energy array per range bin (i.e., Figure 23 the peaks 2302A - 2302B) and their associated significance values {shown as (p1,prop1)(p2,prop2)...(p N ,prop N )}. These peaks {(p1,prop1)(p2,prop2)...(p N ,prop N )} are analyzed one by one until a peak is determined to satisfy a set of conditions that define a human respiratory signal, such as a group that follows two conditions at blocks 2106 and 2108. The significance value associated with a peak can be a measure of how much greater the average energy of the peak is compared to the average energy of adjacent range bins.

[0171] If the peak significance is greater than the range-dependent threshold represented by Equation 12, the condition at block 2106 is satisfied, where prop i represents the significance value associated with the range bin position p i (also referred to as "range bin ID" or simply "range bin"), and i represents the index from 1 to the total number of peaks in the peak set 2104. The 2D position of peak 2302B can be the corresponding range bin ID p Figure 23 obtained from the graph 2300 of the average energy per range bin of i , and the associated significance value prop i can be the average energy.

[0172] ...(12)

[0173] At block 2107, based on the range bin p of the peaki and the angle of arrival θ of the current scan, which is mapped to Figure 22 of CIRSUM 2200), calculate the 2D position of the peak. If the 2D position of the peak is close to the position of the latest human movement (e.g., within a distance threshold), the condition at block 2108 is satisfied. More specifically, if the 2D position of the peak is close to the entry in the recorded_human_movement_xy that has the latest CIR ID, the condition at block 2108 is satisfied. The criterion for proximity can be that the Euclidean distance between the 2D position of the peak and the latest human movement position is less than a distance threshold (e.g., 0.5 meters).

[0174] In another embodiment, Doppler information of the movement can be recorded to identify entry / exit points or positions where people often sit / sleep. Such Doppler information can help improve the reliability of the respiration detector 2100 and reduce its search space.

[0175] At block 2110, when such a peak is determined to satisfy the two conditions at blocks 2106 and 2108, it is determined that there is a respiration signal in the current long-term CIR window 264. If the peak does not satisfy either of the conditions at blocks 2106 and 2108, then at block 2112, the respiration detector 2100 determines whether the set of arbitrary peaks 2104 includes peaks that have not been analyzed by blocks 2106 and 2108. If the entire set of peaks 2104 does not satisfy the two conditions at blocks 2106 and 2108, then at block 2120, it is determined that there is no respiration signal in the current long-term CIR window 264.

[0176] Figure 24 A method 2400 for presence detection using a UWB radar according to an embodiment of the present disclosure is shown. Figure 24 The embodiment of the method 2400 shown is for illustration only, and other embodiments can be used without departing from the scope of the present disclosure. The method 2400 is implemented by an electronic device 200 having a transceiver configured to transmit and receive UWB radar signals. For ease of explanation, the method 2400 is described as being executed by Figure 5 a processor 240 of a room-level presence detection system 500 that includes Figure 7 , Figure 19 , Figure 20 and Figure 21 components in.

[0177] At block 2410, the processor 240 obtains a set of centroids based on ultra-wideband (UWB) radar measurement results. For example, the processor 240 uses the HMD 700 to generate a target set 718 based on the CIR window 702, as Figure 7 shown.

[0178] In some embodiments of block 2410, to obtain a set of centroids based on UWB radar measurement results, the processor 240 calculates a range-Doppler map (RDM) based on the UWB radar measurement results. Examples of RDMs include Figure 8 , Figure 9 and Figure 15 RDMs 806, 906, and 1500. For each cell under test (CUT) in the RDM, the processor 240 performs a process similar to that of block 708-716 of Figure 7 . That is, for each CUT, the processor 240 calculates an adaptive threshold power level of the CUT based on the energy levels of the adjacent cells of the CUT; determines whether the power level of the CUT exceeds the adaptive threshold power level; and based on the power level of the CUT exceeding the adaptive threshold power level, determines that the CUT corresponds to a potential target. The processor 240 generates a CA-CFAR hit map, which includes hit cells mapped to each CUT corresponding to a potential target in the RDM. The processor 240 applies a clustering algorithm to the filtered CA-CFAR hit map. The centroid of each corresponding cluster of adjacent cells represents the cluster. Each corresponding cluster represents a corresponding target, such as Figure 4 target 408 in

[0179] At block 2420, for each corresponding centroid in the set of centroids, the processor 240 classifies the corresponding centroid as either human motion or non-human motion based on whether the feature set of the corresponding centroid meets the human motion condition. To classify the corresponding centroid as human motion, at block 2422, the processor 240 determines whether the feature set of the corresponding centroid meets the human motion condition.

[0180] At block 2430, for each corresponding centroid in the set of centroids, when the corresponding centroid is classified as human motion, the processor 240 determines the two-dimensional (2D) position of the human motion based on the UWB radar measurement results.

[0181] At block 2440, the processor 240 determines the boundary of the motion area, which is a plane within the spatial boundary. The 2D position of the human motion is within this plane. The UWB radar measurement results are generated by at least two antennas adjacent to each other, and at least one of the at least two antennas is parallel or coplanar with this plane.

[0182] At block 2450, the processor 240 determines whether a human presence is detected within the boundary of the 3D space. At block 2452, in response to determining that no human presence is detected within the boundary of the 3D space, the processor 240 classifies the corresponding centroid as no human presence detected, and the method 2400 returns to block 2420. The process at block 2452 is the same as that of Figure 7The process at block 760 is similar. At block 2454, in response to determining that a human presence is detected within the boundaries of the 3D space, the processor 240 classifies the corresponding centroid as detecting a human presence, and method 2400 proceeds to block 2460. The process at block 2454 is similar to Figure 7 the process at block 750.

[0183] In some embodiments, in response to determining that the 2D position of the human body movement is within the boundaries of the movement area in the space, the processor 240 maps the latest channel impulse response (CIR) identifier (ID) to the coordinates of the 2D position of the human body movement, records the latest CIR ID in the CIR time window, and when determining a new 2D position of the human body movement, the processor 240 updates the CIR time window by recording a new CIR ID mapped to the coordinates of the new 2D position of the human body movement. In such an embodiment, the CIR window includes a series of CIR IDs.

[0184] At block 2460, the processor 240 updates the current state value indicating whether a human presence is detected within the boundaries of the 3D space, at least in part based on the classification of each corresponding centroid in the set of centroids. In some embodiments, updating the current state value further includes: updating based on whether the 2D position is within the boundaries of the space, as shown in block 2462. In some embodiments, updating the current state value further includes: updating based on the current occupancy state 263 and based on the respiration detection result, as shown in block 2464.

[0185] At block 2462, it has been determined that the current state value represents the current state of human presence within the boundaries of the space. At block 2462, the processor 240 updates the current state value to one of the following values: a first current state value, indicating that a human presence is detected within the boundaries of the space, such that the current state is non-empty; or a second current state value, indicating that no human presence is detected within the boundaries of the space, such that the current state is empty.

[0186] At block 2464, in response to determining that the corresponding centroid in the set of centroids is classified as human body movement and the current occupancy state 263 is non-empty, the processor 240 resets the no-movement time count and resets the channel impulse response (CIR) time window defined by a sliding window of a series of CIR inputs. The process at block 2464 is similar to Figure 19 the process at block 1912.

[0187] In some cases, the current occupancy state 263 is non-empty, and the HMD 700 subsequently determines that none of the centroids in the set of centroids are classified as human body movement, for example Figure 19block 1916. For each new CIR 702 received before the expiration of the timeout period (e.g., block 1918), the processor 240 increments the no-motion time count, updates the long-term CIR time window 264 by adding the new CIR 702, and does not change the current occupancy status of the person, as shown in block 1920. After the expiration of the timeout period, the processor 240 executes a respiration detection algorithm to determine whether a respiration signal is detected within the proximity distance of the most recent 2D position of human movement. The processor 240 executes Figure 21 the respiration detection algorithm shown, which is called or triggered by the EHM 1900 at block 1922.

[0188] As part of executing the respiration detection algorithm 2100, the processor 240 detects an average energy peak based on historical UWB radar measurements corresponding to the CIR window, e.g., as shown in block 2102. The processor 240 determines whether a respiration signal is detected based on whether the significance of each peak meets a threshold significance condition, e.g., as shown in block 2106. The processor 240 calculates the 2D position of the respiration corresponding to each peak 2302A - 2302B that meets the threshold significance condition, e.g., as shown in block 2107. The processor 240 determines that a respiration signal is detected based on determining that the 2D position of the respiration is within the proximity distance of the most recent 2D position of human movement, e.g., as shown in block 2108. In response to determining that a respiration signal is detected, the processor 240 adds an affirmative indicator to the history register of the respiration detection result, e.g., as shown in block 2110. On the other hand, in response to determining that a respiration signal is not detected, the processor 240 adds a negative indicator to the history register of the respiration detection result, e.g., as shown in block 2120. The processor 240 determines whether a human presence is detected within the boundaries of the 3D space based on the count of affirmative indicators in the history register of the respiration detection result, e.g., as shown in block 1926.

[0189] Although Figure 24 illustrates an example method 2400 for presence detection using UWB radar, various changes can be made to Figure 24 it. For example, although shown as a series of steps, Figure 24 the various steps in

[0190] can overlap, occur in parallel, occur in a different order, or occur any number of times. The above flowcharts illustrate example methods that can be implemented in accordance with the principles of the present disclosure, and various changes can be made to the methods shown in the flowcharts herein. For example, although shown as a series of steps, the various steps in each figure can overlap, occur in parallel, occur in a different order, or occur multiple times. In another example, steps can be omitted or replaced with other steps.

[0191] Although the drawings illustrate different examples of user equipment, various changes can be made to the drawings. For example, a user equipment can include any number of various components with any suitable arrangement. Generally speaking, the drawings do not limit the scope of the present disclosure to any particular configuration. In addition, although the drawings illustrate an operating environment in which various user equipment features disclosed in this patent document can be used, these features can be used in any other suitable system.

[0192] Although the present disclosure has been described with exemplary embodiments, various changes and modifications can be suggested to those skilled in the art. The present disclosure is intended to include such changes and modifications that fall within the scope of the appended claims. The description in this application should not be construed as implying that any particular element, step, or function is an essential element that must be included in the scope of the claims. The scope of the patent subject matter is defined by the claims.

Claims

1. A method for presence detection by an electronic device (200), comprising: Obtaining (2410) at least one centroid based on ultra-wideband UWB radar measurement results; Classifying (2420) the at least one centroid as human motion or non-human motion based on at least one feature of the at least one centroid and human motion conditions; and When the at least one centroid is classified as the human motion, determining (2430) a two-dimensional 2D position of the human motion based on the UWB radar measurement results; and Updating (2460) a current state value indicating whether a human presence is detected within a boundary of a three-dimensional 3D space based on the classification of the at least one centroid.

2. The method according to claim 1, further comprising: Determining a boundary of a motion area, the motion area being a plane within the boundary of the 3D space, wherein: The 2D position of the human motion is within the plane; and The UWB radar measurement results are generated by at least two antennas adjacent to each other, and at least one of the at least two antennas is parallel or coplanar with the plane.

3. The method according to claim 1, wherein: The current state value represents a current state of human presence within the boundary of the 3D space; and Updating the current state value further comprises: based on whether the 2D position is within the boundary of the 3D space, updating the current state value to one of the following: A first current state value, indicating that a human presence is detected within the boundary of the 3D space, such that the current state is non-empty; or A second current state value, indicating that a human presence is not detected within the boundary of the 3D space, such that the current state is empty.

4. The method according to claim 3 further comprises: In response to determining that the at least one centroid is classified as the human motion and the current state is non-empty, Resetting a no-motion time count, and Resetting a CIR time window defined by a sliding window of a series of channel impulse responses CIR inputs.

5. The method according to claim 1, further comprising: In response to determining that the 2D position of the human motion is within the boundary of the motion area within the 3D space, mapping the latest channel impulse response CIR identifier ID to the coordinates of the 2D position of the human motion; Recording the latest CIR ID in the CIR time window; and When determining a new 2D position of the human motion, updating the CIR time window by recording a new CIR ID mapped to the coordinates of the new 2D position of the human motion, wherein the CIR window includes a series of CIR IDs.

6. The method according to claim 1, further comprising: In response to determining that none of the at least one centroid is classified as the human motion, For each new channel impulse response CIR before the expiration of a timeout period: Incrementing a no-motion time count, Updating the channel impulse response CIR time window by adding the new CIR, and Not changing the current state; and After the expiration of the timeout period, performing a respiration detection algorithm to determine whether a respiration signal is detected within a proximity distance to the latest 2D position of the human motion.

7. The method according to claim 6, wherein Performing the respiration detection algorithm includes: Detect the peak of the average energy based on historical UWB radar measurement results corresponding to the CIR window; Determine whether a breathing signal is detected based on whether the significance of each peak satisfies the threshold significance condition; Calculate the 2D position of the breathing corresponding to each peak that satisfies the threshold significance condition; and Determine that a breathing signal is detected based on the determined 2D position of the breathing being within a proximity distance of the latest 2D position of the human movement.

8. The method according to claim 6, further comprising: In response to determining that a breathing signal is detected, add an affirmative indicator to a historical register of breathing detection results; In response to determining that a breathing signal is not detected, add a negative indicator to the historical register of the breathing detection results; And Based on the count of the affirmative indicators in the historical register of the breathing detection results, determine whether a human presence is detected within the boundary of the 3D space.

9. The method according to claim 8, further comprising: Determine that a human presence is detected within the boundary of the 3D space based on the count of the affirmative indicators in the historical register of the breathing detection results exceeding a threshold.

10. The method according to claim 1, wherein, Obtaining the at least one centroid includes: Based on the UWB radar measurement results, calculate a range-Doppler map RDM; For each cell under test CUT in the RDM: Based on the energy levels of adjacent cells of the CUT, calculate an adaptive threshold power level of the CUT; and Based on the power level of the CUT exceeding the adaptive threshold power level, determine that the CUT corresponds to a potential target; and Generate a cell-averaging constant false alarm rate CA-CFAR hit map, the CA-CFAR hit map including hit cells mapped to each CUT in the RDM corresponding to the potential target; Filter the CA-CFAR hit map by applying erosion and dilation of morphological processing; and Apply a clustering algorithm to the filtered CA-CFAR hit map, wherein the centroid of each corresponding cluster of adjacent cells represents the cluster, and wherein each corresponding cluster represents a corresponding target.

11. An electronic device (200) for presence detection, comprising: A memory (260) storing instructions; And At least one processor (240) configured to cause the electronic device to perform operations when executing the instructions, the operations including: Obtain (2410) at least one centroid based on ultra-wideband UWB radar measurement results; Classify (242) the at least one centroid as human movement or non-human movement based on at least one feature of the at least one centroid and human movement conditions; and When the at least one centroid is classified as the human movement, determine (2430) a two-dimensional 2D position of the human movement based on the UWB radar measurement results; and Update (2460) a current state value indicating whether a human presence is detected within the boundary of a three-dimensional 3D space based on the classification of at least one corresponding centroid.

12. The electronic device according to claim 11, wherein, The operations further include at least one operation according to one of claims 2 to 10.

13. A non - transitory computer - readable storage medium storing instructions, which, when executed by at least one processor (240) of an electronic device (200), cause the electronic device (200) to perform operations, the operations including: Obtaining (2410) at least one centroid based on ultra - wideband UWB radar measurement results; Classifying (2420) the at least one centroid as human motion or non - human motion based on at least one feature of the at least one centroid and human motion conditions; and When the at least one centroid is classified as the human motion, determining (2430) a two - dimensional 2D position of the human motion based on the UWB radar measurement results; and Updating (2460) a current status value indicating whether a human presence is detected within the boundaries of a three - dimensional 3D space based on the classification of the at least one centroid.

14. The non-transitory computer-readable storage medium according to claim 13, wherein, The operations further include at least one operation according to one of claims 2 to 10.

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