Use of MIMO training fields for motion detection

Abstract

In general aspects, various fields of a PHY frame are used for motion detection. In some aspects, a first training field and a different second training field are identified in PHY frames of respective wireless signals transmitted between wireless communication devices in a wireless communication network. A first time-domain channel estimate and a second time-domain channel estimate are generated for each wireless signal. The first time-domain channel estimate is based on a first frequency-domain signal included in the first training field, and the second time-domain channel estimate is based on a second frequency-domain signal included in the second training field. Based on the first time-domain channel estimate, it is determined whether motion has occurred in space during a period of time, and based on the second time-domain channel estimate, the position of the motion in space is determined.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims priority to U.S. Provisional Application 62 / 928,684, filed October 31, 2019, entitled "Using MIMO Training Fields for Motion Detection", the content of which is incorporated herein by reference. Background Art

[0003] The following description relates to using multiple - input / multiple - output (MIMO) training fields for motion detection.

[0004] Motion detection systems have been used to detect the movement of objects, such as in a room or an outdoor area. In some exemplary motion detection systems, infrared or optical sensors are used to detect the movement of objects within the field of view of the sensors. Motion detection systems have been used in security systems, automated control systems, and other types of systems. Brief Description of the Drawings

[0005] Figure 1 is a diagram showing an exemplary wireless communication system.

[0006] Figure 2A - 2B is a diagram showing an exemplary wireless signal communicated between wireless communication devices.

[0007] Figure 2C is a diagram of an exemplary wireless sensing system operating to detect motion in space.

[0008] Figure 3 is a diagram showing an exemplary PHY frame.

[0009] Figure 4 is a diagram showing an exemplary PHY frame.

[0010] Figure 5 is a diagram showing an exemplary multiple - input multiple - output (MIMO) radio configuration.

[0011] Figure 6 is a diagram showing an exemplary spectrum of a wireless signal.

[0012] Figure 7 is a diagram showing an example signal path in a wireless communication system.

[0013] Figure 8 is a diagram showing an example filter representation of a propagation environment.

[0014] Figure 9A - 9C is a diagram showing an example change of the filter representation over time.

[0015] Figure 10A series of graphs showing the relationship between transmitted and received signals in a wireless communication system.

[0016] Figure 11 A graph showing example channels and signal information in a wireless communication system.

[0017] Figure 12 A schematic diagram of an example signal processing system of a motion detection system.

[0018] Figure 13 A flowchart showing a motion detection process.

[0019] Figure 14 A block diagram showing an example wireless communication device. Detailed Description

[0020] In some aspects described herein, a wireless sensing system can process wireless signals (e.g., radio frequency signals) transmitted through the space between wireless communication devices for wireless sensing applications. Exemplary wireless sensing applications include detecting motion, which can include one or more of the following: detecting the motion of an object in space, motion tracking, localizing motion in space, respiration detection, respiration monitoring, presence detection, gesture detection, gesture recognition, human detection (e.g., detecting moving and stationary humans), human tracking, fall detection, speed estimation, intrusion detection, walking detection, step counting, respiration rate detection, sleep pattern detection, apnea estimation, posture change detection, activity recognition, gait classification, gesture decoding, sign language recognition, hand tracking, heart rate estimation, respiration rate estimation, room occupancy detection, human dynamics monitoring, and other types of motion detection applications. Other examples of wireless sensing applications include object recognition, speech recognition, keystroke detection and recognition, tamper detection, touch detection, attack detection, user authentication, driver fatigue detection, traffic monitoring, smoking detection, campus violence detection, human counting, metal detection, human identification, bicycle localization, human queue estimation, Wi-Fi cameras, and other types of wireless sensing applications. For example, a wireless sensing system can operate as a motion detection system to detect the presence and location of motion based on Wi-Fi signals or other types of wireless signals.

[0021] The examples described herein can be used for home surveillance. In some cases, home surveillance using the wireless sensing systems described herein can provide several advantages, including complete home coverage through walls and in the dark, discreet detection without cameras, higher accuracy and reduced false alarms (e.g., compared to sensors that do not use Wi-Fi signals to sense their environment), and adjustable sensitivity. By incorporating Wi-Fi motion detection capabilities into routers and gateways, a robust motion detection system can be provided.

[0022] The examples described herein can also be used for health monitoring. Caregivers want to know that their loved ones are safe, while the elderly and those with special needs want to maintain their independence at home with dignity. In some instances, health monitoring using the wireless sensing systems described herein can provide a solution that uses wireless signals to detect movement without using cameras or invading privacy, generate alerts when abnormal activity is detected, track sleep patterns, and generate preventive health data. For example, caregivers can monitor movement, visits from healthcare professionals, and unusual behavior (such as staying in bed longer than normal). Additionally, movement can be unobtrusively monitored without the need for wearable devices, and the wireless sensing systems described herein provide a more economical and convenient alternative to assisted living facilities and other safety and health monitoring tools.

[0023] The examples described herein can also be useful for setting up smart homes. In some examples, the wireless sensing systems described herein use predictive analytics and artificial intelligence (AI) to learn movement patterns and trigger smart home functions accordingly. Examples of smart home functions that can be triggered include adjusting the thermostat when a person walks past the front door, turning other smart devices on or off based on preferences, automatically adjusting lighting, and adjusting the HVAC system based on the current occupants.

[0024] In certain aspects described herein, multi-input multi-output (MIMO) training fields included in wireless signals are used for motion detection. For example, the HE-LTF field in the PHY frame of a wireless transmission according to the Wi-Fi 6 standard (IEEE 802.11ax) can be used for motion detection. The wireless signals can be sent through space over a period of time, e.g., from one wireless communication device to another wireless communication device. An efficient long training field (HE-LTF) or another type of MIMO training field can be identified in the PHY frames of the respective wireless signals. Conventional PHY fields can also be identified in the PHY frames of the respective wireless signals. Example conventional PHY fields include L-LTF and L-STF. In some cases, channel information is generated based on the respective MIMO training fields and the respective conventional PHY fields. The channel information obtained from the conventional PHY fields can be used to make a macro-level determination of whether movement has occurred in space during the period of time. The channel information obtained from the MIMO training fields can be used to detect fine-grained motion attributes, e.g., the location or direction of movement in space during the period of time.

[0025] In some instances, aspects of the systems and techniques described herein provide technical improvements and advantages over existing methods. For example, compared to signals provided by traditional PHY fields, MIMO training fields can provide signals with higher frequency resolution, a greater number of subcarrier frequencies, and higher frequency bandwidths (or combinations of these features), which can provide more accurate and fine-grained motion detection capabilities. In some cases, motion detection can be performed with higher spatial and temporal resolution, precision, and accuracy. The technical improvements and advantages realized in examples where a wireless sensing system is used for motion detection can also be realized in examples where a wireless sensing system is used for other wireless sensing applications.

[0026] In some instances, a wireless sensing system can be implemented using a wireless communication network. Wireless signals received at one or more wireless communication devices in the wireless communication network can be analyzed to determine channel information for different communication links (between corresponding wireless communication device pairs in the network). Channel information can represent the physical medium to which a transfer function is applied to a wireless signal passing through space. In some instances, the channel information includes a channel response. The channel response can characterize the physical communication path, thereby representing, for example, the combined effects of scattering, fading, and power attenuation within the space between a transmitter and a receiver. In some instances, the channel information includes beamforming state information (e.g., a feedback matrix, a steering matrix, channel state information (CSI), etc.) provided by a beamforming system. Beamforming is a signal processing technique used in typically multi-antenna (multiple-input multiple-output (MIMO)) radio systems for directional signal transmission or reception. Beamforming can be achieved by operating the elements in an antenna array in such a way that signals with a particular angle experience constructive interference while other signals experience destructive interference.

[0027] The channel information for each communication link can be analyzed by one or more motion detection algorithms (e.g., running on a hub device, a client device, or other devices in the wireless communication network, or running on a remote device communicatively coupled to the network) to detect, for example, whether motion has occurred in space, to determine the relative position of the detected motion, or both. In some aspects, the channel information for each communication link can be analyzed to detect the presence or absence of an object (e.g., in the case where no motion is detected in space).

[0028] In some instances, a motion detection system returns motion data. In some implementations, the motion data is a result indicating the degree of motion in space, the position of motion in space, the direction of motion in space, the time at which the motion occurs, or a combination thereof. In some instances, the motion data can include a motion score, which can include or can be one or more than one of the following: a scalar indicating a signal perturbation level in an environment accessed by a wireless signal; an indication of whether motion is present; an indication of whether an object is present; or an indication or classification of a gesture performed in an environment accessed by a wireless signal.

[0029] In some implementations, one or more motion detection algorithms can be used to implement a motion detection system. Exemplary motion detection algorithms that can be used to detect motion based on wireless signals include the techniques described in the following patents: U.S. Patent 9,523,760 titled "Detecting Motion Based on Repeated Wireless Transmissions"; U.S. Patent 9,584,974 titled "Detecting Motion Based on Reference Signal Transmissions"; U.S. Patent 10,051,414 titled "Detecting Motion Based On Decompositions Of Channel Response Variations"; U.S. Patent 10,048,350 titled "Motion Detection Based on Groupings of Statistical Parameters of Wireless Signals"; U.S. Patent 10,108,903 titled "Motion Detection Based on Machine Learning of Wireless Signal Properties"; U.S. Patent 10,109,167 titled "Motion Localization in a Wireless Mesh Network Based on Motion Indicator Values"; U.S. Patent 10,109,168 titled "Motion Localization Based on Channel Response Characteristics"; U.S. Patent 10,743,143 titled "Determining a Motion Zone for a Location of Motion Detected by Wireless Signals"; U.S. Patent 10,605,908 titled "Motion Detection Based on Beamforming Dynamic Information from Wireless Standard Client Devices"; U.S. Patent 10,605,907 titled "Motion Detection by a Central Controller Using Beamforming Dynamic Information";U.S. Patent 10,600,314 titled "Modifying Sensitivity Settings in a Motion Detection System"; U.S. Patent 10,567,914 titled "Initializing Probability Vectors for Determining a Location of Motion Detected from Wireless Signals"; U.S. Patent 10,565,860 titled "Offline Tuning System for Detecting New Motion Zones in a Motion Detection System"; U.S. Patent 10,506,384 titled "Determining a Location of Motion Detected from Wireless Signals Based on Prior Probability"; U.S. Patent 10,499,364 titled "Identifying Static Leaf Nodes in a Motion Detection System"; U.S. Patent 10,498,467 titled "Classifying Static Leaf Nodes in a Motion Detection System"; U.S. Patent 10,460,581 titled "Determining a Confidence for a Motion Zone Identified as a Location of Motion for Motion Detected by Wireless Signals"; U.S. Patent 10,459,076 titled "Motion Detection based on Beamforming Dynamic Information"; U.S. Patent 10,459,074 titled "Determining a Location of Motion Detected from Wireless Signals Based on Wireless Link Counting"; U.S. Patent 10,438,468 titled "Motion Localization in a Wireless Mesh Network Based on MotionIndicator Values"U.S. Patent 10,404,387 titled "Determining Motion Zones in a Space Traversed by Wireless Signals"; U.S. Patent 10,393,866 titled "Detecting Presence Based on Wireless Signal Analysis"; U.S. Patent 10,380,856 titled "Motion Localization Based on Channel Response Characteristics"; U.S. Patent 10,318,890 titled "Training Data for a Motion Detection System using Data from a Sensor Device"; U.S. Patent 10,264,405 titled "Motion Detection in Mesh Networks"; U.S. Patent 10,228,439 titled "Motion Detection Based on Filtered Statistical Parameters of Wireless Signals"; U.S. Patent 10,129,853 titled "Operating a Motion Detection Channel in a Wireless Communication Network"; U.S. Patent 10,111,228 titled "Selecting Wireless Communication Channels Based on Signal Quality Metrics" and other technologies.;

[0030] Figure 1 FIG. 4 shows an exemplary wireless communication system 100. The wireless communication system 100 may perform one or more operations of a motion detection system. The technical improvements and advantages achieved by detecting motion using the wireless communication system 100 are also applicable to examples where the wireless communication system 100 is used for other wireless sensing applications.

[0031] The exemplary wireless communication system 100 includes three wireless communication devices - 102A, 102B, and 102C. The exemplary wireless communication system 100 may include additional wireless communication devices 102 and / or other components (e.g., one or more network servers, network routers, network switches, cables, or other communication links, etc.).

[0032] Exemplary wireless communication devices 102A, 102B, 102C may operate in a wireless network, for example, according to a wireless network standard or other types of wireless communication protocols. For example, the wireless network may be configured to operate as a wireless local area network (WLAN), a personal area network (PAN), a metropolitan area network (MAN), or other types of wireless networks. Examples of WLANs include networks configured to operate according to one or more standards in the 802.11 standard family developed by the IEEE (e.g., Wi-Fi networks), etc. Examples of PANs include networks that operate according to short-range communication standards (e.g., near-field communication (NFC), ZigBee), and millimeter-wave communication, etc.

[0033] In some implementations, wireless communication devices 102A, 102B, 102C may be configured to communicate in a cellular network, for example, according to cellular network standards. Examples of cellular networks include networks configured according to the following standards: 2G standards such as Global System for Mobile Communications (GSM) and Enhanced Data Rates for GSM Evolution (EDGE) or EGPRS; 3G standards such as Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Universal Mobile Telecommunications System (UMTS), and Time Division-Synchronous Code Division Multiple Access (TD-SCDMA); 4G standards such as Long-Term Evolution (LTE) and LTE-Advanced (LTE-A); and 5G standards, etc.

[0034] In some cases, one or more of the wireless communication devices 102 are Wi-Fi access points or other types of wireless access points (WAPs). In some cases, one or more of the wireless communication devices 102 are access points of a wireless mesh network (e.g., commercially available mesh network systems (e.g., GOOGLE Wi-Fi, EERO Mesh, etc.)). In some instances, one or more of the wireless communication devices 102 may be implemented as wireless access points (APs) in a mesh network, while other wireless communication devices 102 are implemented as leaf devices (e.g., mobile devices, smart devices, etc.) that access the mesh network through one of the APs. In some cases, one or more of the wireless communication devices 102 are mobile devices (e.g., smartphones, smartwatches, tablets, laptops, etc.), wireless-enabled devices (e.g., smart thermostats, Wi-Fi-enabled cameras, smart TVs), or other types of devices that communicate in a wireless network.

[0035] In Figure 1In the example shown, wireless communication devices send wireless signals to each other via a wireless communication link (e.g., according to a wireless network standard or a non-standard wireless communication protocol), and the wireless signals communicated between the devices can be used for motion detection to detect the motion of an object in the signal path between the devices. In some implementations, standard signals (e.g., channel sounding signals, beacon signals), non-standard reference signals, or other types of wireless signals can be used for motion detection.

[0036] In Figure 1 the example shown, the wireless communication link between wireless communication devices 102A and 102C can be used to detect a first motion detection area 110A, the wireless communication link between wireless communication devices 102B and 102C can be used to detect a second motion detection area 110B, and the wireless communication link between wireless communication devices 102A and 102B can be used to detect a third motion detection area 110C. In some instances, the motion detection area 110 can include, for example, air, solid materials, liquids, or other media through which wireless electromagnetic signals can propagate.

[0037] In Figure 1 the example shown, when an object moves in any motion detection area 110, the motion detection system can detect the motion based on the signal transmitted through the relevant motion detection area 110. Generally, the object can be any type of static or movable object, and can be living or inanimate. For example, the object can be a human (e.g., Figure 1 the person 106 shown in

[0038] ), an animal, an inorganic object, or other devices, equipment, or components, an object that defines all or part of the boundary of a space (e.g., a wall, a door, a window, etc.), or other types of objects. In some examples, the wireless signal can propagate through a structure (e.g., a wall) before or after interacting with a moving object, which can allow the detection of the motion of the object without an optical line of sight between the moving object and the transmission or reception hardware. In some instances, the motion detection system can communicate a motion detection event to other devices or systems, such as a security system or a control center, etc.

[0039] In some cases, the wireless communication device 102 itself is configured to perform one or more operations of the motion detection system, for example, by executing computer-readable instructions (e.g., software or firmware) on the wireless communication device. For example, each device can process the received wireless signal to detect motion based on changes in the communication channel. In some cases, other devices (e.g., a remote server, a cloud-based computer system, a network-attached device, etc.) are configured to perform one or more operations of the motion detection system. For example, each wireless communication device 102 can send channel information to a designated device, system, or service that performs the operations of the motion detection system.

[0040] In an example aspect of operation, the wireless communication devices 102A, 102B can broadcast a wireless signal to or address a wireless signal to another wireless communication device 102C, and the wireless communication device 102C (and potentially other devices) receives the wireless signal transmitted by the wireless communication devices 102A, 102B. Then, the wireless communication device 102C (or other system or device) processes the received wireless signal to detect the movement of an object in the space accessed by the wireless signal (e.g., in zones 110A, 110B). In some instances, the wireless communication device 102C (or other system or device) can perform one or more operations of a motion detection system.

[0041] Figure 2A and 2B FIG. is a diagram illustrating exemplary wireless signals communicated between wireless communication devices 204A, 204B, 204C. The wireless communication devices 204A, 204B, 204C can be, for example Figure 1 the wireless communication devices 102A, 102B, 102C shown in, or can be other types of wireless communication devices.

[0042] In some cases, a combination of one or more of the wireless communication devices 204A, 204B, 204C can be part of a motion detection system or can be used by a motion detection system. The exemplary wireless communication devices 204A, 204B, 204C can send wireless signals through space 200. The exemplary space 200 can be fully or partially enclosed or open at one or more boundaries of the space 200. The space 200 can be or can include the interior of a room, multiple rooms, a building, an indoor area, an outdoor area, etc. In the example shown, the first wall 202A, the second wall 202B, and the third wall 202C at least partially surround the space 200.

[0043] In Figure 2A and 2B the example shown, the first wireless communication device 204A repeatedly (e.g., periodically, intermittently, at scheduled, unscheduled, or random intervals, etc.) sends a wireless motion detection signal. The second communication device 204B and the third wireless communication device 204C receive signals based on the motion detection signal transmitted by the wireless communication device 204A.

[0044] As shown in the figure, Figure 2A the object is at the first position 214A at an initial time (t0), and Figure 2B the object has moved to the second position 214B at a subsequent time (t1). In Figure 2A and 2BIn this case, the moving object in the space 200 is represented as a person, but the moving object can be other types of objects. For example, the moving object can be an animal, an inorganic object (e.g., a system, a device, an equipment, or an assembly), an object for defining all or part of the boundary of the space 200 (e.g., a wall, a door, a window, etc.), or other types of objects. In Figure 2A and 2B In the illustrated example, the wireless communication devices 204A, 204B, 204C are stationary and thus are in the same position at the initial time t0 and the subsequent time t1. However, in other examples, one or more of the wireless communication devices 204A, 204B, 204C can be moving and can move between the initial time t0 and the subsequent time t1.

[0045] As Figure 2A and 2B shown, a plurality of exemplary paths of the wireless signals transmitted from the first wireless communication device 204A are shown by dashed lines. Along the first signal path 216, the wireless signal is transmitted from the first wireless communication device 204A and is reflected by the first wall 202A towards the second wireless communication device 204B. Along the second signal path 218, the wireless signal is transmitted from the first wireless communication device 204A and is reflected by the second wall 202B and the first wall 202A towards the third wireless communication device 204C. Along the third signal path 220, the wireless signal is transmitted from the first wireless communication device 204A and is reflected by the second wall 202B towards the third wireless communication device 204C. Along the fourth signal path 222, the wireless signal is transmitted from the first wireless communication device 204A and is reflected by the third wall 202C towards the second wireless communication device 204B.

[0046] In Figure 2A , along the fifth signal path 224A, the wireless signal is transmitted from the first wireless communication device 204A and is reflected by the object at the first position 214A towards the third wireless communication device 204C. Between the time t0 in Figure 2A and the time t1 in Figure 2B , the object moves from the first position 214A in the space 200 to the second position 214B (e.g., a certain distance away from the first position 214A). In Figure 2B , along the sixth signal path 224B, the wireless signal is transmitted from the first wireless communication device 204A and is reflected by the object at the second position 214B towards the third wireless communication device 204C. Since the object moves from the first position 214A to the second position 214B, the sixth signal path 224B depicted in Figure 2B is different from that in Figure 2Ais longer than the fifth signal path 224A depicted therein. In some examples, due to the movement of objects in space, signal paths can be added, removed, or otherwise modified.

[0047] Figure 2A and 2B The exemplary wireless signals shown can experience attenuation, frequency shift, phase shift, or other effects through their respective paths, and can have components that propagate in other directions, such as through portions of walls 202A, 202B, and 202C. In some examples, the wireless signal is a radio frequency (RF) signal. The wireless signal can include other types of signals.

[0048] The transmitted signal can have multiple frequency components within a frequency bandwidth, and the transmitted signal can include one or more frequency bands within the frequency bandwidth. The transmitted signal can be transmitted from the first wireless communication device 204A in an omnidirectional manner, in a directional manner, or otherwise. In the example shown, the wireless signal travels through multiple respective paths in space 200, and the signal along each path can become attenuated due to path loss, scattering, or reflection, etc., and can have a phase shift or a frequency shift.

[0049] As Figure 2A and 2B shown, the signals from the various paths 216, 218, 220, 222, 224A, and 224B are combined at the third wireless communication device 204C and the second wireless communication device 204B to form the received signal. Due to the effects of the multiple paths in space 200 on the transmitted signal, space 200 can be represented as a transfer function (e.g., a filter) that takes the transmitted signal as input and outputs the received signal. When an object moves in space 200, the attenuation or phase shift that affects the wireless signal along the signal path can change, and thus the transfer function of space 200 can change. In the case of transmitting the same wireless signal from the first wireless communication device 204A, if the transfer function of space 200 changes, the output of this transfer function (e.g., the received signal) can also change. The change in the received signal can be used to detect the movement of the object. Conversely, in some cases, if the transfer function of the space does not change, the output of the transfer function (the received signal) can remain unchanged.

[0050] Figure 2C is a diagram showing an exemplary wireless sensing system that operates to detect motion in space 201. Figure 2CThe exemplary space 201 shown in [Figure] includes a home that comprises multiple different spatial regions or zones. In the example shown, the wireless motion detection system uses a multi-AP home network topology (e.g., a mesh network or a self-organizing network (SON)), which includes three access points (APs): a central access point 226 and two extended access points 228A, 228B. In a typical multi-AP home network, each AP generally supports multiple frequency bands (2.4G, 5G, 6G), and multiple frequency bands can be enabled simultaneously. Each AP can use different Wi-Fi channels to serve its clients, as this can allow for better spectral efficiency.

[0051] In Figure 2C the example shown, the wireless communication network includes a central access point 226. In a multi-AP home Wi-Fi network, one AP can be designated as the central AP. This selection, typically managed by the manufacturer's software running on each AP, is typically the AP that has a wired Internet connection 236. The other APs 228A, 228B are wirelessly connected to the central AP 226 via respective wireless backhaul connections 230A, 230B. The central AP 226 can select a different wireless channel from the extended APs to serve its connected clients.

[0052] In Figure 2C the example shown, the extended APs 228A, 228B extend the range of the central AP 226 by allowing devices to connect to a potentially closer AP or a different channel. The end user does not need to know which AP the device has connected to, as all services and connections are generally the same. In addition to serving all connected clients, the extended APs 228A, 228B also use the wireless backhaul connections 230A, 230B to connect to the central AP 226 to enable network traffic to move between the other APs and provide a gateway to the Internet. Each extended AP 228A, 228B can select a different channel to serve its connected clients.

[0053] In Figure 2C the example shown, client devices (e.g., Wi-Fi client devices) 232A, 232B, 232C, 232D, 232E, 232F, 232G are associated with either the central AP 226 or one of the extended APs 228 via respective wireless links 234A, 234B, 234C, 234D, 234E, 234F, 234G. Client devices 232 connected to a multi-AP network can operate as leaf nodes in the multi-AP network. In some implementations, client devices 232 can include devices capable of wireless operation (e.g., mobile devices, smartphones, smartwatches, tablets, laptops, smart thermostats, cameras capable of wireless operation, smart TVs, speakers capable of wireless operation, power outlets capable of wireless operation, etc.).

[0054] When the client devices 232 seek to connect to and associate with their respective APs 226, 228, the client devices 232 may go through an authentication and association phase with their respective APs 226, 228. Additionally, the association phase assigns address information (e.g., an association ID or other type of unique identifier) to each client device 232. For example, within the IEEE 802.11 standard family for Wi-Fi, each client device 232 may use a unique address (e.g., a 48-bit address, an example being a MAC address) to identify itself, but the client devices 232 may use other types of identifiers embedded within one or more fields of a message to identify. The address information (e.g., MAC address or other type of unique identifier) may be hard-coded and fixed, or randomly generated according to network address rules at the start of the association process. Once the client devices 232 have associated with their respective APs 226, 228, their respective address information may remain fixed. Subsequently, transmissions by the APs 226, 228 or the client devices 232 typically include sending the address information (e.g., MAC address) of the wireless device and the address information (e.g., MAC address) of the receiving device.

[0055] In Figure 2C the example shown, the wireless backhaul connections 230A, 230B carry data between the APs and can also be used for motion detection. The wireless backhaul channels (or bands) may each be different from the channels (or bands) used to serve the connected Wi-Fi devices.

[0056] In Figure 2C the example shown, the wireless links 234A, 234B, 234C, 234D, 234E, 234F, 234G may include frequency channels used by the client devices 232A, 232B, 232C, 232D, 232E, 232F, 232G to communicate with their respective APs 226, 228. Each AP may independently select its own channel to serve its respective client devices, and the wireless links 234 may be used for data communication as well as motion detection.

[0057] A motion detection system may collect and process data (e.g., channel information) corresponding to local links participating in the operation of the wireless sensing system, and the motion detection system may include one or more motion detection or positioning processes running on one or more of the client devices 232 or on one or more of the APs 226, 228. The motion detection system may be installed as a software or firmware application on the client devices 232 or the APs 226, 228, or may be part of the operating system of the client devices 232 or the APs 226, 228.

[0058] In some implementations, APs 226, 228 do not include motion detection software and are not otherwise configured to perform motion detection in space 201. Instead, in such implementations, the operation of the motion detection system is performed on one or more client devices 232. In some implementations, channel information may be obtained by client device 232 by receiving a wireless signal from APs 226, 228 (or possibly from other client devices 232) and processing the wireless signal to obtain the channel information. For example, a motion detection system running on client device 232 may access channel information provided by the radio firmware of the client device (e.g., Wi-Fi radio firmware) such that the channel information can be collected and processed.

[0059] In some implementations, client device 232 sends a request to its corresponding APs 226, 228 to send a wireless signal that can be used by the client device for motion detection to detect the motion of an object in space 201. The request sent to the corresponding APs 226, 228 can be an empty data packet frame, a beamforming request, a ping, standard data traffic, or a combination thereof. In some implementations, client device 232 is stationary when performing motion detection in space 201. In other examples, one or more of client devices 232 may be mobile and may move within space 201 while performing motion detection.

[0060] Mathematically, the signal f(t) transmitted from a wireless communication device (e.g., Figure 2A and 2B the wireless communication device 204A in Figure 2C or APs 226, 228 in

[0061]

[0062] where ω , ,

[0060] , 2B , n,k ,

[0062] , n ,

[0063] , ,

[0061] , Figure 2A , n,k , , Figure 2C , k ,

[0064] , n , , , represents the frequency of the nth frequency component of the transmitted signal, c n represents the complex coefficient of the nth frequency component, and t represents time. In the case where the transmitted signal f(t) is transmitted, the output signal r k (t) from path k can be described according to Equation (2):

[0063]

[0064] where α n,k represents the attenuation factor (or channel response; e.g., due to scattering, reflection, and path loss) for the nth frequency component along path k, and φ n,krepresents the phase of the signal for the nth frequency component along path k. Then, the received signal R at the wireless communication device can be described as the sum of all output signals r k (t) from all paths to the wireless communication device, i.e., as shown in Equation (3):

[0065]

[0066] Substituting Equation (2) into Equation (3) gives the following Equation (4):

[0067]

[0068] Then, the received signal R at the wireless communication device (e.g., the wireless communication devices 204B, 204C in Figure 2A and 2B or the client device 232 in Figure 2C ) can be analyzed (e.g., using one or more motion detection algorithms) to detect motion. The received signal R at the wireless communication device can be transformed to the frequency domain, for example, using the Fast Fourier Transform (FFT) or other types of algorithms. The transformed signal can represent the received signal R as a series of n complex values, where each corresponding frequency component of the (n frequencies ω n ) corresponds to a complex value. For the frequency component of frequency ω n , the complex value Y n can be expressed as the following Equation (5):

[0069]

[0070] For a given frequency component ω n , the complex value Y n indicates the relative amplitude and phase shift of the received signal at that frequency component ω n . The transmitted signal f(t) can be repeated over a period of time, and the complex value Y n can be obtained for each transmitted signal f(t). When an object moves in space, the complex value Y n changes over the period of time due to the change in the channel response α n,k of the space. Therefore, the detected changes in the channel response (and thus the complex value Y n ) can indicate the movement of an object within the communication channel. Conversely, a stable channel response can indicate no motion. Therefore, in some implementations, the complex values Y n of each of multiple devices in the wireless network can be processed to detect whether motion has occurred in the space traversed by the transmitted signal f(t). The channel response can be expressed in the time domain or the frequency domain, and the Fourier transform or the inverse Fourier transform can be used to switch between the time-domain expression and the frequency-domain expression of the channel response.

[0071] In Figure 2A 、 2B and in another aspect of 2C, the beamforming state information can be used to detect whether there has been movement in the space through which the transmitted signal f(t) passes. For example, beamforming can be performed between devices based on some knowledge of the communication channel (e.g., the feedback attributes generated by the receiver), where the knowledge can be used to generate one or more steering attributes (e.g., a steering matrix), and the one or more steering attributes are applied by the transmitter device to shape the transmit beam / signal in a specific direction. In some instances, as described herein, a change in the steering or feedback attributes used in the beamforming process indicates a possible change in the space accessed by the wireless signal caused by a moving object. For example, movement can be detected by an obvious change in the communication channel over a certain period of time (e.g., as indicated by the channel response, or the steering or feedback attributes, or any combination thereof).

[0072] For example, in some implementations, a steering matrix can be generated at the transmitter device (beamformer) based on the feedback matrix provided by the receiver device (beamformee) based on channel sounding. Since the steering matrix and the feedback matrix are related to the propagation characteristics of the channel, these beamforming matrices change as the object moves within the channel. The changes in the channel characteristics are correspondingly reflected in these matrices, and by analyzing these matrices, movement can be detected, and different characteristics of the detected movement can be determined. In some implementations, a spatial map can be generated based on one or more beamforming matrices. The spatial map can indicate the general direction of the object in the space relative to the wireless communication device. In some cases, the "modes" of the beamforming matrix (e.g., the feedback matrix or the steering matrix) can be used to generate the spatial map. The spatial map can be used to detect the presence of movement in the space or the location of the detected movement.

[0073] In some implementations, the output of the motion detection system can be provided as a notification for graphical display on the user interface of the user device. In some implementations, the user device is the device used to detect motion, the user device of a caregiver or emergency contact assigned to an individual in spaces 200, 201, or any other user device communicatively coupled to the motion detection system to receive notifications from the motion detection system.

[0074] In some instances, the graphical display includes a graph line of motion data indicating the degree of motion detected by the motion detection system for each of a series of time points. The graphical display can show the relative degree of motion detected by the various nodes of the motion detection system. The graphical display can assist a user in determining an appropriate action to take in response to a motion detection event, correlating the motion detection event with the user's observations or knowledge, determining whether the motion detection event is true or false, and so on.

[0075] In some implementations, the output of the motion detection system can be provided in real time (e.g., to an end user). Additionally or alternatively, the output of the motion detection system can be stored (e.g., locally on the wireless communication device 204, client device 232, APs 226, 228 or on a cloud-based storage service) and analyzed to reveal statistics over a time frame (e.g., hours, days or months). Examples where the output of the motion detection system can be stored and analyzed to reveal statistics over a time frame are in health monitoring, vital signs monitoring, sleep monitoring, etc. In some implementations, an alert (e.g., a notification, an audio alert or a video alert) can be provided based on the output of the motion detection system. For example, a motion detection event can be communicated to other devices or systems (e.g., a security system or a control center), a designated caregiver or a designated emergency contact based on the output of the motion detection system.

[0076] In some implementations, a wireless motion detection system can detect motion by analyzing components of a wireless signal specified by a wireless communication standard. For example, a motion detection system can analyze the standard header of a wireless signal exchanged in a wireless communication network. One such example is the IEEE 802.11ax standard, which is also known as "Wi-Fi 6". The draft of the IEEE 802.11ax standard was published in the document titled "P802.11ax / D4.0, IEEE Draft Standard for Information Technology - Telecommunications and Information Exchange Between Systems Local and Metropolitan Area Networks - Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications Amendment Enhancements for High Efficiency WLAN" in March 2019, which is accessible at https: / / ieeexplore.ieee.org / document / 8672643 and is hereby incorporated by reference in its entirety. In some cases, the standard header specified by other types of wireless communication standards can be used for motion detection.

[0077] In some implementations, the motion detection algorithm used by a wireless motion detection system utilizes the channel response (the output of a channel estimation process) computed by a wireless receiver (e.g., a Wi-Fi receiver). For example, the channel response computed by a channel estimation process according to the Wi-Fi 6 standard can be received as an input to the motion detection algorithm. Channel estimation in the Wi-Fi 6 standard occurs at the PHY layer using the PHY frame (the PHY frame is also known as PPDU) of the received wireless signal.

[0078] In some examples, the motion detection algorithm employed by a wireless motion detection system uses a channel response calculated based on an orthogonal frequency division multiplexing (OFDM)-based PHY frame (including PHY frames generated by the Wi-Fi 6 standard). In some instances, the OFDM-based PHY frame can be a frequency domain signal with multiple fields, each field having a corresponding frequency domain signal. With such OFDM-based PHY frames, there are typically two types of PPDU fields that allow a Wi-Fi receiver to estimate the channel. The first is the legacy training field, and the second is the MIMO training field. Either or both of these fields can be used for motion detection. An example of a MIMO training field that can be used is the so-called "High Efficiency Long Training Field" (HE-LTF) of what is referred to as HE-PHY (e.g., in the Wi-Fi 6 standard according to the IEEE802.11ax standard).

[0079] Figure 3 An exemplary PHY frame 300 including the HE-LTF is shown. Figure 3 The exemplary PHY frame 300 shown in comes from the IEEE802.11ax standard. In some cases, these and other types of PHY frames including the HE-LTF can be used for motion detection. As Figure 3 shown, the exemplary PHY frame 300 includes multiple fields defined in the 802.11 standard: L-STF (Legacy Short Training Field), L-LTF (Legacy Long Training Field), L-SIG (Legacy Signal), RL-SIG (Repeated Legacy Signal), HE-SIG-A (High Efficiency Signal), HE-STF (High Efficiency Short Training Field), multiple HE-LTFs, Data, PE (Packet Extension). In some instances, the L-LTF field can be used to estimate the channel response that can be provided as an input to a motion detection algorithm. The HE-LTF field provided as a MIMO training field can also be used to estimate the channel response that can be provided as an input to a motion detection algorithm.

[0080] In Figure 3In the example shown, the HE-LTF can have a variable duration and bandwidth, and in some examples, the PHY frame 300 divides a 20 MHz channel into 256 frequency points (instead of 64 used in previous PHY frame versions). As such, the example HE-LTF in the PHY frame 300 can provide four times better frequency resolution (e.g., compared to earlier PHY frame versions), since each point represents a frequency bandwidth of 78.125 kHz rather than 312.5 kHz. In other words, the consecutive frequency points in the HE-LTF in the PHY frame 300 are closer together compared to the consecutive frequency points in the conventional PHY fields in the PHY frame 300. Additionally, the HE-LTF provides more consecutive subcarriers than other fields, so a wider continuous frequency bandwidth can be used for motion detection. For example, Table I (below) shows the continuous frequency bandwidth and frequency resolution of the HE-LTF field (labeled "HE" in the table) compared to conventional PHY fields (L-STF and L-LTF) and other MIMO training fields (e.g., High Throughput (HT) Long Training Field and Very High Throughput (VHT) Long Training Field).

[0081]

[0082] Table I

[0083] In some IEEE 802.11 standards, the PHY layer is divided into 2 sublayers: the PLCP sublayer (Physical Layer Convergence Procedure) and the PMD sublayer (PHY Medium Dependent). The PLCP sublayer (Physical Layer Convergence Procedure) obtains data from the MAC layer and converts it into the PHY frame format. The format of the PHY frame is also referred to as the PPDU (PLCP Protocol Data Unit). The PPDU can include fields for channel estimation. The PMD sublayer (PHY Medium Dependent) provides the modulation scheme for the PHY layer. Many different IEEE 802.11-based PHY frame formats are defined. In some examples, a wireless motion detection system uses information derived from OFDM-based PHY frames, such as those described in the following standard documents, for example: IEEE 802.11a-1999: Conventional OFDM PHY; IEEE 802.11n-2009: HT PHY (High Throughput); IEEE 802.11ac-2013: VHT PHY (Very High Throughput); IEEE 802.11ax (Draft 4.0, March 2019): HE PHY (High Efficiency).

[0084] Other types of PHY layer data can be used, and each PHY layer specification can provide its own PPDU format. For example, in some IEEE 802.11 standards, at the head is " <xxx>"PHY specification" ==> <xxx>"PHY" ==> <xxx>The PPDU format for the PHY layer specification can be found under the section "PPDU format". Figure 3 The exemplary PHY frame 300 shown is a HE PHY frame provided by the ODFM PHY layer of the example 802.11 standard.

[0085] In some IEEE 802.11 standards (e.g., IEEE 802.11a-1999), the OFDM PHY divides a 20 MHz channel into 64 frequency bins. Modulation and demodulation are done using 64-point complex inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT). In an exemplary modulation process: data bits are grouped (e.g., depending on the QAM constellation), each group of bits is assigned to one of the subcarriers (or frequency bins); depending on the QAM constellation, the group of bits is mapped to the complex numbers of each subcarrier; and a 64-point IFFT is performed to generate the complex time-domain I and Q waveforms for transmission. In an exemplary demodulation process: the received complex I and Q time-domain signals are received; a 64-point FFT is performed to calculate the complex numbers of each subcarrier; according to the QAM constellation, each subcarrier complex number is mapped to bits; and the bits from each subcarrier are recombined into data. In a typical modulation or demodulation process, not all 64 subcarriers are used; for example, only 52 subcarriers can be considered valid for data and pilots, and the remaining subcarriers can be considered empty. The PHY layer specifications in recently developed IEEE 802.11 standards utilize larger channel bandwidths (e.g., 40 MHz, 80 MHz, and 160 MHz).

[0086] Figure 4 An exemplary PHY frame 400 is shown that includes a very high throughput long training field (also known as "VHT-LTF"). Figure 4 The exemplary PHY frame 400 shown in FIG. is from the IEEE 802.11ac standard. The draft of the IEEE 802.11ac standard was published in December 2018 in a document entitled "802.11ac-2013-IEEE Standard for Information technology--Telecommunications and information exchange between systems—Local and metropolitan area networks--Specific requirements--Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications--Amendment 4: Enhancements for Very High Throughput for Operation in Bands below 6GHz", which is accessible at https: / / ieeexplore.ieee.org / document / 7797535 and is hereby incorporated by reference in its entirety. In some cases, these and other types of PHY frames, including the VHT-LTF, can be used for motion detection.

[0087] As Figure 4 shown, the exemplary PHY frame 400 includes a plurality of components defined in the 802.11 standard: L-STF, L-LTF, L-SIG, VHT-SIG-A (Very High Throughput Signal A), VHT-STF (Very High Throughput Short Training Field), VHT-LTF (Very High Throughput Long Training Field), VHT-SIG-B (Very High Throughput Signal B), data. The PPDU for legacy, HT, VHT, and HE PHYs starts with a legacy preamble that includes the L-STF and L-LTF, as Figure 4 shown in the exemplary PHY frame 400 in FIG.. In some cases, the L-LTF can be used for channel estimation. The VHT-LTF, which has a wider bandwidth and contains similar information compared to the L-LTF, can be used for MIMO channel estimation. The HT-LTF and VHT-LTF are very similar, except that the VHT-LTF allows for higher-order MIMO and allows for 80 MHz and 160 MHz channels. These fields are beneficial for motion detection because they can provide a wider continuous frequency bandwidth and MIMO channel information. With MIMO channel estimation, typically Nr×Nc channel responses are calculated. This provides more information for motion detection. Figure 5 An exemplary MIMO device configuration 500 is shown that includes a transmitter having Nr antennas and a receiver having Nc antennas. In Figure 5 the example, Nr×Nc channel responses can be calculated based on the HE-LTF or the VHT-LTF or another MIMO training component of the PHY frame.

[0088] In some implementations, a wireless communication device calculates the channel response, for example, by performing channel estimation processing based on the PHY frame. For example, the wireless communication device can perform channel estimation based on the exemplary PHY frame 300 shown in Figure 3 , the exemplary PHY frame 400 shown in Figure 4 , or other types of PHY frames from the wireless signal.

[0089] In some instances, the channel information for motion detection can include the channel response generated by channel estimation based on the L-LTF in the PHY frame. The L-LTF in the 802-11ax standard can be equivalent to the LTF in the IEEE 802.11a-1999 standard. The L-LTF can be provided in the frequency domain as the input to a 64-point IFFT. Generally, only 52 of the 64 points are considered valid points for channel estimation; and the remaining points (points [-32, -26) and (26, 31]) are zero. As described in the IEEE 802.11a-1999 standard, the L-LTF can be a long OFDM training symbol that includes 53 subcarriers (including a zero value at DC), which is modulated by an element sequence L given by:

[0090] L -26，26 ={1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, -1, 1, -1, 1, 1, 1, 0, 1, -1, -1, 1, 1, -1, 1, -1, 1, -1, -1, -1, -1, -1, 1, 1, -1, -1, 1, -1, 1, -1, 1, 1, 1, 1}

[0091] The exemplary "L" vector shown above represents the complex frequency domain representation of the field at baseband (centered at DC) and is described on page 13 of the draft of the IEEE 802.11a-1999 standard. The draft of the IEEE 802.11a-1999 standard is published in the document titled "802.11a-1999 - IEEE Standard for Telecommunications and Information Exchange between Systems - LAN / MAN Specific Requirements - Part 11: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications: High Speed Physical Layer in the 5GHz Band" and is accessible at https: / / ieeexplore.ieee.org / document / 815305. The exemplary "L" vector is considered "legacy" as it is part of the original OFDM PHY specification and is considered part of the legacy preamble. Thus, in later specification versions, it is referred to as the L-LTF (for legacy long training field).

[0092] In some instances, the channel information for motion detection can include a channel response generated by channel estimation based on one or more than one MIMO training field in a PHY frame (e.g., HE-LTF, HT-LTF, or VHT-LTF fields). The HE-LTF can be provided as the input to a 256-point IFFT in the frequency domain. With a typical HE-LTF, there are 241 valid points (e.g., as opposed to 52 in the legacy case). Each point in the HE-LTF represents a frequency range of 78.125 kHz, while each legacy point represents a larger frequency range of 312.5 kHz. Thus, the HE-LTF can provide higher frequency resolution, more frequency domain data points, and a larger frequency bandwidth, which can provide more accurate and higher resolution (e.g., higher time and space resolution) motion detection. An exemplary HE-LTF is described on page 561 of the draft of the IEEE 802.11ax standard as follows:

[0093] In a 20MHz transmission, the 4x HE-LTF transmitted on subcarriers [-122:122] is given by Equation (27-42).

[0094]

[0095] In some instances, the channel response can be estimated at the receiver device by performing an FFT on the received time-domain sequence (e.g., the exemplary L-LTF and HE-LTF sequences shown above), and dividing by the expected result [CH(N) = RX(N) / L(N)]. Figure 6 The 64-point FFT bin 600 in the top of Figure 6 shows the resulting spectrum that can be measured from the L-LTF in the exemplary PHY frame (20MHz and 40MHz channels). Figure 6 The 128-point FFT bin 650 in the bottom of Figure 6 shows the resulting spectrum that can be measured from the HE-LTF for the same 20MHz channel in the exemplary PHY frame. As Figure 6 shown, the HE-LTF can provide higher frequency resolution (more points in the same frequency bandwidth), a higher number of frequency-domain data points (more bins), and a larger frequency bandwidth.

[0096] Figure 7 is a diagram showing an example signal path in a wireless communication system 700. Figure 7 The example wireless communication system 700 shown in Figure 7 includes wireless communication devices 702A, 702B. The wireless communication devices 702A, 702B can be, for example Figure 1 the wireless communication devices 102A, 102B shown in Figure 1 , Figure 2A and 2B the wireless communication devices 204A, 204B, 204C shown in 2B , Figure 2C the devices 226, 228, 232 shown in Figure 2C , or they can be other types of wireless communication devices. The wireless communication system 700 operates in an environment including two scatterers 710A, 710B. The wireless communication system 700 and its environment can include additional or different features.

[0097] In Figure 7 the example shown in Figure 7 , the wireless communication device 702A transmits a radio frequency (RF) wireless signal, and the wireless communication device 702B receives the wireless signal. In the environment between the wireless communication devices 702A, 702B, the wireless signal interacts with the scatterers 710A, 710B. The scatterers 710A, 710B can be any type of physical object or medium that scatters radio frequency signals, e.g., part of a structure, furniture, a living body, etc. Each wireless signal can include, for example, a PHY frame that includes traditional PHY fields and one or more MIMO training fields (e.g., HE-LTF, VHT-LTF, HT-LTF) that can be used for motion detection.

[0098] In Figure 7 In the example shown, the wireless signal passes through the direct signal path 704A and two indirect signal paths 704B, 704C. The wireless signal from the wireless communication device 702A along the signal path 704B is reflected from the scatterer 710A before reaching the wireless communication device 702B. The wireless signal from the wireless communication device 702A along the signal path 704C is reflected from the scatterer 710B before reaching the wireless communication device 702B.

[0099] The propagation environment represented by the signal paths shown in Figure 7 can be described as a time-domain filter. For example, Figure 7 the characteristic response or impulse response of the propagation environment shown in

[0100]

[0101] Here, the integer k indexes the three signal paths, and the coefficient α k is a complex phasor representing the magnitude and phase of the scattering along each signal path. The value of the coefficient α k is determined by the physical characteristics of the environment, such as free-space propagation and the type of scattering objects present. In some examples, the increased attenuation along a signal path (e.g., through an absorbing medium such as a human body or others) can generally reduce the magnitude of the corresponding coefficient α k . Similarly, a human body or another medium acting as a scatterer can change the magnitude and phase of the coefficient α k .

[0102] Figure 8 is a graph 800 showing an example filter representation of the propagation environment. Specifically, Figure 8 the graph 800 in Figure 8 shows the time-domain representation of the filter h(t) in the above equation (6). The horizontal axis of the graph 800 represents time, and the vertical axis represents the value of the filter h(t). As Figure 8 shown, the filter can be described by three pulses distributed on the time axis (at times τ1, τ2, and τ3). In this example, the pulse at time τ1 represents the impulse response corresponding to Figure 7 the signal path 704A in Figure 7 , the pulse at time τ2 represents the impulse response corresponding to Figure 7 the signal path 704B in Figure 8 , and the pulse at time τ3 represents the impulse response corresponding to Figure 7 the signal path 704C in Figure 8 . The magnitude of each pulse in Figure 8 represents the magnitude of the corresponding coefficient α k for each signal path.

[0103] The time-domain representation of the filter can have additional or different pulses or other features. The number of pulses, as well as their corresponding positions on the time axis and their corresponding amplitudes, can vary according to the scattering distribution of the environment. For example, if an object appears towards the end of the coverage area (e.g., at scatterer 710B), this may cause the third pulse (at time τ3) to shift left or right. Generally, the first pulse (at time τ1) represents the earliest pulse or the direct line of sight in most systems; thus, if an object enters the line of sight between the transmitter and the receiver, this pulse will be affected. In some instances, the distance and direction of motion (relative to the transmitter and receiver) in the propagation environment can be inferred by looking at the behavior of these pulses over time. As an example, in some instances, an object moving towards the line of sight may affect the third, second, and first pulses in the order of the third pulse, second pulse, and first pulse, while an object moving away from the line of sight may affect the pulses in the reverse order.

[0104] The Fourier transform of the filter h(t) from equation (6) provides the frequency representation of the filter:

[0105]

[0106] In the frequency representation shown in equation (7), the individual pulses from equation (6) have been converted to complex exponentials (sine waves and cosine waves). The individual components of the exponentials in the frequency domain have specific rotational frequencies, which are given by the relevant pulse times τ k given.

[0107] In some implementations, when a wireless communication device (e.g., a WiFi transceiver) receives a wireless signal, the wireless communication device obtains the frequency-domain representation from the PHY frame of the wireless signal, which can be in the form of equation (7) or otherwise. In some instances, a motion detection system can convert the frequency-domain representation to a time-domain representation, which can be in the form of equation (6) or otherwise. Then, the motion detection system can make inferences about the motion (e.g., near / far, line-of-sight / non-line-of-sight motion) in the propagation environment based on the time-domain representation.

[0108] In some implementations, a motion detection system uses channel responses estimated based on traditional PHY fields (e.g., L-STF, L-LTF) and channel responses estimated based on MIMO training fields (e.g., HE-LTF, VHT-LTF, HT-LTF) to make inferences about motion in the propagation environment. In some instances, the differences in the continuous frequency bandwidth and frequency resolution between MIMO training fields and traditional PHY fields can be used to detect motion in space with varying granularity. For example, the time-domain channel response estimated based on traditional PHY fields (e.g., referred to as the traditional-PHY-based channel response) can be used to make macro-level determinations about whether motion has occurred in the propagation environment, while the time-domain channel response estimated based on MIMO training fields (e.g., referred to as the MIMO-field-based channel response) can be used to make more fine-grained determinations of the motion. As an example, the MIMO-field-based channel response can be used to make inferences about the location of motion in the propagation environment, the direction of motion in the propagation environment, or both. The MIMO-field-based channel response can be referred to as the HE-LTF-based channel response, the HT-LTF-based channel response, or the VHT-LTF-based channel response, depending on which MIMO training field is used to estimate the channel response.

[0109] As an illustration, Figure 8 An example time window 802 of the traditional-PHY-based channel response and an example time window 804 of the MIMO-field-based channel response are shown. The continuous frequency bandwidth of each MIMO training field (e.g., HE-LTF, VHT-LTF, HT-LTF) is greater than the continuous frequency bandwidth of each traditional PHY field (e.g., as shown in Table I above). Additionally, the frequency resolution of each MIMO training field is finer (e.g., higher) than the frequency resolution of each traditional PHY field (e.g., as shown in Table I above). Thus, at least based on the duality between the time domain and the frequency domain, the MIMO-field-based channel response has a finer time resolution over a larger time window compared to the traditional-PHY-based channel response. In other words, the time points in the MIMO-field-based channel response are closer together compared to the time points in the traditional-PHY-based channel response, and the MIMO-field-based channel response extends over a longer time period compared to the traditional-PHY-based channel response.

[0110] Since the duration of time window 804 is greater than the duration of time window 802, the channel response based on the MIMO field can detect the third pulse (at time τ3) without aliasing artifacts, thus accurately revealing the existence of the indirect signal path 704C and the scatterer 710B in the propagation environment. In addition, since the continuous frequency bandwidth of the MIMO training field is greater than the continuous frequency bandwidth of the traditional PHY field, the channel response based on the MIMO field has a finer (e.g., higher) time resolution than the channel response based on the traditional PHY. Since the channel response based on the MIMO field has a finer (e.g., higher) time resolution, a shift of any of the pulses in the channel response to the left or right (e.g., caused by movement along the direct path 704A or the indirect paths 704B, 704C) can be detected without aliasing artifacts. For example, movement at the scatterer 710B (e.g., caused by movement of the scatterer 710B or an object near the scatterer 710B) can shift the third pulse (at time τ3) to the left or right. The finer (e.g., higher) time resolution of the channel response based on the MIMO field can detect the shift in the third pulse (at time τ3) without aliasing artifacts, thus allowing inference that movement has occurred at the location of the scatterer 710B.

[0111] In addition, in some instances, the direction of movement in the propagation environment (relative to the transmitter and receiver) can be inferred by determining the change in the pulses of the channel response over time. For example, Figure 9A , 9B FIGS. 9C illustrate graphs 900, 902, 904 that show example changes in the filter representation over time. Graph 900 shows the filter representation for a first time period, graph 902 shows the filter representation for a second time period, and graph 904 shows the filter representation for a third time period. The horizontal axis of graphs 900, 902, 904 represents time, and the vertical axis represents the value of the filter. A comparison of graph 902 with graph 900 shows that the third pulse has undergone a shift to the right and a change in its amplitude, while the first pulse (at time τ1) and the second pulse (at time τ2) are substantially undisturbed. A comparison of graph 904 with graph 902 shows that the second pulse has undergone a shift to the left, the first pulse (at time τ1) is undisturbed, and the third pulse has returned to time τ3. The finer (e.g., higher) time resolution of the channel response based on the MIMO field can detect the shift in the third pulse (at time τ3) without aliasing artifacts, thus allowing inference that an object has started moving at the scatterer 710B and is moving from the scatterer 710B towards the scatterer 710A.

[0112] Although the channel response based on traditional PHY has a smaller time window and coarser (e.g., lower) time resolution than the channel response based on the MIMO field, the channel response based on traditional PHY can be used for macro-level determination of whether motion has occurred in the propagation environment. For example, motion can be detected by identifying substantial changes over time in the coefficients and pulse times of the channel response based on traditional PHY.

[0113] Figure 10 are a series of plots 1000A, 1000B, 1000C showing the relationship between the transmitted signal and the received signal in a wireless communication system. The first plot 1000A represents an example of the transmitted OFDM signal 1002 in the frequency domain. The transmitted OFDM signal includes multiple subcarriers at different frequencies within the Figure 10 bandwidth shown.

[0114] Figure 10 The OFDM signal 1002 shown in Figure 3 and 4 may include a PHY frame that includes one or more MIMO training fields. For example, the OFDM signal 1002 may include

[0115] any example MIMO training fields or other types of MIMO training fields shown in Figure 7 and Figure 10 In some instances, one or more MIMO training fields in the OFDM signal 1002 can be used for motion detection.

[0115] The transmitted OFDM signal 1002 is transmitted through the propagation environment (e.g., from Figure 7 the wireless communication device 702A in Figure 10 to the wireless communication device 702B), and the propagation environment can be represented as the channel between the transmitter and the receiver. Figure 10 The second plot 1000B in

[0116] shows an example frequency domain representation of the channel 1012. The channel can be represented as a superposition of sine waves, for example, as shown in equation (7) above.

[0116] The propagation environment transforms the transmitted OFDM signal 1002 and its components (e.g., MIMO training fields and other components) to form the received OFDM signal 1022. The effect of the propagation environment on the wireless signal can be represented as the channel multiplied by the signal (both in the frequency domain), which results in the received OFDM signal at the receiver. The third plot 1000C represents the received OFDM signal 1022 in the frequency domain. Thus, the received OFDM signal 1022 represents the transmitted OFDM signal 902 modified by the channel 1012.

[0117] As Figure 10 As shown, the system samples only a portion 1014 of channel 1012. This portion 1014 of channel 1012 may be referred to as the channel response or (e.g., in the WiFi standard and related literature) as channel state information (CSI), which may be isolated during a sounding process in some systems. In some cases, the CSI may be converted to a time-domain physical response (e.g., as represented in equation (6)) to provide a time-domain representation of the channel (e.g., the set of τ k and α k values). The measured portion of the channel response may be in a contiguous or non-contiguous spectral region. For example, the WiFi 6 standard (IEEE 802.11ax) includes two or more separate spectral regions (e.g., as Figure 6 shown).

[0118] Figure 11 FIG. 1100 is a plot showing example channel and signal information in a wireless communication system. In plot 1100, the horizontal axis represents the frequency domain, and the vertical axis represents the values of channel 1102 and the frequency bands 1104A, 1104B of the received wireless signal. As Figure 11 shown, the received signal includes a first frequency band 1104A and a second frequency band 1104B that jointly extend over a bandwidth. The first frequency band 1104A covers a first frequency domain and samples a corresponding first portion of channel 1102, and the second frequency band 1104B covers a second different frequency domain and samples a corresponding second different portion of channel 1102.

[0119] In some cases, a time-domain representation of channel 1102 sampled by the frequency bands 1104A, 1104B of the received wireless signal may be constructed (e.g., by applying a fast Fourier transform to the frequency-domain representation). The time-domain representation may include, for example, multiple pulses at time τ Figure 8 represented in the format of equation (6) and k . The minimum time interval between the pulses (between the values of τ k ) is typically given by τ min = 1 / B. Thus, the time resolution of the time-domain representation is a function of the total bandwidth B of the transmitted wireless signal. Therefore, a wireless communication standard that extends the total bandwidth of the wireless signal can also reduce (i.e., improve) the minimum interval between the pulses in the time-domain representation, resulting in a finer-grained physical model of the propagation environment. For example, more accurate motion inference can be made based on channel variations due to the separation of two closely spaced paths.

[0120] In some implementations, an optimization process may be used to transform any number of sampled frequency bands and convert them to a pulse-based model (e.g., the time-domain representation shown in equation (6)). This process can be formulated as the following optimization problem:

[0121]

[0122] The minimization problem in equation (8) seeks to identify K paths through the channel (where τ k is the delay of path K) such that the resulting time-domain impulse response matches the observed channel frequency response. The minimization operator seeks to minimize the difference between (1) the frequency response using a certain set of τ′ k s and (2) the measured frequency response. Generally, any suitable optimization method can be used to minimize the difference using a set of τ′ k s. Once the optimization is complete, the output is a set of τ′ k s values. These values are the pulse delays that allow the best match between the time-domain response and the observed frequency-domain response.

[0123] Therefore, solving the optimization problem in equation (8) corresponds to finding the pulse times that will minimize the residuals of this set of equations for all frequencies at which the channel response has been sampled. This is a non-linear optimization problem because these equations are non-linear functions of the pulse times although they are linear functions of the coefficients. In some cases, this optimization problem is solved, for example, by an iterative greedy process (such as successive least squares, etc.). For example, the matrix equation can be formulated as follows:

[0124]

[0125] Here, the matrix is created by scanning the values of the pulse times on the rows and the values of the frequencies on the columns. In this case, the value f l represents the vector of all frequencies at which the channel response has been observed for a given signal. The columns that are most correlated with the output can be selected from the matrix, and the corresponding coefficients α k can be found. Then the result can be subtracted from the output H(f l ), and this process can be repeated to provide K columns and K coefficients (each corresponding to a pulse at time τ k ). In some cases, the value of K can be estimated a priori based on the dynamic range of the radio receiver and thus the amount of noise in the CSI estimate. In some cases, the value of K can be estimated based on a study of the indoor environment (which limits the number of different pulses that can be observed in a typical environment). For example, free space propagation loss combined with a limited radio dynamic range can limit the number of pulses observable by the radio to less than ten in some environments. In such an environment, the successive least squares operation can be iterated until some other predetermined small integer number of values of the pulse times τ k and the coefficients α k have been extracted. In some cases, some of the values can be zero or negligibly small.

[0126] Figure 12 is a schematic diagram of an example signal processing system 1200 for a motion detection system. The example signal processing system 1200 can implement an algorithm state machine that performs the above-described optimization processing. The example signal processing system 1200 includes a channel (h(t)) estimator block 1202, a delay (z -1 ) operator 1204, a Fourier transform block 1206, a model-based threshold 1208, a latch 1210, a coefficient tracker 1212, and a motion inference engine 1214. The signal processing system 1200 can include additional or different features and can operate as shown or in another manner. Figure 12 as shown or in another manner.

[0127] As Figure 12 shown, a first frequency response H1(f) and a second frequency response H2(f) are received at a set of frequencies (e.g., at a wireless communication device). The first frequency response H1(f) can be obtained based on a frequency-domain signal included in a conventional PHY field of a wireless signal, and the second frequency response H2(f) can be obtained based on a frequency-domain signal included in a MIMO training field (e.g., HE-LTF, VHT-LTF, HT-LTF) of the received wireless signal. In some cases, a wireless communication device that receives a wireless signal uses a conventional PHY and a MIMO training field to calculate the amplitude and phase of each individual subcarrier obtained as a result of a wireless transmission. The amplitude and phase of each subcarrier can be represented as complex values H1(f) and H2(f) over a frequency range and frequency ranges, where each complex value corresponds to a single symbol or subcarrier in the training field.

[0128] Each frequency response H1(f) and H2(f) is provided to the h(t) estimator block 1202, and the h(t) estimator block 1202 generates corresponding time-domain channel estimates h1(t) and h2(t). In some implementations, the h(t) estimator block 1202 generates the time-domain channel estimates h1(t) and h2(t) based on the optimization expressed in Equation (8). Each time-domain channel estimate h1(t) and h2(t) can be represented as a pulse having a coefficient α at a pulse time τ k (e.g., as shown by the graph line 800 in k ). The final calculation of the pulse is held by the latch 1210, and the latch 1210 is controlled by the z Figure 8 operator. In the example signal processing system 1200 shown in -1 , the latch 1210 is used to hold the time-domain signature of the channel, and the z Figure 12 operator represents a one-sample delay. In general, a z -1 operator can be used to hold the Nth past value, and the integer N can be selected to control how quickly the dynamics of a changing environment are adjusted. -1 operator can be used to hold the Nth past value, and the integer N can be selected to control how quickly the dynamics of a changing environment are adjusted.

[0129] Then, the Fourier transform block 1206 transforms the estimated time-domain representations h1(t) and h2(t) into the corresponding estimated frequency-domain representations by applying the Fourier transform. And Then, an error value is calculated based on the difference between the estimated frequency-domain representation and the corresponding received frequency response. For example, the error value between the received frequency response H1(f) and the estimated frequency-domain representation can be calculated based on the difference between and H1(f). Similarly, the error value between the received frequency response H2(f) and the estimated frequency-domain representation can be calculated based on the difference between and H2(f). This processing loop can be iterated until the error value has been reduced to a sufficiently low value.

[0130] The error value is provided to the model-based threshold block 1208. The threshold block 1208 (e.g., based on the radio dynamic range, free space propagation loss, and potentially other factors) determines what is an appropriate threshold for the baseline model to which the system has converged to the pulse time τ k Once converged, the threshold detector closes the latch 1210 (e.g., by outputting a certain value that causes the latch 1210 to close), which allows the detected coefficients and the associated pulse time τ k to move to the coefficient tracking block 1212. The coefficient tracking block 1212 obtains the estimated frequency-domain representations H1(f) and H2(f) at each time step and recalculates the coefficient α k to ensure that the channel model is tracked closely enough. In some implementations, when large-scale changes occur in the propagation environment, the error loop is triggered again for the refreshed calculation of the coefficient α k and the pulse time τ k and then propagated to the coefficient tracker 1212. The output representing the reflected pulse and the output of the corresponding complex multiplier coefficient tracker 1212 are provided to the motion inference engine 1214 to detect motion characteristics. For example, the motion inference engine 1214 can identify the motion of an object in space by analyzing the changes in the coefficient α k and the pulse time τ k over time. As discussed above, the change in the channel response based on the traditional PHY can be used to make a macro-level determination of whether motion has occurred, while the change in the channel response based on the MIMO field can be used to make a finer-grained determination of motion (e.g., the position of the motion, the direction of the motion, or both).

[0131] As Figure 12 shown, the latch 1210 serves as a computational tool to store the coefficient α k and the associated pulse time τ k The last calculated value of τ is used to drive the error loop. In some aspects of operation, each time a new frequency response is received, the previous set of pulses (stored in latch 1210) is converted to a frequency response via Fourier transform (at 1216) and compared to the newly arrived channel response. This loop can ensure that large-scale changes in the channel are handled by continuously adapting the pulse time calculator. For example, when a macroscopic change occurs in the environment, a new response that better matches the new environment can be calculated. If no significant change in the response is detected, the same set of τ is continued to be used. k When a significant change is detected, a new set of τ is calculated. k In some cases, the coefficient α k and the associated pulse time τ k The calculation is better than just the coefficient α k Therefore, the example signal processing system 1200 is programmed to calculate the pulse time τ only when a significant change is detected. k , and calculate the coefficient α of each new signal received k This can help track the power of the received multipath over time and reveal information such as whether a path has been blocked.

[0132] Figure 13 is a flow chart illustrating a motion detection process 1300. The process 1300 may include additional or different operations, and Figure 13 The operations shown in can be performed in the order shown or in another order. In some cases, Figure 13 One or more operations shown in are implemented as processes that include multiple operations, sub-processes for other types of routines. In some cases, the operations may be combined, performed in another order, performed in parallel, iterated or otherwise repeated, or performed in another manner. Process 1300 may be performed by Figure 1 The example wireless communication devices 102A, 102B, 102C shown in FIG. Figure 2A and 2B The example wireless communication devices 204A, 204B, 204C shown in FIG. Figure 2C The process may be performed by any of the example devices shown in (e.g., client device 232) or by other types of devices.

[0133] At 1302, a wireless signal transmitted through a space (e.g., spaces 200, 201) during a period of time is received. The wireless signal may be transmitted through a space (e.g., spaces 200, 201) during a period of time. Figure 1 The example wireless communication devices 102A, 102B, 102C shown in FIG. Figure 2A and 2B between the example wireless communication devices 204A, 204B, 204C shown in, and Figure 2C between any of the example devices 226, 228, 232 shown in, or between other types of wireless communication devices.

[0134] Each wireless signal can be formatted according to a wireless communication standard. In some instances, the wireless signal can be formatted according to the IEEE 802.11 standard and can include PHY frames, examples being Figure 3 the example PHY frame 300 shown in, Figure 4 the example PHY frame 400 shown in, or other types of PHY frames from the wireless signal.

[0135] At 1304, legacy PHY fields (e.g., L-STF and L-LTF) and MIMO training fields (e.g., HE-LTF, HT-LTF, or VHT-LTF) are identified in the PHY frames of the respective wireless signals. A first frequency-domain signal (e.g., H1(f)) can be included in the legacy PHY field, and a second frequency-domain signal (e.g., H2(f)) can be included in the MIMO training field. At 1306, a first time-domain channel estimate (e.g., h1(t)) is generated based on the first frequency-domain signal, and at 1308, a second time-domain channel estimate (e.g., h2(t)) is generated based on the second frequency-domain signal. Since the continuous frequency bandwidth of the MIMO training field is greater than the continuous frequency bandwidth of the legacy PHY field, the time resolution of the first time-domain channel estimate is coarser than the time resolution of the second time-domain channel estimate. In other words, the time resolution of the first time-domain channel estimate is lower than the time resolution of the second time-domain channel estimate.

[0136] At 1310, it is determined whether motion has occurred in the space based on the first time-domain channel estimate (e.g., h1(t)). In some instances, the determination at 1310 is a macro-level indication of whether motion has occurred in the space. At 1312, the position of the motion within the space is determined based on the second time-domain channel estimate (e.g., h2(t)). In some instances, the determination at 1312 is more fine-grained motion data that allows a motion detection system to locate the motion within the space, and in some instances, the direction of the motion within the space is determined (e.g., as Figure 9A , Figure 9B , Figure 9C shown).

[0137] Figure 14 is a block diagram showing an exemplary wireless communication device 1400. As Figure 14 shown, the exemplary wireless communication device 1400 includes an interface 1430, a processor 1410, a memory 1420, and a power supply unit 1440. The wireless communication device (e.g., Figure 1 The wireless communication devices 102A, 102B, 102C) may include additional or different components, and the wireless communication device 1400 may be configured to operate as described with respect to the above examples. In some implementations, the interface 1430, the processor 1410, the memory 1420, and the power supply unit 1440 of the wireless communication device are housed together in a common housing or other assembly. In some implementations, one or more of the components of the wireless communication device may be housed separately, for example, in a separate housing or other assembly.

[0138] The exemplary interface 1430 may communicate (receive, transmit, or both) wireless signals. For example, the interface 1430 may be configured to communicate radio frequency (RF) signals formatted according to wireless communication standards (e.g., Wi-Fi, 4G, 5G, Bluetooth, etc.). In some implementations, the exemplary interface 1430 includes a radio electronic system and a baseband subsystem. The radio electronic system may include, for example, one or more antennas and radio frequency circuitry. The radio electronic system may be configured to communicate radio frequency wireless signals over a wireless communication channel. As an example, the radio electronic system may include a radio chip, an RF front end, and one or more antennas. The baseband subsystem may include, for example, digital electronics configured to process digital baseband data. In some cases, the baseband subsystem may include a digital signal processor (DSP) device or other type of processor device. In some cases, the baseband system includes digital processing logic to operate the radio electronic system, communicate wireless network traffic through the radio electronic system, or perform other types of processing.

[0139] The exemplary processor 1410 may, for example, execute instructions to generate output data based on data input. The instructions may include programs, code, scripts, modules, or other types of data stored in the memory 1420. Additionally or alternatively, the instructions may be encoded as pre-programmed or reprogrammable logic circuits, logic gates, or other types of hardware or firmware components or modules. The processor 1410 may be or include a general-purpose microprocessor, a dedicated co-processor, or other type of data processing device. In some cases, the processor 1410 performs the high-level operations of the wireless communication device 600. For example, the processor 1410 may be configured to execute or interpret software, scripts, programs, functions, executable instructions, or other instructions stored in the memory 1420. In some implementations, the processor 1410 is included in the interface 1430 or other components of the wireless communication device 1400.

[0140] Exemplary memory 1420 may include a computer-readable storage medium, such as a volatile memory device, a non-volatile memory device, or a combination of both. Memory 1420 may include one or more read-only memory devices, random access memory devices, buffer memory devices, or a combination of these and other types of memory devices. In some instances, one or more components of the memory may be integrated with or otherwise associated with other components of wireless communication device 1400. Memory 1420 may store instructions executable by processor 1410. For example, the instructions may include instructions for performing one or more of the operations described above.

[0141] Exemplary power supply unit 1440 supplies power to other components of wireless communication device 1400. For example, the other components may operate based on the power supplied by power supply unit 1440 via a voltage bus or other connection. In some implementations, power supply unit 1440 includes a battery or battery system, such as a rechargeable battery. In some implementations, power supply unit 1440 includes an adapter (e.g., an AC adapter) that receives an external power signal (from an external source) and converts the external power signal into an internal power signal that is regulated for the components of wireless communication device 1400. Power supply unit 1420 may include other components or operate in other ways.

[0142] Some of the subject matter and operations described in this specification may be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in a combination of one or more of the structures. Some of the subject matter described in this specification may be implemented as one or more computer programs (i.e., one or more modules of computer program instructions), encoded on a computer storage medium for execution by, or to control the operation of, a data processing apparatus. A computer storage medium may be a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them, or may be included in them. Further, although a computer storage medium is not a propagated signal, a computer storage medium may be the source or destination of computer program instructions encoded in an artificially generated propagated signal. A computer storage medium may also be one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices), or may be included in them.

[0143] Some of the operations described in this specification may be implemented as operations performed by a data processing apparatus on data stored on one or more computer-readable storage devices or received from other sources.

[0144] The term "data processing apparatus" encompasses all kinds of devices, apparatuses, and machines for processing data, including, by way of example, programmable processors, computers, system-on-chips, or multiple or combinations of the foregoing. The apparatus may include dedicated logic circuitry, such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit). In addition to hardware, the apparatus may also include code that creates an execution environment for the computer program being considered, such as code for constituting processor firmware, protocol stacks, database management systems, operating systems, cross-platform runtime environments, virtual machines, or combinations of one or more of them.

[0145] A computer program (also known as a program, software, software application, script, or code) may be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, etc., and it may be deployed in any form, including being deployed as a stand-alone program or being deployed as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. The program may be stored in a portion of a file that holds other programs or data (such as one or more scripts stored in a markup language file) in a single file dedicated to the program, or in multiple coordinated files (such as files for storing one or more modules, subroutines, or portions of code). A computer program may be deployed to execute on one computer or on multiple computers located at one site or distributed across multiple sites and interconnected by a communication network.

[0146] Some of the processes and logic flows described in this specification may be carried out using one or more programmable processors, where the one or more programmable processors execute one or more computer programs to act by operating on input data and generating output. These processes and logic flows may also be carried out by dedicated logic circuitry and the apparatus may also be implemented as dedicated logic circuitry, where the dedicated logic circuitry is, for example, an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0147] To provide interaction with a user, operations may be implemented on a computer having a display device (e.g., a monitor or other type of display device) for displaying information to the user, and a keyboard and a pointing device (e.g., a mouse, trackball, tablet computer, touch-sensitive screen, or other type of pointing device) by which the user may provide input to the computer. Other kinds of devices may also be used to provide interaction with the user; for example, feedback provided to the user may be any form of sensory feedback, such as visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including sound, voice, or tactile input. Additionally, the computer may interact with the user by sending and receiving documents relative to the device used by the user (e.g., by sending a web page to a web browser in response to a request received from a web browser on the user's client device).

[0148] In general aspects, one or more fields in a PHY frame are used for motion detection.

[0149] In a general example, a wireless signal is transmitted through space from a first wireless communication device to a second wireless communication device over a period of time. The wireless signal is formatted according to a wireless communication standard, and each wireless signal includes a PHY frame according to the standard. A MIMO training field (e.g., HE-LTF, HT-LTF, or VHT-LTF) is identified in the PHY frame of each wireless signal. A channel response is generated based on the corresponding MIMO training field. The channel response is used to detect motion (e.g., motion of an object) that occurs in space during the period of time.

[0150] Implementations of the general example may include one or more of the following features. The wireless communication standard is a standard for multiple-input multiple-output (MIMO) radio communication, a MIMO training field is identified in the PHY frame of each wireless signal, and a channel response is generated based on the corresponding MIMO training field. The wireless communication standard is the IEEE 802.11ax standard. The channel response is used to detect the location of motion that occurs in space during the period of time. The channel response may be analyzed in a time-domain representation, such as to detect the motion or location of a moving object.

[0151] In a first example, wireless signals transmitted through space over a period of time are received. The wireless signals can be transmitted between wireless communication devices in a wireless communication network and can be formatted according to a wireless communication standard. A first training field and a different second training field are identified in the orthogonal frequency division multiplexing (OFDM)-based PHY frames of the respective wireless signals. A first time-domain channel estimate and a second time-domain channel estimate are generated for each wireless signal. The first time-domain channel estimate can be based on a first frequency-domain signal included in the first training field of the wireless signal, and the second time-domain channel estimate can be based on a second frequency-domain signal included in the second training field of the wireless signal. In some instances, the time resolution of the first time-domain channel estimate is coarser (e.g., lower) than the time resolution of the second time-domain channel estimate. Whether motion has occurred in space during the period of time is determined based on the first time-domain channel estimate, and the location of the motion within the space is determined based on the second time-domain channel estimate.

[0152] Implementations of the first example can include one or more than one of the following features. Determining the location of the motion within the space can include determining the direction of the motion within the space based on the second time-domain channel estimate. The frequency resolution of the first frequency-domain signal can be coarser (e.g., lower) than the frequency resolution of the second frequency-domain signal. The first training field can include a legacy training field of the OFDM-based PHY frame, and the second training field can include a multiple-input multiple-output (MIMO) training field of the OFDM-based PHY frame. The MIMO training field can include a high-efficiency long training field (HE-LTF). The MIMO training field can include a very high throughput long training field (VHT-LTF). The MIMO training field can include a high throughput long training field (HT-LTF). The wireless communication standard can be an IEEE 802.11 standard. The wireless communication network can be a wireless local area network (WLAN).

[0153] In a second example, a non-transitory computer-readable medium stores instructions that, when executed by a data processing device, are operable to perform one or more than one operation of the first example. In a third example, a system includes a plurality of wireless communication devices and a device configured to perform one or more than one operation of the first example.

[0154] Implementations of the third example can include one or more than one of the following features. One of the wireless communication devices can be or include a computer device. The computer device can be located at a location remote from the wireless communication devices.

[0155] Although this specification contains many details, these details should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features specific to particular examples. Certain features that are described or illustrated in this specification in the context of separate implementations may also be combined. Conversely, various features that are described or illustrated in the context of a single implementation may also be implemented separately or in any suitable sub-combination in multiple embodiments.

[0156] Similarly, although operations are depicted in the drawings in a particular order, this should not be construed as requiring that the operations be performed in the particular order shown or in a sequential order, or that all illustrated operations be performed, to achieve a desired result. In some cases, multitasking and parallel processing may be advantageous. Additionally, the separation of various system components in the above-described implementations should not be construed as requiring such separation in all implementations, and it should be understood that the described program components and systems can generally be integrated together in a single product or packaged into multiple products.

[0157] Numerous embodiments have been described. However, it should be understood that various modifications may be made. Accordingly, other embodiments are within the scope of the above description.< / xxx> < / xxx> < / xxx>

Claims

1. A method for motion detection, comprising: Receiving wireless signals transmitted through space during a time period, wherein the wireless signals are transmitted between wireless communication devices in a wireless communication network and are formatted according to a wireless communication standard; Identifying a first training field and different second training fields in an orthogonal frequency division multiplexing (OFDM)-based PHY frame of each wireless signal; For each wireless signal: Generating a first time-domain channel estimate based on a first frequency-domain signal included in the first training field of the wireless signal; and Generating a second time-domain channel estimate based on a second frequency-domain signal included in the second training field of the wireless signal, wherein a time resolution of the first time-domain channel estimate is lower than a time resolution of the second time-domain channel estimate; Determining whether motion has occurred in the space during the time period based on the first time-domain channel estimate; and Determining a location of the motion within the space based on the second time-domain channel estimate.

2. The method according to claim 1, wherein, A frequency resolution of the first frequency-domain signal is lower than a frequency resolution of the second frequency-domain signal.

3. The method according to claim 1, wherein, The first training field includes a conventional training field of the OFDM-based PHY frame, and the second training field includes a multiple-input multiple-output (MIMO) training field of the OFDM-based PHY frame.

4. The method according to claim 3, wherein, The MIMO training field includes a high-efficiency long training field (HE-LTF).

5. The method according to claim 3, wherein, The MIMO training field includes a very high throughput long training field (VHT-LTF).

6. The method according to claim 3, wherein, The MIMO training field includes a high throughput long training field (HT-LTF).

7. The method according to claim 1, wherein The wireless communication standard is the IEEE 802.11 standard.

8. The method according to claim 1, wherein The wireless communication network is a wireless local area network (WLAN).

9. The method according to claim 1, wherein Determining the location of the motion within the space includes: determining a direction of the motion within the space based on the second time-domain channel estimate.

10. A non-transitory computer-readable medium including instructions that, when executed by a data processing device, are operative to perform operations including the following: Receiving a wireless signal transmitted through space during a time period, wherein, The wireless signals are transmitted between wireless communication devices in a wireless communication network and are formatted according to a wireless communication standard; Identifying a first training field and different second training fields in an orthogonal frequency division multiplexing (OFDM)-based PHY frame of each wireless signal; For each wireless signal: Generating a first time-domain channel estimate based on a first frequency-domain signal included in the first training field of the wireless signal; and Generating a second time-domain channel estimate based on a second frequency-domain signal included in the second training field of the wireless signal, wherein a time resolution of the first time-domain channel estimate is lower than a time resolution of the second time-domain channel estimate; Determining whether motion has occurred in the space during the time period based on the first time-domain channel estimate; and Determining a location of the motion within the space based on the second time-domain channel estimate.

11. The non-transitory computer-readable medium according to claim 10, wherein, Determining the location of the motion within the space includes: determining a direction of the motion within the space based on the second time-domain channel estimate.

12. The non-transitory computer-readable medium according to claim 10, wherein, The frequency resolution of the first frequency-domain signal is lower than that of the second frequency-domain signal.

13. The non-transitory computer-readable medium according to claim 10, wherein, The first training field includes the conventional training field of the OFDM-based PHY frame, and the second training field includes the multiple-input multiple-output training field of the OFDM-based PHY frame, i.e., the MIMO training field.

14. The non-transitory computer-readable medium according to claim 13, wherein, The MIMO training field includes the high-efficiency long training field, i.e., HE-LTF.

15. The non-transitory computer-readable medium according to claim 13, wherein, The MIMO training field includes the very high throughput long training field, i.e., VHT-LTF.

16. The non-transitory computer-readable medium according to claim 13, wherein, The MIMO training field includes the high throughput long training field, i.e., HT-LTF.

17. The non-transitory computer-readable medium according to claim 10, wherein, The wireless communication standard is the IEEE802.11 standard.

18. The non-transitory computer-readable medium according to claim 10, wherein, The wireless communication network is a wireless local area network, i.e., WLAN.

19. A system for motion detection, comprising: A plurality of wireless communication devices in a wireless communication network, the plurality of wireless communication devices being configured to transmit wireless signals formatted according to a wireless communication standard through space within a time period; A computer device, which includes one or more processors capable of operating to perform the following operations: Identifying a first training field and a different second training field in an orthogonal frequency division multiplexing-based, i.e., OFDM-based, PHY frame of each wireless signal; For each wireless signal: Generating a first time-domain channel estimate based on a first frequency-domain signal included in the first training field of the wireless signal; and Generating a second time-domain channel estimate based on a second frequency-domain signal included in the second training field of the wireless signal, wherein the time resolution of the first time-domain channel estimate is lower than that of the second time-domain channel estimate; Determining whether motion has occurred in the space during the time period based on the first time-domain channel estimate; and Determining the position of the motion in the space based on the second time-domain channel estimate.

20. The system according to claim 19, wherein, The frequency resolution of the first frequency-domain signal is lower than that of the second frequency-domain signal.

21. The system according to claim 19, wherein, The first training field includes the conventional training field of the OFDM-based PHY frame, and the second training field includes the multiple-input multiple-output training field of the OFDM-based PHY frame, i.e., the MIMO training field.

22. The system according to claim 21, wherein, The MIMO training field includes the high-efficiency long training field, i.e., HE-LTF.

23. The system according to claim 21, wherein, The MIMO training field includes the very high throughput long training field, i.e., VHT-LTF.

24. The system according to claim 21, wherein, The MIMO training field includes the high throughput long training field, i.e., HT-LTF.

25. The system according to claim 19, wherein The wireless communication standard is the IEEE 802.11 standard.

26. The system according to claim 19, wherein, The wireless communication network is a wireless local area network, i.e., WLAN.

27. The system according to claim 19, wherein, Determining the position of the motion in the space includes determining the direction of the motion in the space based on the second time-domain channel estimate.

28. The system according to claim 19, wherein, The computer device is one of the wireless communication devices.

29. A computer program product, which includes instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 1-9.

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