CN118871811A

CN118871811A - System and method for identifying frequency characteristics of a waveform using a timestamp

Info

Publication number: CN118871811A
Application number: CN202380026858.2A
Authority: CN
Inventors: C·贝格; M·欧密尔
Original assignee: Cognitive Systems Corp
Current assignee: Cognitive Systems Corp
Priority date: 2022-03-11
Filing date: 2023-03-08
Publication date: 2024-10-29

Abstract

A method for Wi-Fi sensing is described. The method is performed by a networked device that operates as a sensing receiver. The networked device includes at least one processor configured to execute instructions. Initially, a series of time-domain pulse sets is obtained, which are determined from a series of sensing measurements based on a series of sensing transmissions transmitted by a sensing transmitter and received by the networked device within a time interval. Thereafter, characteristic pulses occurring in the set of time-domain pulses are identified. A series of amplitudes of the characteristic pulses in the set of time domain pulses is recorded. Furthermore, waveform frequency features of small motions occurring in a sensing space corresponding to the networked device are identified based on the series of amplitudes of the characteristic pulses.

Description

System and method for identifying waveform frequency characteristics using time stamps

Technical Field

The present disclosure relates generally to systems and methods for Wi-Fi sensing. In particular, the present disclosure relates to systems and methods for identifying waveform frequency characteristics using time stamps.

Background

Motion detection systems have generally been used to detect movement in an environment, for example, movement of an object in a room or in an outdoor area. Wi-Fi sensing systems are one type of system that has recently incorporated motion detection systems. The Wi-Fi sensing system may be a network of Wi-Fi enabled devices, which may be part of an IEEE 802.11 network. For example, a Wi-Fi sensing system may include a sensing receiver and a sensing transmitter. In an example, a Wi-Fi sensing system may be configured to detect a feature of interest in a sensing space. The sensing space may refer to any physical space in which the Wi-Fi sensing system may operate, such as a residence, a work place, a shopping mall, a gym or stadium, a garden, or any other physical space. Features of interest may include motion and motion tracking of objects, presence detection, intrusion detection, gesture recognition, fall detection, respiratory rate detection, and other applications. The feature of interest may also be referred to as a physical process.

Disclosure of Invention

Systems and methods for Wi-Fi sensing are provided. In an example embodiment, a method for Wi-Fi sensing by a networked device operating as a sensing receiver is described. The networked device includes at least one processor configured to execute instructions. The method comprises the following steps: obtaining a set of time-domain pulses, the set of time-domain pulses determined from a series of sensing measurements based on a series of sensing transmissions transmitted by a sensing transmitter and received by a networked device over a time interval; identifying characteristic pulses occurring in the time domain pulse set; recording a series of amplitudes of the characteristic pulses in the time domain pulse set; and identifying waveform frequency features of small motions occurring in a sensing space corresponding to the networked device based on the series of amplitudes of the feature pulses.

In some embodiments, the characteristic pulse represents a plurality of corresponding pulses, each of the corresponding pulses occurring in a respective one of the sets of time-domain pulses.

In some embodiments, identifying the signature pulse includes selecting a signature pulse from among a plurality of wobble pulses that exhibits a change in amplitude of the set of time domain pulses.

In some embodiments, identifying the signature pulse includes selecting a wobble pulse having a largest amplitude variation from among a plurality of wobble pulses.

In some embodiments, the series of amplitudes of the characteristic pulse have uniform timing between the amplitudes.

In some embodiments, the series of amplitudes of the characteristic pulse has non-uniform timing between the amplitudes.

In some embodiments, the timing between amplitudes in the series of amplitudes is based on the timing of at least one of the sensing transmission and the sensing measurement.

In some embodiments, the series of amplitudes of the recording signature includes a change in the amplitude of the recording signature.

In some embodiments, identifying the waveform frequency signature includes evaluating the series of amplitudes of the signature pulses relative to a reasonable frequency waveform.

In some embodiments, evaluating the series of amplitudes of the characteristic pulse relative to the reasonable frequency waveform includes creating a fourier basis function from the series of amplitudes of the characteristic pulse and the reasonable frequency waveform.

In some embodiments, the sensing space further corresponds to a transmission path between the networked device and the sensing transmitter.

Other aspects and advantages of the present disclosure will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, illustrating by way of example the principles of the disclosure.

Drawings

The foregoing and other objects, aspects, features, and advantages of the present disclosure will become more apparent and better understood with reference to the following description taken in conjunction with the accompanying drawings in which:

fig. 1 is a diagram illustrating an example wireless communication system;

FIGS. 2A and 2B are diagrams showing example wireless signals transmitted between wireless communication devices;

fig. 3A and 3B are graphs showing examples of channel responses calculated from wireless signals transmitted between wireless communication devices in fig. 2A and 2B;

FIGS. 4A and 4B are diagrams illustrating example channel responses associated with movement of an object in different spatial regions;

Fig. 4C and 4D are graphs showing the example channel responses of fig. 4A and 4B superimposed on an example channel response associated with no motion occurring in space;

fig. 5 depicts some architectures of implementations of systems for Wi-Fi sensing according to some embodiments;

FIG. 6 illustrates a management frame carrying a sensing transmission in accordance with some embodiments;

fig. 7A illustrates an example of a format of a control frame according to some embodiments, and fig. 7B illustrates a format of a sense transmission notification control field of a control frame according to some embodiments;

Fig. 8A illustrates another example of a format of a control frame according to some embodiments, and fig. 8B illustrates a format of a sensing measurement control field of a control frame according to some embodiments;

FIG. 9 illustrates a management frame carrying a CRI transfer message in accordance with some embodiments;

Fig. 10 depicts an example representation of a transmission channel containing a direct signal path and a single multipath, in accordance with some embodiments;

FIG. 11 depicts an example representation of the amplitude and time of a multipath time domain pulse in accordance with some embodiments;

fig. 12 depicts an example representation of the amplitude of a received multipath signal with a single reflected time domain pulse altered or modulated by small motion of an object, in accordance with some embodiments;

FIG. 13 depicts an example representation of small motions with waveform frequency features, in accordance with some embodiments;

FIG. 14 depicts an example representation of the amplitude of a received time domain pulse containing a characteristic pulse, in accordance with some embodiments;

15A, 15B, and 15C depict example representations of the amplitudes of received time domain pulses containing characteristic pulses at different sensing measurement times, in accordance with some embodiments;

FIG. 16 depicts an example representation of a series of amplitudes of a characteristic pulse in accordance with some embodiments;

FIG. 17A depicts an example of a reasonable frequency well aligned with the frequency of waveform amplitude variations of a characteristic pulse, in accordance with some embodiments;

FIG. 17B depicts an example of a reasonable frequency that is not well aligned with the frequency of waveform amplitude variations of a characteristic pulse, in accordance with some embodiments; and

FIG. 18 depicts a flowchart of identification of waveform frequency features for small movements occurring in a sensing space, in accordance with some embodiments.

Detailed Description

In some aspects described herein, wireless sensing systems may be used in a variety of wireless sensing applications by processing wireless signals (e.g., radio Frequency (RF) signals) transmitted through a space between wireless communication devices. An example wireless sensing application includes motion detection, which may include the following: detecting a subject's motion in space, motion tracking, breath detection, breath monitoring, presence detection, gesture recognition, human detection (moving and stationary human detection), human tracking, fall detection, velocity estimation, intrusion detection, walking detection, step counting, breath rate detection, apnea estimation, gesture change detection, activity recognition, pace classification, gesture decoding, sign language recognition, hand tracking, heart rate estimation, breath rate estimation, room occupancy detection, human dynamics monitoring, and other types of motion detection applications. Other examples of wireless sensing applications include object recognition, voice recognition, keystroke detection and recognition, tamper detection, touch detection, attack detection, user authentication, driver fatigue detection, traffic monitoring, smoke detection, campus violence detection, people counting, body recognition, bicycle positioning, people queue estimation, wi-Fi imaging, and other types of wireless sensing applications. For example, the wireless sensing system may 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. As described in more detail below, the wireless sensing system may be configured to control measurement rates, wireless connections, and device participation, e.g., to improve system operation or achieve other technical advantages. In examples where the wireless sensing system is used for another type of wireless sensing application, the system improvements and technical advantages achieved when the wireless sensing system is used for motion detection are likewise achieved.

In some example wireless sensing systems, the wireless signal includes a component that the wireless device may use to estimate a channel response or other channel information (e.g., a synchronization preamble in a Wi-Fi PHY frame, or another type of component), and the wireless sensing system may detect motion (or another characteristic, depending on the wireless sensing application) by analyzing changes in the channel information collected over time. In some examples, the wireless sensing system may operate similar to a bistatic radar system, with a Wi-Fi Access Point (AP) functioning as a receiver and each Wi-Fi device (station (STA) or node or peer) connected to the AP functioning as a transmitter. The wireless sensing system may trigger the connected devices to generate transmissions and generate channel response measurements at the receiver device. This triggering process may be repeated periodically to obtain a series of time-varying measurements. The wireless sensing algorithm may then receive the generated time series of channel response measurements (e.g., calculated by the Wi-Fi receiver) as input, and through a correlation or filtering process, may then make a determination (e.g., determine whether there is motion within the environment represented by the channel response, e.g., based on a change or pattern of channel estimates). In examples where the wireless sensing system detects motion, the location of the motion within the environment may also be identified based on the motion detection results among several wireless devices.

Thus, wireless signals received at each wireless communication device in the wireless communication network may be analyzed to determine channel information for various communication links in the network (between the respective pair of wireless communication devices). The channel information may represent a physical medium in which a transfer function is applied to a wireless signal passing through a space. In some examples, the channel information includes a channel response. The channel response may characterize the physical communication path, representing, for example, the combined effects of scattering, fading, and power attenuation in the space between the transmitter and the receiver. In some examples, the channel information includes beamforming state information (e.g., feedback matrix, steering matrix, channel state information, etc.) provided by the beamforming system. Beamforming is a signal processing technique commonly used in multi-antenna (multiple input/multiple output (MIMO)) radio systems for directional signal transmission or reception. Beamforming may be achieved by operating elements in an antenna array in such a way that signals at some angles experience constructive interference, while other signals experience destructive interference.

The channel information for each communication link may be analyzed (e.g., by a hub device or other device in the wireless communication network, or a sensing transmitter, sensing receiver, or sensing initiator communicatively coupled to the network), for example, to detect whether motion has occurred in space, to determine the relative location of the detected motion, or both. In some aspects, the channel information for each communication link may be analyzed to detect whether an object is present or absent, such as when no motion is detected in space.

In some cases, the wireless sensing system may control the node measurement rate. For example, wi-Fi motion systems may configure variable measurement rates (e.g., channel estimation/environmental measurement/sampling rates) based on criteria given by current wireless sensing applications (e.g., motion detection). In some implementations, when motion is not present or detected for a period of time, for example, the wireless sensing system may reduce the rate of the measurement environment such that the sensing transmission or sensing measurement will be triggered or caused to occur less frequently via the connection device. In some implementations, when motion is present, for example, the wireless sensing system may increase the trigger rate or sense the transmission rate or sense the measurement rate to produce a time series of measurements with finer time resolution. Controlling the variable sensing measurement rate may enable power savings (triggered by the device), reduced processing (reduced data to be correlated or filtered), and improved resolution during specified times.

In some cases, the wireless sensing system may perform band steering or client steering for nodes in the overall wireless network, e.g., in Wi-Fi multi-AP or Extended Service Set (ESS) topologies, multiple coordinator wireless APs each provide a Basic Service Set (BSS), which BSSs may occupy different frequency bands and allow devices to transparently move between one participating AP to another (e.g., mesh). For example, in a home mesh network, a Wi-Fi device may connect to any AP, but typically will select an AP with good signal strength. The coverage areas of mesh APs typically overlap, and each device is typically placed within communication range or within more than one AP. If the AP supports multiple bands (e.g., 2.4GHz and 5 GHz), the wireless sensing system may cause the device to remain connected to the same physical AP, but instruct the device to use different frequency bands to obtain more diverse information to help improve the accuracy or result of the wireless sensing algorithm (e.g., motion detection algorithm). In some implementations, the wireless sensing system may change the device from being connected to one mesh AP to being connected to another mesh AP. For example, such device steering may be performed during wireless sensing (e.g., motion detection) based on criteria detected in a particular region to improve detection coverage or better locate motion within the region.

In some cases, beamforming may be performed between wireless communication devices based on some knowledge of the communication channel (e.g., by feedback properties generated by the receiver), which may be used to generate one or more steering properties (e.g., steering matrices) that the transmitter device applies to shape the transmitted beam/signal in one or more particular directions. Thus, a change in the steering or feedback properties used in the beamforming process is indicative of a change in the space accessed by the wireless communication system that may be caused by the moving object. For example, motion may be detected by a significant change in the communication channel over a period of time, e.g., as indicated by channel response, or steering or feedback properties, or any combination thereof.

In some implementations, for example, the steering matrix may be generated at the transmitter device (beamformer) based on a feedback matrix provided by the receiver device (beamformee) based on channel sounding. Since the steering and feedback matrices are related to the propagation characteristics of the channel, these matrices change as the object moves within the channel. The change in channel characteristics is reflected in these matrices accordingly, and by analyzing these matrices, the motion can be detected, and different characteristics of the detected motion can be determined. In some implementations, the spatial map may be generated based on one or more beamforming matrices. The spatial map may indicate a general direction of objects in space relative to the wireless communication device. In some cases, a number of beamforming matrices (e.g., feedback matrices or steering matrices) may be generated to represent a plurality of directions in which an object may be positioned relative to a wireless communication device. The number of beamforming matrices may be used to generate a spatial map. The spatial map may be used to detect the presence of motion in space or to detect the location of detected motion.

In some examples, the motion detection system may control the variable device measurement rate during motion detection. For example, a feedback control system for a multi-node wireless motion detection system may adaptively change the sampling rate based on environmental conditions. In some cases, such control may improve the operation of the motion detection system or provide other technical advantages. For example, the measurement rate may be controlled in a manner that optimizes or otherwise improves the air time usage and detection capabilities, which is suitable for a variety of different environments and different motion detection applications. The rate may be measured in a manner that reduces redundant measurement data to be processed, thereby reducing processor load/power requirements. In some cases, the measurement rate is controlled in an adaptive manner, e.g., the adaptive samples may be controlled separately for each participating device. The adaptive sampling rate may be used with a tuned control loop to accommodate different use cases or device characteristics.

In some cases, the wireless sensing system may allow the device to dynamically indicate and communicate its wireless sensing capabilities or wireless sensing willingness to the wireless sensing system. For example, sometimes a device may not wish to be periodically interrupted or triggered to transmit a wireless signal that allows an AP to generate channel measurements. For example, if the device is sleeping, waking up the device frequently to transmit or receive wireless sensing signals may consume resources (e.g., cause the cell phone battery to discharge faster). These and other events may make the device willing or unwilling to engage in wireless sensing system operation. In some cases, a cell phone running with a battery may not want to participate, but when the cell phone is plugged into a charger, it may be willing to participate. Thus, if a handset is not plugged in, the handset may indicate to the wireless sensing system to exclude the handset from participation; and if a handset is plugged in, the handset may indicate to the wireless sensing system to include the handset in wireless sensing system operation. In some cases, a device may not want to participate if the device is underloaded (e.g., the device is streaming audio or video) or busy performing a primary function; and when the load of the same device is reduced and participation does not interfere with the primary function, the device may indicate to the wireless sensing system that it is willing to participate.

Example wireless sensing systems are described below in the context of motion detection (detecting motion of an object in space, motion tracking, respiration detection, respiration monitoring, presence detection, gesture recognition, human detection (moving and stationary human detection), human tracking, fall detection, velocity estimation, intrusion detection, walking detection, step counting, respiration rate detection, apnea estimation, gesture change detection, activity recognition, pace 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). However, in examples where the wireless sensing system is used for another type of wireless sensing application, the operational, system improvements, and technical advantages achieved when the wireless sensing system is operated as a motion detection system are equally applicable.

In various embodiments of the present disclosure, the following provides a non-limiting definition of one or more terms that will be used in this document.

The term "measurement activity" may refer to a series of bi-directional one or more sensing transmissions between a sensing receiver and a sensing transmitter that allow for the calculation of a series of one or more sensing measurements.

The term "sensing transmitter" may refer to a device that sends a transmission (e.g., NDP and PPDU or any other transmission) for sensing measurements (e.g., channel state information) in a Wireless Local Area Network (WLAN) sensing session. In an embodiment, the role of the sensing transmitter may be played by the remote device.

The term "sensing receiver" may refer to a device that receives transmissions sent by a sensing transmitter (e.g., NDP and PPDU or any other transmission that may have the opportunity to be used for sensing measurements) and performs one or more sensing measurements (e.g., channel state information) in a WLAN sensing session. In an embodiment, the role of the sensing receiver may be assumed by the sensing device.

The term "waveform amplitude variation" of a time domain pulse may refer to a variation over the fundamental amplitude of a reflected time domain pulse received at a sensing receiver. In an embodiment, the waveform amplitude variation may be caused by periodic motion of the object in the propagation path of the received reflected time domain pulse from the sensing transmitter to the sensing receiver.

The term "steady state channel" may refer to a transmission channel in which an object causing reflection in the transmission channel in a sensing space is relatively stationary and reflects a generated multipath signal with a stable amplitude and time delay. An example of a sensing space that produces a steady state channel may be a living room with furniture at various locations in the living room.

The term "pseudo-steady state channel" may refer to a transmission channel in which an object causing reflections in the transmission channel in the sensing space is stationary for a sufficiently long period of time such that the fundamental amplitude of each time domain pulse may be determined to produce a multipath signal. An example of a sensing space that produces a pseudo steady state channel may be a bedroom in which a person is in bed and sleeping.

The term "fundamental amplitude" of a time domain pulse may be the amplitude of the time domain pulse in a steady state channel or pseudo steady state channel.

The term "channel state information" may refer to properties of a communication channel that are known or measured by channel estimation techniques. The channel state information may represent how the wireless signal propagates from a transmitter (e.g., a sensing transmitter) to a receiver (e.g., a sensing receiver) along multiple paths. The channel state information is typically a complex-valued matrix representing the amplitude attenuation and phase shift of the signal, which provides an estimate of the communication channel.

The term "Inverse Discrete Fourier Transform (IDFT)" may refer to an algorithm that transforms a signal in the frequency domain into a signal in the time domain. In an example, IDFT may be used to transform channel state information into TD-CRI. In an embodiment, the IDFT may be implemented using an Inverse Fast Fourier Transform (IFFT).

The term "complete time domain channel representation information (complete TD-CRI)" may refer to a series of pairs of time domain pulse complex numbers created by performing IDFT or IFFT on channel state information values (e.g., channel state information calculated by a baseband receiver).

The term "Channel Representation Information (CRI)" may refer to a set of sensing measurements that together represent the state of a channel between two devices. Examples of CRIs are channel state information and complete TD-CRI.

The term "filtered time domain channel representation information (filtered TD-CRI)" may refer to a reduced series of pairs of time domain pulse complex numbers created by applying an algorithm to the complete TD-CRI. The algorithm may select some time domain pulses and reject other time domain pulses. The filtered TD-CRI contains information relating the selected time domain pulse to the corresponding time domain pulse in the complete TD-CRI.

The term "Null Data PPDU (NDP)" may refer to a PPDU that does not include a data field. In an example, the null data PPDU may be used for the sensing transmission, where in an example it is a Medium Access Control (MAC) header containing the required information.

The term "sensing transmission" may refer to any transmission from a sensing transmitter to a sensing receiver that may be used to make a sensing measurement. In an example, the sensing transmission may also be referred to as a wireless sensing signal or a wireless signal.

The term "sensing trigger message" may refer to a message sent from a sensing receiver to a sensing transmitter to trigger one or more sensing transmissions that may be used to perform a sensing measurement. In an example, a sensing trigger message may be sent from the sensing transmitter to the sensing receiver to cause the sensing receiver to send a sensing measurement response message back to the sensing transmitter or to the sensing initiator.

The term "sensing response message" may refer to a message contained within a sensing transmission from a sensing transmitter to a sensing receiver. In an example, a sensing transmission including a sensing response message may be used to perform a sensing measurement.

The term "sensing measurement" may refer to a measurement of the state of a channel, i.e., a channel state information measurement between a sensing transmitter and a sensing receiver derived from a transmission (e.g., a sensing transmission).

The term "transmission parameters" may refer to a set of IEEE 802.11PHY transmitter configuration parameters that are defined as part of a transmission vector (TXVECTOR) corresponding to a particular PHY and may be configured for each PHY layer protocol data unit (PPDU) transmission.

The term "PHY layer protocol data unit (PPDU)" may refer to a data unit that includes a preamble and a data field. The preamble field may contain transmission vector format information and the data field may contain a payload and a higher layer header.

The term "Channel Response Information (CRI) transmission message" may refer to a message sent by a sensing receiver that has performed a sensing measurement on a sensing transmission, where the sensing receiver sends the CRI to the sensing transmitter.

The term "time domain pulse" may refer to a complex number representing the amplitude and phase of discretized energy in the time domain. When the channel state information value of each tone is obtained from the baseband receiver, a time domain pulse is obtained by performing an inverse fourier transform (e.g., IDFT or IFFT) on the channel state information value.

The term "delivered transmission configuration" may refer to a transmission parameter applied by the sensing transmitter to the sensing transmission.

The term "requested transmission configuration" may refer to a requested transmission parameter of a sensing transmitter to be used when sending a sensing transmission.

A "transmission channel" may refer to a tunable channel on which a sensing receiver performs sensing measurements and/or on which a sensing transmitter performs sensing transmissions.

The term "sensing transmission notification message" may refer to a message sent from a sensing transmitter to a sensing receiver that a notification sensing transmission NDP will follow within a Short Inter Frame Space (SIFS). The sensing transmission NDP may be transmitted using a transmission parameter defined using a sensing transmission notification message.

The term "sensing transmission NDP" may refer to an NDP transmission sent by a sensing transmitter and used for sensing measurements at a sensing receiver. The transmission is subsequent to sensing the transmission notification message and may be transmitted using transmission parameters defined in the sensing transmission notification message.

The term "sensing measurement poll message" may refer to a message sent from a sensing transmitter to a sensing receiver to solicit transmission of channel representation information that the sensing receiver has determined.

The term "sensing configuration message" may refer to a message sent from a device (e.g., via a networked device) containing a sensing algorithm to a sensing receiver. The sensing configuration message may include a channel representation information configuration. The channel representation information configuration is interchangeably referred to as a time domain channel representation information (TD-CRI) configuration.

The term "sensing configuration response message" may refer to a message sent from a sensing receiver to a device (e.g., a networked device) that includes a sensing algorithm in response to the sensing configuration message. In an example, the sensing configuration response message may be an acknowledgement of the sensing configuration message.

The term "feature of interest" may refer to an item or item state that is actively detected and/or identified by a sensing algorithm.

The term "motion path" may refer to a physical route taken by an object traveling through a sensing space. The path of movement may occur between the conveyor and/or the reflector.

The term "sensing space" may refer to a physical space in which a Wi-Fi sensing system may operate.

The term "Wi-Fi sensing session" may refer to a period of time during which an object in a sensing space may be detected, and/or characterized. In an example, during a Wi-Fi sensing session, several devices participate therein, facilitating the generation of sensing measurements. Wi-Fi sensing sessions may also be referred to as WLAN sensing sessions or simply as sensing sessions.

For reading the description of the various embodiments below, the following descriptions of the various sections of this specification and their respective contents may be helpful:

Section a describes wireless communication systems, wireless transmissions, and sensing measurements that may be used to practice the embodiments described herein.

Section B describes systems and methods that may be used with Wi-Fi sensing systems configured to send sensing transmissions and make sensing measurements.

Section C describes embodiments of systems and methods for identifying waveform frequency characteristics using time stamps.

A. Wireless communication system, wireless transmission and sensing measurement

Fig. 1 illustrates a wireless communication system 100. The wireless communication system 100 includes three wireless communication devices: a first wireless communication device 102A, a second wireless communication device 102B, and a third wireless communication device 102C. The wireless communication system 100 may include additional wireless communication devices and other components (e.g., additional wireless communication devices, one or more network servers, network routers, network switches, cables or other communication links, etc.).

The wireless communication devices 102A, 102B, 102C may operate in a wireless network, for example, according to a wireless network standard or another type of wireless communication protocol. 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 another type of wireless network. Examples of WLANs include networks (e.g., wi-Fi networks) configured to operate in accordance with one or more of the IEEE developed 802.11 family of standards, and the like. Examples of PANs include those according to short-range communication standards (e.g.,Near Field Communication (NFC), zigBee), millimeter wave communication, and the like.

In some implementations, the 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 2G standards such as Global System for Mobile (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); 5G standard, etc.

In the example shown in fig. 1, the wireless communication devices 102A, 102B, 102C may be or may include standard wireless network components. For example, the wireless communication devices 102A, 102B, 102C may be a commercially available Wi-Fi AP or another type of Wireless Access Point (WAP) that performs one or more operations as described herein, embedded as instructions (e.g., software or firmware) on a modem of the WAP. In some cases, the wireless communication devices 102A, 102B, 102C may be nodes of a wireless mesh network, such as a commercially available mesh network system (e.g., plasmid Wi-Fi, google Wi-Fi, qualcomm Wi-Fi SoN, etc.). In some cases, another type of standard or conventional Wi-Fi transmitter device may be used. In some examples, one or more of the wireless communication devices 102A, 102B, 102C may be implemented as WAPs in a mesh network, while other wireless communication devices 102A, 102B, 102C are implemented as leaf devices (e.g., mobile devices, smart devices, etc.) that access the mesh network through one of the WAPs. In some cases, one or more of the wireless communication devices 102A, 102B, 102C are mobile devices (e.g., smartphones, smartwatches, tablet computers, laptops, etc.), wireless enabled devices (e.g., smart thermostats, wi-Fi enabled cameras, smart televisions), or another type of device that communicates in a wireless network.

The wireless communication devices 102A, 102B, 102C may be implemented without Wi-Fi components; for example, other types of standard or non-standard wireless communications may be used for motion detection. In some cases, the wireless communication devices 102A, 102B, 102C may be, or may be part of, a dedicated motion detection system. For example, a dedicated motion detection system may include a hub device and one or more beacon devices (as remote sensor devices), and the wireless communication devices 102A, 102B, 102C may be hub devices or beacon devices in the motion detection system.

As shown in fig. 1, the wireless communication device 102C includes a modem 112, a processor 114, a memory 116, and a power supply unit 118; any of the wireless communication devices 102A, 102B, 102C in the wireless communication system 100 may contain the same, additional, or different components, and these components may be configured to operate as shown in fig. 1 or in another manner. In some implementations, the modem 112, processor 114, memory 116, and power supply unit 118 of the wireless communication device are housed together in a common housing or other component. In some embodiments, one or more components of the wireless communication device may be housed separately, e.g., in a separate housing or other assembly.

Modem 112 may transmit (receive, transmit, or both) wireless signals. For example, modem 112 may be configured to transmit RF signals formatted according to a wireless communication standard (e.g., wi-Fi or bluetooth). The modem 112 may be implemented as the example wireless network modem 112 shown in fig. 1, or may be implemented in another manner, e.g., with other types of components or subsystems. In some implementations, the modem 112 includes a radio subsystem and a baseband subsystem. In some cases, the baseband subsystem and the radio subsystem may be implemented on a common chip or chipset, or may be implemented in a card or another type of assembled device. The baseband subsystem may be coupled to the radio subsystem, for example, by leads, pins, wires, or other types of connections.

In some cases, the radio subsystem in modem 112 may include one or more antennas and RF circuitry. The RF circuitry may include, for example, circuitry to filter, amplify, or otherwise condition analog signals, circuitry to up-convert baseband signals to RF signals, circuitry to down-convert RF signals to baseband signals, and the like. Such circuitry may include, for example, filters, amplifiers, mixers, local oscillators, and the like. The radio subsystem may be configured to transmit radio frequency wireless signals over a wireless communication channel. As an example, a radio subsystem may include a radio chip, an RF front end, and one or more antennas. The radio subsystem may include additional or different components. In some implementations, the radio subsystem may be or include radio electronics (e.g., RF front-end, radio chip, or the like) from a conventional modem, such as from a Wi-Fi modem, pico base station modem, or the like. In some implementations, the antenna includes a plurality of antennas.

In some cases, the baseband subsystem in modem 112 may include digital electronics configured to process digital baseband data, for example. As an example, the baseband subsystem may include a baseband chip. The baseband subsystem may include additional or different components. In some cases, the baseband subsystem may include a Digital Signal Processor (DSP) device or another type of processor device. In some cases, the baseband system includes digital processing logic to operate the radio subsystem, transmit wireless network traffic through the radio subsystem, detect motion based on motion detection signals received through the radio subsystem, or perform other types of processing. For example, a baseband subsystem may include one or more chips, chipsets, or other types of devices configured to encode signals and deliver the encoded signals to a radio subsystem for transmission, or to identify and analyze data encoded in signals from the radio subsystem (e.g., by decoding the signals according to a wireless communication standard, by processing the signals according to a motion detection process, or otherwise).

In some examples, the radio subsystem in modem 112 receives baseband signals from the baseband subsystem, up-converts the baseband signals to RF signals, and wirelessly transmits the RF signals (e.g., via an antenna). In some examples, the radio subsystem in modem 112 receives the RF signal wirelessly (e.g., through an antenna), down-converts the RF to a baseband signal, and sends the baseband signal to the baseband subsystem. The signals exchanged between the radio subsystem and the baseband subsystem may be digital or analog signals. In some examples, the baseband subsystem includes conversion circuitry (e.g., digital-to-analog converter, analog-to-digital converter) and exchanges analog signals with the radio subsystem. In some examples, the radio subsystem includes conversion circuitry (e.g., digital-to-analog converter, analog-to-digital converter) and exchanges digital signals with the baseband subsystem.

In some cases, the baseband subsystem of modem 112 may transmit wireless network traffic (e.g., data packets) over one or more network traffic channels through a radio subsystem in a wireless communication network. The baseband subsystem of modem 112 may also transmit or receive (or both) signals (e.g., motion detect signals or motion detect signals) over a dedicated wireless communication channel through a radio subsystem. In some examples, the baseband subsystem generates motion detection signals for transmission, e.g., to detect motion space. In some examples, the baseband subsystem processes the received motion detection signal (a signal based on the motion detection signal transmitted through space), e.g., to detect motion of an object in space.

The processor 114 may execute instructions, for example, to generate output data based on data input. The instructions may include programs, code, scripts, or other types of data stored in memory. Additionally or alternatively, the instructions may be encoded as preprogrammed or re-programmable logic circuits, logic gates, or other types of hardware or firmware components. Processor 114 may be or include a general purpose microprocessor, as a special purpose coprocessor or another type of data processing device. In some cases, the processor 114 performs advanced operations of the wireless communication device 102C. For example, the processor 114 may be configured to execute or interpret software, scripts, programs, functions, executable files, or other instructions stored in the memory 116. In some implementations, the processor 114 may be included in the modem 112.

The memory 116 may include computer readable storage media such as volatile memory devices, non-volatile memory devices, or both. Memory 116 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 examples, one or more components of the memory may be integrated with or otherwise associated with another component of the wireless communication device 102C. The memory 116 may store instructions executable by the processor 114. For example, the instructions may include instructions to time align the signals using the disturbance buffer and the motion detection buffer, for example, by one or more operations of the example process of fig. 18. The power supply unit 118 provides power to other components of the wireless communication device 102C. For example, other components may operate based on power provided by the power supply unit 118 through a voltage bus or other connection. In some embodiments, the power supply unit 118 includes a battery or battery system, such as a rechargeable battery. In some implementations, the power supply unit 118 includes an adapter (e.g., an Alternating Current (AC) adapter) that receives an external power supply signal (from an external source) and converts the external power supply signal to an internal power supply signal that is conditioned for components of the wireless communication device 102C. The power supply unit 118 may include other components or operate in another manner.

In the example shown in fig. 1, the wireless communication devices 102A, 102B transmit wireless signals (e.g., according to a wireless network standard, motion detection protocol, or otherwise). For example, the wireless communication devices 102A, 102B may broadcast wireless motion probe signals (e.g., reference signals, beacon signals, status signals, etc.), or may send wireless signals addressed to other devices (e.g., user equipment, client devices, servers, etc.), and other devices (not shown) as well as the wireless communication device 102C may receive wireless signals transmitted by the wireless communication devices 102A, 102B. In some cases, the wireless signals transmitted by the wireless communication devices 102A, 102B are periodically repeated, e.g., according to a wireless communication standard or otherwise.

In the illustrated example, the wireless communication device 102C processes wireless signals from the wireless communication devices 102A, 102B to detect movement of an object in a space accessed by the wireless signals, to determine a location of the detected movement, or both. For example, the wireless communication device 102C may perform one or more operations of the example process described below with respect to fig. 18 or another type of process for detecting motion or determining a location of the detected motion. The space accessed by the wireless signal may be an indoor or outdoor space, which may include, for example, one or more fully or partially enclosed areas, open areas without fences, and the like. The space may be or may include a room interior, a plurality of rooms, a building, and the like. In some cases, the wireless communication system 100 may be modified, for example, such that the wireless communication device 102C may transmit wireless signals, and the wireless communication devices 102A, 102B may process the wireless signals from the wireless communication device 102C to detect motion or determine the location of the detected motion.

The wireless signals for motion detection may include, for example, a beacon signal (e.g., a bluetooth beacon, wi-Fi beacon, other wireless beacon signal), another standard signal generated for other purposes according to a wireless network standard, or a non-standard signal (e.g., a random signal, a reference signal, etc.) generated for motion detection or other purposes. In an example, motion detection may be performed by analyzing one or more training fields carried by the wireless signal or by analyzing other data carried by the signal. In some examples, data will be added for the explicit purpose of motion detection, or the data used will nominally be used for another purpose and again or instead for motion detection. In some examples, wireless signals propagate through an object (e.g., a wall) before or after interacting with the moving object, which may allow movement of the moving object to be detected without an optical line of sight between the moving object and the transmitting or receiving hardware. Based on the received signal, the wireless communication device 102C may generate motion detection data. In some examples, the wireless communication device 102C may communicate the motion detection data to another device or system, such as a security system, which may include a control center for monitoring movement within a space, such as a room, building, outdoor area, or the like.

In some implementations, the wireless communication devices 102A, 102B may be modified to transmit a motion detection signal (which may include, for example, a reference signal, a beacon signal, or another signal for detecting motion space) on a wireless communication channel (e.g., a frequency channel or an encoded channel) separate from the wireless network traffic signal. For example, the wireless communication device 102C may be aware of the modulation of the payload applied to the motion detection signal and the type of data or data structures in the payload, which may reduce the amount of processing performed by the wireless communication device 102C for motion sensing. The header may include additional information such as an indication of whether another device in the communication system 100 detected motion, an indication of a modulation type, an identification of the device transmitting the signal, etc.

In the example shown in fig. 1, the wireless communication system 100 is a wireless mesh network with wireless communication links between each wireless communication device 102. In the example shown, the wireless communication link between wireless communication device 102C and wireless communication device 102A may be used to probe motion detection field 110A, the wireless communication link between wireless communication device 102C and wireless communication device 102B may be used to probe motion detection field 110B, and the wireless communication link between wireless communication device 102A and wireless communication device 102B may be used to probe motion detection field 110C. In some examples, each wireless communication device 102 detects motion in the motion detection field 110 accessed by the device by processing a received signal that is based on wireless signals transmitted by the wireless communication device 102 through the motion detection field 110. For example, as the person 106 shown in fig. 1 moves in the motion detection fields 110A and 110C, the wireless communication device 102 may detect motion based on signals they receive, which are based on wireless signals transmitted through the respective motion detection fields 110. For example, the wireless communication device 102A may detect movement of the person 106 in the movement detection fields 110A, 110C, the wireless communication device 102B may detect movement of the person 106 in the movement detection field 110C, and the wireless communication device 102C may detect movement of the person 106 in the movement detection field 110A.

In some examples, the motion detection field 110 may include, for example, air, a solid material, a liquid, or another medium through which wireless electromagnetic signals may propagate. In the example shown in fig. 1, the motion detection field 110A provides a wireless communication channel between the wireless communication device 102A and the wireless communication device 102C, the motion detection field 110B provides a wireless communication channel between the wireless communication device 102B and the wireless communication device 102C, and the motion detection field 110C provides a wireless communication channel between the wireless communication device 102A and the wireless communication device 102B. In some aspects of operation, wireless signals transmitted over a wireless communication channel (separate from or shared with wireless communication channels for network traffic) are used to detect movement of an object in space. The object may be any type of static or movable object and may be living or inanimate. For example, the object may be a human (e.g., human 106 shown in fig. 1), an animal, an inorganic object or another device, apparatus or component, an object defining all or part of a boundary of a space (e.g., a wall, a door, a window, etc.), or another type of object. In some implementations, motion information from a wireless communication device may be analyzed to determine a location of detected motion. For example, as described further below, one of the wireless communication devices 102 (or another device communicatively coupled to the wireless communication device 102) may determine that the detected motion is in the vicinity of a particular wireless communication device.

Fig. 2A and 2B are diagrams showing example wireless signals transmitted between wireless communication devices 204A, 204B, 204C. The wireless communication devices 204A, 204B, 204C may be, for example, the wireless communication devices 102A, 102B, 102C shown in fig. 1, or other types of wireless communication devices. The wireless communication devices 204A, 204B, 204C transmit wireless signals through the space 200. The space 200 may be fully or partially enclosed or open at one or more boundaries. In an example, the space 200 may be a sensing space. The space 200 may be or include a room interior, multiple rooms, a building, an indoor area, an outdoor area, and the like. In the example shown, the first wall 202A, the second wall 202B, and the third wall 202C at least partially enclose the space 200.

In the example shown in fig. 2A and 2B, the wireless communication device 204A is operable to transmit wireless signals repeatedly (e.g., periodically, intermittently, at planned, unplanned or random intervals, etc.). The wireless communication devices 204B, 204C are operable to receive signals based on those signals transmitted by the wireless communication device 204A. The wireless communication devices 204B, 204C each have a modem (e.g., modem 112 shown in fig. 1) configured to process the received signals to detect movement of the object in space 200.

As shown, the object is in a first position 214A in fig. 2A, and the object has moved to a second position 214B in fig. 2B. In fig. 2A and 2B, the moving object in the space 200 is represented as a human being, but the moving object may be another type of object. For example, the moving object may be an animal, an inorganic object (e.g., a system, apparatus, device, or component), an object defining all or part of the boundary of the space 200 (e.g., a wall, door, window, etc.), or another type of object.

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

In fig. 2A, along a fifth signal path 224A, wireless signals are transmitted from the wireless communication device 204A and reflected from the object at the first location 214A toward the wireless communication device 204C. Between fig. 2A and 2B, the surface of the object moves from a first position 214A to a second position 214B (e.g., a distance from the first position 214A) in the space 200. In fig. 2B, along a sixth signal path 224B, wireless signals are transmitted from wireless communication device 204A and reflected from the object at second location 214B toward wireless communication device 204C. As the object moves from the first position 214A to the second position 214B, the sixth signal path 224B depicted in fig. 2B is longer than the fifth signal path 224A depicted in fig. 2A. In some examples, signal paths may be added, removed, or otherwise modified as a result of movement of objects in space.

The example wireless signals shown in fig. 2A and 2B may experience attenuation, frequency shift, phase shift, or other effects through their respective paths, and may have portions that propagate in another direction, for example, through the first wall 202A, the second wall 202B, and the third wall 202C. In some examples, the wireless signal is a Radio Frequency (RF) signal. The wireless signals may include other types of signals.

In the example shown in fig. 2A and 2B, the wireless communication device 204A may repeatedly transmit wireless signals. Specifically, fig. 2A illustrates a wireless signal transmitted from the wireless communication device 204A at a first time, and fig. 2B illustrates the same wireless signal transmitted from the wireless communication device 204A at a second, later time. The transmitted signal may be transmitted continuously, periodically, randomly or intermittently, or the like, or a combination thereof. The transmitted signal may have several frequency components in the frequency bandwidth. The transmitted signal may be transmitted from the wireless communication device 204A in an omni-directional manner, a directional manner, or other manner. In the example shown, the wireless signal passes through multiple respective paths in space 200, and the signal along each path may be attenuated by path loss, scattering, reflection, etc., and may have a phase or frequency offset.

As shown in fig. 2A and 2B, the signals from the first through sixth paths 216, 218, 220, 222, 224A, and 224B are combined at the wireless communication device 204C and the wireless communication device 204B to form a received signal. Due to the effects of multiple paths in the space 200 on the transmitted signal, the space 200 may be represented as a transfer function (e.g., a filter), where the transmitted signal is input and the received signal is output. As the object moves in the space 200, the attenuation or phase offset of the signal in the influencing signal path may change and, thus, the transfer function of the space 200 may change. Assuming the same wireless signal is transmitted from wireless communication device 204A, if the transfer function of space 200 changes, the output of the transfer function (the received signal) will also change. The change in the received signal may be used to detect movement of the object.

Mathematically, the transmitted signal f (t) transmitted from the first wireless communication device 204A can be described according to equation (1):

Where ω _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 of transmission of f (t) from the first wireless communication device 204A, the output signal r _k (t) from the path k can be described according to equation (2):

Where α _n,k represents the attenuation factor (or channel response; e.g., due to scattering, reflection, and path loss) of the nth frequency component along k, and φ _n,k represents the phase of the signal of the nth frequency component along k. The signal R received at the wireless communication device may then be described as the sum of all output signals R _k (t) from all paths to the wireless communication device, as shown in equation (3):

R＝∑_kr_k(t)…(3)

Substituting equation (2) into equation (3) yields the following equation (4):

R at the wireless communication device may then be analyzed. For example, R at the wireless communication device may be transformed to the frequency domain using a Fast Fourier Transform (FFT) or another type of algorithm. The transformed signal may represent R as a series of n complex values, each corresponding to a respective frequency component (at n frequencies ω _n). For frequency components at frequency ω _n, the complex value H _n can be expressed in equation (5) as follows:

H _n of a given ω _n indicates the relative amplitude and phase offset of the signal received at ω _n. As the object moves in space, H _n changes as a _n,k of the space changes. Thus, a detected change in the channel response may be indicative of movement of the object within the communication channel. In some examples, noise, interference, or other phenomena may affect the channel response detected by the receiver, and the motion detection system may reduce or isolate such effects to improve the accuracy and quality of the motion detection capability. In some embodiments, the overall channel response may be expressed in equation (6) as follows:

In some examples, the spatial channel response h _c" may be determined, for example, based on estimated mathematical theory. For example, the reference signal R _e$ may be modified with the candidate h _ch, and then the maximum likelihood method may be used to select the candidate channel that best matches the received signal (R _c％&). In some cases, the estimated received signal is obtained from the convolution of R _e$ using candidate h _ch The channel coefficient of h _ch is then changed to makeThe square error of (c) is minimized. This can be shown mathematically in equation (7) as follows:

Wherein the optimization criterion is

The minimization or optimization process may utilize adaptive filtering techniques such as Least Mean Square (LMS), recursive Least Squares (RLS), batch Least Squares (BLS), and the like. The channel response may be a Finite Impulse Response (FIR) filter, an Infinite Impulse Response (IIR) filter, or the like. As shown in the above equation, the received signal may be regarded as a convolution of the reference signal and the channel response. Convolution operation means that the channel coefficients have a degree of correlation with each delayed copy of the reference signal. Thus, the convolution operation shown in the above equation shows that the received signal occurs at different delay points, each weighted by the channel coefficients.

Fig. 3A and 3B are graphs showing examples of channel responses 360, 370 calculated from wireless signals transmitted between wireless communication devices 204A, 204B, 204C in fig. 2A and 2B. Fig. 3A and 3B also illustrate a frequency domain representation 350 of the initial wireless signal transmitted by the wireless communication device 204A. In the example shown, the channel response 360 in fig. 3A represents the signal received by the wireless communication device 204B when there is no motion in the space 200, and the channel response 370 in fig. 3B represents the signal received by the wireless communication device 204B in fig. 2B after the object has moved in the space 200.

In the example shown in fig. 3A and 3B, for purposes of illustration, the wireless communication device 204A transmits a signal having a flat frequency distribution (the amplitude of each frequency component f ₁、f₂ and f ₅ is the same) as shown in the frequency domain representation 350. Due to the interaction of the signal with the space 200 (and objects therein), the signal received at the wireless communication device 204B based on the signal transmitted from the wireless communication device 204A is different from the transmitted signal. In this example, where the transmitted signal has a flat frequency distribution, the received signal represents the channel response of the space 200. As shown in fig. 3A and 3B, the channel responses 360, 370 are different from the frequency domain representation 350 of the transmitted signal. When motion occurs in space 200, the channel response also changes. For example, as shown in fig. 3B, the channel response 370 associated with the motion of the object in space 200 is different from the channel response 360 associated with no motion in space 200.

Further, the channel response may be different from the channel response 370 as the object moves within the space 200. In some cases, the space 200 may be divided into different regions, and the channel responses associated with each region may share one or more characteristics (e.g., shape), as described below. Thus, the motion of the object in different regions can be distinguished and the location of the detected motion can be determined based on analysis of the channel response.

Fig. 4A and 4B are diagrams showing example channel responses 401, 403 associated with movement of an object 406 in different regions 408, 412 of space 400. In the example shown, the space 400 is a building and the space 400 is divided into a plurality of different areas, namely a first area 408, a second area 410, a third area 412, a fourth area 414 and a fifth area 416. In some examples, space 400 may include additional or fewer regions. As shown in fig. 4A and 4B, the area within the space 400 may be defined by walls between rooms. In addition, the area may be defined by ceilings between building floors. For example, the space 400 may include additional floors with additional rooms. Additionally, in some examples, the multiple areas of space may be or include a number of floors in a multi-story building, a number of rooms in a building, or a number of rooms on a particular floor of a building. In the example shown in fig. 4A, the object located in the first region 408 is represented as a person 406, but the moving object may be another type of object, such as an animal or an inorganic object.

In the example shown, wireless communication device 402A is located in a fourth region 414 of space 400, wireless communication device 402B is located in a second region 410 of space 400, and wireless communication device 402C is located in a fifth region 416 of space 400. The wireless communication device 402 may operate in the same or similar manner as the wireless communication device 102 of fig. 1. For example, the wireless communication device 402 may be configured to transmit and receive wireless signals and detect whether motion has occurred in the space 400 based on the received signals. As an example, the wireless communication device 402 may periodically or repeatedly transmit a motion detection signal through the space 400 and receive a signal based on the motion detection signal. The wireless communication device 402 may analyze the received signal to detect whether an object has moved in the space 400, for example, by analyzing a channel response associated with the space 400 based on the received signal. Additionally, in some implementations, the wireless communication device 402 may analyze the received signals to identify the location of the detected motion within the space 400. For example, the wireless communication device 402 may analyze characteristics of the channel responses to determine whether the channel responses share the same or similar characteristics as are known to be associated with the first through fifth regions 408, 410, 412, 414, 416 of the space 400.

In the illustrated example, the wireless communication device(s) 402 repeatedly transmit motion detection signals (e.g., reference signals) through the space 400. In some examples, the motion detection signal may have a flat frequency distribution, where the amplitudes of f ₁、f₂ and f ₅ are the same or nearly the same. For example, the motion detection signal may have a frequency response similar to the frequency domain representation 350 shown in fig. 3A and 3B. In some examples, the motion detection signals may have different frequency distributions. Due to the interaction of the reference signal with the space 400 (and objects therein), a signal received at the other wireless communication device 402 based on the motion detection signal transmitted from the other wireless communication device 402 is different from the transmitted reference signal.

Based on the received signals, the wireless communication device 402 may determine a channel response of the space 400. When motion occurs in different regions within space, different characteristics can be seen in the channel response. For example, while the channel responses may be slightly different for motion within the same region of space 400, the channel responses associated with motion in different regions may generally share the same shape or other characteristics. For example, the channel response 401 of fig. 4A represents an example channel response associated with the movement of the object 406 in the first region 408 of the space 400, while the channel response 403 of fig. 4B represents an example channel response associated with the movement of the object 406 in the third region 412 of the space 400. The channel responses 401, 403 are associated with signals received by the same wireless communication device 402 in the space 400.

Fig. 4C and 4D are graphs showing the channel responses 401, 403 of fig. 4A and 4B superimposed on the channel response 460 associated with no motion occurring in the space 400. In the example shown, the wireless communication device 402 transmits a motion detection signal having a flat frequency distribution as shown in the frequency domain representation 450. When motion occurs in space 400, a change in channel response will occur with respect to channel response 460 associated with no motion, and thus, motion of an object in space 400 may be detected by analyzing the change in channel response. In addition, the relative position of the detected motion within the space 400 may be identified. For example, the shape of the channel response associated with the motion may be compared to reference information (e.g., using a trained Artificial Intelligence (AI) model) to classify the motion as having occurred in a different region of space 400.

When there is no motion in the space 400 (e.g., when the object 406 is not present), the wireless communication device 402 may calculate a channel response 460 associated with the no motion. The channel response may vary slightly due to a number of factors; however, multiple channel responses 460 associated with different time periods may share one or more characteristics. In the example shown, the channel response 460 associated with no motion has a decreasing frequency distribution (each of f ₁、f₂ and f ₅ is less in magnitude than the previous one). In some examples, the distribution of channel responses 460 may be different (e.g., based on different inter-room layouts or placements of wireless communication device 402).

When motion occurs in space 400, the channel response will change. For example, in the example shown in fig. 4C and 4D, the channel response 401 associated with the movement of the object 406 in the first region 408 is different from the channel response 460 associated with no movement, and the channel response 403 associated with the movement of the object 406 in the third region 412 is different from the channel response 460 associated with no movement. The channel response 401 has a concave parabolic frequency distribution (the magnitude of the intermediate frequency component f ₂ is smaller than the outer frequency components f1 and f 3), while the channel response 403 has a convex asymptotic frequency distribution (the magnitude of the intermediate frequency component f2 is larger than the outer frequency components f ₁ and f ₅). In some examples, the distribution of channel responses 401, 403 may be different (e.g., based on different inter-room layouts or placements of wireless communication device 402).

Analyzing the channel response may be considered similar to analyzing a digital filter. The channel response may be formed by reflections of objects in space, and reflections produced by moving or stationary people. When a reflector (e.g., a person) moves, it changes the channel response. This can translate to a change in the equivalent taps of the digital filter, which can be considered to have poles and zeros (poles amplify the frequency components of the channel response and appear as peaks or high points in the response, while zeros attenuate the frequency components of the channel response and appear as valleys, low points or zero values in the response). The varying digital filter may be characterized by the locations of its peaks and valleys, and the channel response may be similarly characterized by its peaks and valleys. For example, in some embodiments, motion may be detected by analyzing the zero values and peaks in the frequency components of the channel response (e.g., by marking their locations on the frequency axis and their magnitudes).

In some embodiments, time-series aggregation may be used to detect motion. The time series aggregation may be performed by observing characteristics of the channel response over a moving window and aggregating the windowed results by using statistical measures (e.g., mean, variance, principal component, etc.). During an instance of motion, the characteristic digital filter features will shift in position and flip between some values due to the continuous change in the scattering scene. That is, an equivalent digital filter exhibits a range of values (due to motion) of its peaks and zeros. By looking at this range of values, a unique distribution (in an example, the distribution may also be referred to as a signature) may be identified for different regions within the space.

In some implementations, the data may be processed using an AI model. AI models can be of various types, such as linear regression models, logistic regression models, linear discriminant analysis models, decision tree models, na iotave bayes models, K-nearest neighbor models, learning vector quantization models, support vector machines, bagging methods (bagging), and random forest models, and deep neural networks. In general, all AI models are intended to learn a function that provides the most accurate correlation between input and output values and is trained using historical input and output sets of known correlations. In an example, artificial intelligence may also be referred to as machine learning.

In some implementations, the distribution of channel responses associated with motion in different regions of the space 400 may be learned. For example, machine learning may be used to classify channel response characteristics based on the movement of objects in different regions of space. In some cases, a user associated with the wireless communication device 402 (e.g., an owner or other occupant of the space 400) may assist in the learning process. For example, referring to the examples shown in fig. 4A and 4B, a user may move in each of the first through fifth regions 408, 410, 412, 414, 416 during a learning phase, and may indicate (e.g., through a user interface on a mobile computing device) that he/she is moving in one of the particular regions in the space 400. For example, as the user moves through the first region 408 (e.g., as shown in fig. 4A), the user may indicate on the mobile computing device that he/she is in the first region 408 (and may name the region as a "bedroom," "living room," "kitchen," or another type of room of a building, as appropriate). As the user moves through the area, a channel response may be obtained and may be "tagged" with a location (area) indicated by the user. The user may repeat the same process for other areas of the space 400. The term "marking" as used herein may refer to marking and identifying the channel response with a location indicated by the user or any other information.

The marked channel responses may then be processed (e.g., by machine learning software) to identify unique characteristics of the channel responses associated with the motion in the different regions. Once identified, the identified unique characteristics can be used to determine the location of the detected motion of the newly calculated channel response. For example, the marked channel response may be used to train the AI model, and once trained, the newly calculated channel response may be input to the AI model, and the AI model may output the location of the detected motion. For example, in some cases, the mean, range, and absolute values are input to the AI model. In some examples, the amplitude and phase of the complex channel response itself may also be input. These values allow the AI model to design any front-end filter to obtain features most relevant for accurate prediction of motion in different spatial regions. In some embodiments, the AI model is trained by performing a random gradient descent. For example, the channel response changes that are most active during a particular region may be monitored during training, and the particular channel changes may be heavily weighted (by training and adapting weights in the first layer to correlate to these shapes, trends, etc.). The weighted channel variation can be used to create a metric that is activated when a user is present in a particular area.

For extracted features, such as channel response nulls and peaks, aggregation within a moving window may be used to create a time series (of nulls/peaks) to take snapshots of a few features in the past and present, and use the aggregate values as input to the network. Thus, the network, while adapting its weights, will attempt to aggregate values in a certain region to cluster them, which can be done by creating a decision plane based on a logical classifier. The decision plane partitions different clusters and subsequent layers may form categories based on a single cluster or a combination of clusters.

In some embodiments, the AI model includes two or more layers of reasoning. The first layer acts as a logical classifier that can divide values of different concentrations into individual clusters, while the second layer combines some of these clusters together, creating categories for different regions. Additional subsequent layers may help extend different regions over clusters of more than two categories. For example, a fully connected AI model may contain an input layer corresponding to the number of features tracked, an intermediate layer corresponding to the number of active clusters (by iterating between selections), and a final layer corresponding to a different region. In the case where complete channel response information is input to the AI model, the first layer may act as a shape filter that may correlate to certain shapes. Thus, a first layer may lock particular shapes, a second layer may generate measures of changes that occur in those shapes, and a third and subsequent layers may create a combination of these changes and map them to different regions within space. The outputs of the different layers may then be combined by the fusion layer.

Example method and apparatus for wi-Fi sensing system

Section B describes systems and methods that may be used with wireless sensing systems configured to send sensing transmissions and make sensing measurements.

Fig. 5 depicts some architectures of an implementation of a system 500 for Wi-Fi sensing according to some embodiments.

The system 500 may include a sensing receiver 502, a sensing transmitter 504, and a network 560 that enables communication among system components for information exchange. The system 500 may be an example or instance of the wireless communication system 100 and the network 560 may be an example or instance of a wireless network or cellular network, the details of which are provided with reference to fig. 1 and the accompanying description thereof.

According to an embodiment, the sensing receiver 502 may be configured to receive a sensing transmission (e.g., from the sensing transmitter 504) and perform one or more measurements (e.g., channel state information) that may be used for Wi-Fi sensing. These measurements may be referred to as sensing measurements. The sensed measurements may be processed to achieve a sensed result of the system 500, such as detecting motion or gestures. In an embodiment, the sensing receiver 502 may be an AP. In some embodiments, the sense receiver 502 may serve the role of a sense initiator.

According to an implementation, the sensing receiver 502 may be implemented by a device such as the wireless communication device 102 shown in fig. 1. In some implementations, the sensing receiver 502 may be implemented by a device such as the wireless communication device 204 shown in fig. 2A and 2B. Further, the sensing receiver 502 may be implemented by a device such as the wireless communication device 402 shown in fig. 4A and 4B. In some embodiments, the sensing receiver 502 may be any computing device, such as a desktop computer, a handheld computer, a tablet computer, a mobile device, a Personal Digital Assistant (PDA), or any other computing device. According to an embodiment, the sensing receiver 502 may be enabled to control measurement activities to ensure that a desired sensing transmission is made at a desired time and to ensure that the sensing measurement is accurately determined. In some embodiments, the sensing receiver 502 may process the sensing measurements to achieve the sensing results of the system 500. In some embodiments, the sensing receiver 502 may be configured to transmit the sensing measurements to the sensing transmitter 504, and the sensing transmitter 504 may be configured to process the sensing measurements to achieve the sensing results of the system 500.

Referring again to fig. 5, in some embodiments, the sensing transmitter 504 may form part of a Basic Service Set (BSS) and may be configured to send a sensing transmission to the sensing receiver 502 based on which one or more sensing measurements (e.g., channel state information) may be performed for Wi-Fi sensing. In an embodiment, the sensing transmitter 504 may be a Station (STA). In an embodiment, the sensing transmitter 504 may be an Access Point (AP). According to an implementation, the sensing transmitter 504 may be implemented by a device, such as the wireless communication device 102 shown in fig. 1. In some implementations, the sensing transmitter 504 may be implemented by a device such as the wireless communication device 204 shown in fig. 2A and 2B. Further, the sensing transmitter 504 may be implemented by a device such as the wireless communication device 402 shown in fig. 4A and 4B. In some embodiments, the sensing transmitter 504 may be any computing device, such as a desktop computer, a handheld computer, a tablet computer, a mobile device, a Personal Digital Assistant (PDA), or any other computing device. In some embodiments, communication between the sensing receiver 502 and the sensing transmitter 504 may occur via a Station Management Entity (SME) and a MAC Layer Management Entity (MLME) protocol.

Referring to fig. 5, in more detail, the sensing receiver 502 can include a processor 508 and a memory 510. For example, the processor 508 and the memory 510 of the sensing receiver 502 can be the processor 114 and the memory 116, respectively, as shown in fig. 1. In an embodiment, the sensing receiver 502 may further include a transmit antenna 512, a receive antenna 514, and a sensing agent 516.

In an embodiment, the sensing agent 516 may be responsible for receiving the sensing transmissions and associated transmission parameters, calculating the sensing measurements, and processing the sensing measurements to complete the sensing results. In some embodiments, receiving the sensing transmissions and associated transmission parameters and calculating the sensing measurements may be performed by an algorithm running in the MAC layer of the sensing receiver 502, and processing the sensing measurements to complete the sensing results may be performed by an algorithm running in the application layer of the sensing receiver 502. In some examples, the algorithm running in the application layer of the sensing receiver 502 is referred to as a sensing application or sensing algorithm. In some embodiments, the algorithm running in the MAC layer of the sensing receiver 502 and the algorithm running in the application layer of the sensing receiver 502 may run separately on the processor 508. In an embodiment, the sensing agent 516 may pass physical layer parameters (e.g., such as channel state information) from the MAC layer of the sensing receiver 502 to the application layer of the sensing receiver 502, and may use the physical layer parameters to detect one or more features of interest. In an example, the application layer may operate on physical layer parameters and form services or features that may be presented to an end user. According to an embodiment, communication between the MAC layer of the sensing receiver 502 and other layers or components may be based on communication interfaces such as an MLME interface and a data interface. According to some embodiments, the sensing agent 516 may contain/perform sensing algorithms. In an embodiment, the sensing agent 516 may process and analyze the sensing measurements using a sensing algorithm and identify one or more features of interest. Further, for Wi-Fi sensing purposes, the sense agent 516 may be configured to determine the number and timing of sense transmissions and sense measurements. In some embodiments, the sensing agent 516 may be configured to transmit the sensing measurements to the sensing transmitter 504 for further processing.

In an embodiment, the sensing agent 516 may be configured to cause at least one of the transmit antennas 512 to transmit a message to the sensing transmitter 504. Further, the sensing agent 516 may be configured to receive messages from the sensing transmitter 504 via at least one of the receive antennas 514. In an example, the sensing agent 516 may be configured to make a sensing measurement based on one or more sensing transmissions received from the sensing transmitter 504.

Referring again to fig. 5, the sensing receiver 502 can include a data storage 518. In an embodiment, the data storage 518 may store a series of amplitudes of the characteristic pulses in the time domain pulse set. In an example, the data storage 518 may store the base amplitude, and waveform amplitude variations of the characteristic pulse at different sensing measurement times (also referred to as time stamps), such as t ₁、t₂、……、t_l、……、t_N. The information stored in the data store 518 may be updated periodically or dynamically as needed. In an implementation, the data storage 518 may include any type or form of storage, such as a database or file system coupled to the memory 510.

Referring again to fig. 5, the sensing transmitter 504 can include a processor 528 and a memory 530. For example, the processor 528 and the memory 530 of the sensing transmitter 504 may be the processor 114 and the memory 116, respectively, as shown in fig. 1. In an embodiment, the sensing transmitter 504 may further include a transmit antenna 532, a receive antenna 534, and a sensing agent 536. In an embodiment, the sensing agent 536 may be a block that passes physical layer parameters from the MAC of the sensing transmitter 504 to the application layer program. The sensing agent 536 may be configured to cause at least one of the transmit antennas 532 and at least one of the receive antennas 534 to exchange messages with the sensing receiver 502.

In an embodiment, the sensing agent 536 may be responsible for receiving the sensing measurements and associated transmission parameters, calculating the sensing measurements, and/or processing the sensing measurements to complete the sensing results. In some embodiments, receiving the sensing measurements and associated transmission parameters and calculating the sensing measurements and/or processing the sensing measurements may be performed by an algorithm running in the MAC layer of the sensing transmitter 504, and processing the sensing measurements to complete the sensing results may be performed by an algorithm running in the application layer of the sensing transmitter 504. In some examples, the algorithm running in the application layer of the sensing transmitter 504 is referred to as a sensing application or sensing algorithm. In some embodiments, the algorithm running in the MAC layer of the sensing transmitter 504 and the algorithm running in the application layer of the sensing transmitter 504 may run separately on the processor 528. In an embodiment, the sensing agent 536 may pass physical layer parameters (e.g., such as channel state information) from the MAC layer of the sensing transmitter 504 to the application layer of the sensing transmitter 504 and may use the physical layer parameters to detect one or more features of interest. In an example, the application layer may operate on physical layer parameters and form services or features that may be presented to an end user. According to an embodiment, communication between the MAC layer of the sensing transmitter 504 and other layers or components may be based on communication interfaces such as an MLME interface and a data interface. According to some implementations, the sensing agent 536 may include/perform a sensing algorithm. In an embodiment, the sensing agent 536 may process and analyze the sensed measurements using a sensing algorithm and identify one or more features of interest. Further, for Wi-Fi sensing purposes, the sensing agent 536 may be configured to determine the number and timing of sensing transmissions and sensing measurements.

In some embodiments, antennas may be used for transmission and reception in a half-duplex format. When an antenna transmits, the antenna may be referred to as a transmit antenna 512/532, and when the antenna receives, the antenna may be referred to as a receive antenna 514/534. Those of ordinary skill in the art will appreciate that the same antenna may be a transmit antenna 512/532 in some examples and a receive antenna 514/534 in other examples. In the case of an antenna array, for example in a beamforming environment, one or more antenna elements may be used to transmit or receive signals. In some examples, a set of antenna elements for transmitting the composite signal may be referred to as transmit antennas 512/532 and a set of antenna elements for receiving the composite signal may be referred to as receive antennas 514/534. In some examples, each antenna is equipped with its own transmit and receive paths, which paths may be alternately switched to connect to the antennas depending on whether the antenna is operating as a transmit antenna 512/532 or as a receive antenna 514/534.

In accordance with one or more embodiments, communications in the network 560 may be managed by one or more of the IEEE developed 802.11 family of standards. Some example IEEE standards may include IEEE 802.11-2020, IEEE802.11ax-2021, IEEE802.11 me, IEEE802.11 az, and IEEE802.11 be. IEEE 802.11-2020 and IEEE802.11ax-2021 are fully approved standards, whereas IEEE802.11 me reflects continuous maintenance updates to the IEEE 802.11-2020 standard, and IEEE802.11 be defines the next generation standard. IEEE802.11 az is an extension of the IEEE 802.11-2020 and IEEE802.11ax-2021 standards, adding new functionality. In some embodiments, communications may be managed by other standards (other or additional IEEE standards or other types of standards). In some embodiments, portions of network 560 that system 500 does not require to be managed by one or more of the 802.11 family of standards may be implemented by instances of any type of network, including wireless networks or cellular networks.

Referring to fig. 5, the role of the sense initiator may be assumed by the sense receiver 502 for Wi-Fi sensing purposes, in accordance with one or more embodiments. In an implementation, a sensing configuration message may be sent to the sensing receiver 502 via a networked device. In an example, the sensing configuration message may contain a channel representation information configuration. In response to the sensing configuration message, the sensing receiver 502 may send an acknowledgement using the sensing configuration response message and configure itself with the channel representation information configuration for time domain channel representation information (TD-CRI) or filtered TD-CRI. Thereafter, in an example, the sensing receiver 502 can initiate a sensing session and send a sensing trigger message requesting a sensing transmission to the sensing transmitter 504. The sense transmitter 504 may then send a sense transmission to the sense receiver 502 in response to the sense trigger message. Upon receiving the sensing transmission, the sensing receiver 502 can perform channel state measurements on the received sensing transmission and generate channel representation information using the channel representation information configuration. In an example, the sensing receiver 502 can generate a TD-CRI or a filtered TD-CRI. Further, the sensing receiver 502 can send CRI transfer messages including channel state measurements (i.e., TD-CRI or filtered TD-CRI) to networked devices for further processing.

According to some embodiments, the role of the sense initiator may be assumed by the sense transmitter 504. In an implementation, the sensing configuration message may be sent to the sensing transmitter 504 via a networked device. In an example, the sensing configuration message may contain a channel representation information configuration. In response to the sensing configuration message, the sensing transmitter 504 may send an acknowledgement using the sensing configuration response message. Thereafter, in an example, the sensing transmitter 504 may initiate a sensing session and send a sensing transmission notification message and an immediately subsequent sensing transmission NDP to the sensing receiver 502. In an example, the sensing transmission notification message may contain a channel representation information configuration, and in an example, the sensing receiver may configure itself with the channel representation information configuration for generating the TD-CRI or the filtered TD-CRI. In an example, the sensing transmission NDP follows a sensing transmission notification message after one SIFS. In an example, the duration of SIFS is 10 μs. The sensing receiver 502 may perform channel state measurements on the sensing transmission NDPs and generate channel representation information based on the channel representation information configuration. In an example, the sensing receiver 502 can generate a TD-CRI or a filtered TD-CRI. The sensing receiver 502 can send CRI transfer messages including channel state measurements (i.e., TD-CRI or filtered TD-CRI) to networked devices for further processing.

In an example, the sensing receiver 502 may maintain channel state measurements until it receives a sensing measurement poll message. The sense transmitter 504 may send a sense measurement poll message to the sense receiver 502, which may trigger the sense receiver 502 to send formatted channel state measurements (i.e., channel state information, TD-CRI, or filtered TD-CRI) to the sense transmitter 504. In another example, the sensing transmitter 504 can send a sensing measurement poll message to the sensing receiver 502, the sensing measurement poll message including a channel representation information configuration. The sense measurement poll message may trigger the sense receiver 502 to generate a TD-CRI or filtered TD-CRI according to the channel representation information configuration and transmit the TD-CRI or filtered TD-CRI to the sense transmitter 504. In an example, the sensing receiver 502 can send a CRI transfer message containing a channel state measurement (i.e., TD-CRI or filtered TD-CRI) to the networked device.

Some embodiments of the present disclosure as described above define a sense message type for Wi-Fi sensing, such as a sense configuration message and a sense configuration response message. In an example, the sensing configuration message and the sensing configuration response message are carried in a new extension of a management frame of the type described in IEEE 802.11. Fig. 6 shows an example of components of a management frame 600 carrying a sensing transmission. In an example, the system 500 may require an acknowledgement frame and the management frame carrying the sense message may be implemented as an Action frame, and in another example, the system 500 may not require an acknowledgement frame and the management frame carrying the sense message may be implemented as an Action No acknowledgement (Action No Ack) frame.

In an embodiment, the information content of all sensed message types may be carried in a format as shown in fig. 6. In some examples, the transmission configuration, timing configuration, steering matrix configuration, and TD-CRI configuration as described in fig. 6 are implemented as IEEE 802.11 elements. In some examples, the TD-CRI configuration element is part of a transport configuration element.

In one or more embodiments, according to some embodiments, the sensed message types may be identified by a message type field, and each sensed message type may carry other identified elements. In an example, data may be encoded into elements for inclusion in sensing messages between the sensing receiver 502, the sensing transmitter 504, and the networked device. In measurement activities involving multiple sensing receivers and multiple sensing transmitters, these parameters may be defined for all sensing receiver-sensing transmitter pairs. In an example, when these parameters are transmitted from the networked device to the sensing receiver 502, these parameters configure the sensing receiver 502 to process the sensing transmission and calculate the sensing measurement. In some examples, these parameters report the configuration used by the sensing receiver 502 when transmitted from the sensing receiver 502 to the networked device.

According to some implementations, the sensing transmission notification may be carried in a new extension of a control frame of the type described in IEEE 802.11. In some implementations, the sense transmission notification may be carried in a new extension of the control frame extension described in IEEE 802.11. Fig. 7A shows an example of a format of the control frame 700, and fig. 7B shows a format of a sensing transmission control field of the control frame 700. In an example, STA info fields that sense transmission control fields may address up to n sensing receivers via their Association IDs (AID). In an example embodiment, the sensing transmission notification may address n sensing receivers that need to make sensing measurements and relay channel representation information back to the sensing initiator.

According to some implementations, the sensing measurement poll may be carried in a new extension of a control frame of the type described in IEEE 802.11. In some implementations, the sensing measurement poll may be carried in a new extension of the control frame extension described in IEEE 802.11. Fig. 8A shows an example of a format of a control frame 800, and fig. 8B shows a format of a sensing measurement control field of the control frame 800.

According to some implementations, when the sensing receiver 502 has calculated the sensing measurements and created the channel representation information (e.g., in the form of a TD-CRI), the sensing receiver 502 may need to transmit the channel representation information to the sensing transmitter 504 or via a networked device. In an example, the TD-CRI may be transmitted through a management frame. In an example, a message type can be defined that represents a CRI transfer message.

Fig. 9 illustrates an example of components of a management frame 900 carrying CRI transfer messages, in accordance with some embodiments. In an example, the system 500 may require an acknowledgement frame and the management frame carrying the CRI transfer message may be implemented as an action frame, and in another example, the system 500 may not require an acknowledgement frame and the management frame carrying the CRI transfer message may be implemented as an action unacknowledged frame.

In an embodiment, when the networked device is implemented on a separate device (i.e., not implemented within the sensing receiver 502 or the sensing transmitter 504), the management frame may not be necessary, and the TD-CRI may be encapsulated in a standard IEEE802.11 data frame and transmitted to the networked device. In an example, a proprietary header or descriptor can be added to the data structure to allow detection via the networking device that the data structure is in the form of CRI transfer message elements. In an example, the data can be transmitted in the format shown in fig. 9, and the networked device can be configured to interpret a message type value representing a CRI transfer message element.

C. system and method for identifying waveform frequency characteristics using time stamps

Currently, wi-Fi sensing systems may detect small movements of an object present between a sensing transmitter and a sensing receiver. Furthermore, if the small motions have waveform frequency characteristics (i.e., periodic similar motions along the timeline, such as movement of human breath or movement of a small pump), a more accurate Wi-Fi sensing system may be further needed to identify the waveform frequency characteristics of the small motions. In an example, the waveform frequency characteristics of small movements of the object in the sensing space may be beneficial for certain applications, such as home monitoring, assisted living, safety monitoring, etc. In an example, waveform frequency characteristics of small movements (e.g., breathing) of a human being in sleep may help to identify whether the breathing of the human being is normal.

In an example, reflections of the time domain pulses between the sensing transmitter and the sensing receiver may generate multipath signals at the sensing receiver. The multipath signals at the sensing receiver may have different amplitudes and time delays. When the movement of the object reflects the temporal pulses in its path, small repeated (e.g., periodic) movements of the object in the path of the reflected temporal pulses may cause amplitude modulation of the received temporal pulses. In wireless telecommunications, multipath is a propagation phenomenon that contributes to a radio signal reaching a receiving antenna through two or more paths.

The present disclosure describes a solution for identifying waveform frequency characteristics (or frequencies) of small movements of an object in the path of a multipath signal between a sensing transmitter and a sensing receiver. According to an embodiment, waveform frequency characteristics of small movements of an object may be identified by: the method includes detecting waveform amplitude variations (or modulations) over the fundamental amplitude of time domain pulses received from a series of multipath signals at different time stamps, and finding a maximum correlation between a reasonable set of frequencies and the waveform amplitude variations detected across the time series of multipath signals.

Referring to fig. 5, according to one or more embodiments, a sensing receiver 502 or a sensing transmitter 504 may initiate a measurement activity (or Wi-Fi sensing session) for Wi-Fi sensing purposes. During measurement activities, a transmission exchange between the sensing receiver 502 and the sensing transmitter 504 may occur. In an example, the MAC layer of the IEEE 802.11 stack may control these transmissions.

According to an example embodiment, the sensing receiver 502 may initiate measurement activities via one or more sensing trigger messages. In an embodiment, the sensing agent 516 may be configured to generate a sensing trigger message configured to trigger a series of sensing transmissions from the sensing transmitter 504. In an example, the sensing trigger message may contain a requested transmission configuration field. Other examples of information/data contained in the sensing trigger message not discussed herein are contemplated herein. According to an embodiment, the sensing agent 516 may transmit a sensing trigger message to the sensing transmitter 504. In an embodiment, the sensing agent 516 may transmit a sensing trigger message to the sensing transmitter 504 via the transmitting antenna 512 to trigger a series of sensing transmissions from the sensing transmitter 504.

The sensing transmitter 504 may be configured to receive the sensing trigger message from the sensing receiver 502 via the receiving antenna 534. In response to receiving the sensing trigger message, the sensing transmitter 504 may generate a series of sensing transmissions. In an example, one or more of the series of sense transmissions triggered by the sense trigger message from the sense transmitter 504 may include a sense response message. In an embodiment, the sensing transmitter 504 may use the requested transmission configuration to generate one or more transmissions in the series of sensing transmissions. In an embodiment, the sense transmitter 504 may transmit one or more sense transmissions of the series of sense transmissions to the sense receiver 502 in response to the sense trigger message and according to the requested transmission configuration. In an implementation, the sensing transmitter 504 may transmit the series of sensing transmissions to the sensing receiver 502 via the transmit antenna 532.

In an embodiment, the sense receiver 502 may receive the series of sense transmissions transmitted in response to the sense trigger message from the sense transmitter 504. The sensing receiver 502 can be configured to receive the series of sensing transmissions from the sensing transmitter 504 via the receiving antenna 514. According to an embodiment, the sensing agent 516 may be configured to generate a series of sensing measurements based on the series of sensing transmissions received from the sensing transmitter 504. Further, the sensing agent 516 may be configured to determine a plurality of channel representation information based on the series of sensing measurements. In an embodiment, the plurality of channel representation information may include complete time domain channel representation information (TD-CRI) or filtered TD-CRI. According to an example, the plurality of channel representation information may comprise a series of sets of time domain pulses. In an example, the plurality of channel representation information may be calculated by a baseband processor in the sensing receiver 502 as part of normal signal processing performed when receiving the series of sensing transmissions. In an example embodiment, the sensing agent 516 may calculate the TD-CRI using an inverse fourier transform, such as an Inverse Discrete Fourier Transform (IDFT) or an Inverse Fast Fourier Transform (IFFT).

According to some embodiments, the sensing agent 516 may transmit the plurality of channel representation information to the sensing transmitter 504 for further processing. In an embodiment, the sensing agent 516 may transmit the plurality of channel representation information to the sensing transmitter 504 via a Channel Representation Information (CRI) transmission message. According to an embodiment, the sensing agent 516 can transmit the CRI transmit message to the sensing transmitter 504 via the transmit antenna 512.

In the time domain, the transmission channel may be referred to as h (t). The transmission channel may also be described as an impulse response of the transmission channel. The impulse response of the transmission channel may comprise a plurality of time domain pulses. The plurality of time domain pulses may represent reflections experienced by a transmitted signal (e.g., a signal transmitted by a transmitter) before reaching a receiver. The reflected time domain pulse can be expressed as:

h(τ_k)＝α_kδ(t-τ_k)…(8)

Where τ _k represents the time delay of the reflected time domain pulse when it reaches the receiver compared to the non-reflected line of sight time domain pulse, and α _k is a complex value representing the frequency independent attenuation and phase of the reflected time domain pulse.

Fig. 10 depicts an example representation 1000 of an air transmission channel including direct signal paths and single multipaths, in accordance with some embodiments. In an implementation, fig. 10 depicts discrete multipath of time domain pulses δ (t) between a sensing transmitter 1004 and a sensing receiver 1002 according to some embodiments. In fig. 10, the direct signal path is represented as:

h(τ_>)＝α_>δ(t-τ_>)…(9)

And the first reflected time domain pulse is represented as:

h(τ₁)＝α₁δ(t-τ₁)…(10)

in addition to its line-of-sight path, the time domain pulse δ (t) undergoes a single reflection (due to reflector 1006). The line-of-sight time domain pulse transmission time may be incorporated into the complex coefficient α ₀ (i.e., τ ₀ =0). The reflected time domain pulse may experience a delay τ ₁, which represents the amount of time after receiving the line-of-sight time domain pulse to receiving the reflected time domain pulse.

In an embodiment, if the number of discrete time domain pulses in the multipath signal is given by L _A, the received multipath time domain pulse may be expressed as:

The time domain representation of the received multipath signal may be referred to as a TD-CRI. In an example, equation (11) indicates each transmission channel that may include several time domain pulses. The temporal pulses from among the temporal pulses may be determined as line-of-sight temporal pulses. Furthermore, each time domain pulse may have a frequency independent amplitude and phase component (referred to as complex coefficients), and all time domain pulses except the line-of-sight time domain pulse may experience a time delay due to reflection, which contributes a frequency dependent component to the complex coefficients.

According to an embodiment, the filtered TD-CRI may be created by maintaining a portion of the time domain pulses (e.g., time domain pulses having a minimum amplitude and/or within a time delay window). Each of the time domain pulses in the steady-state channel or pseudo steady-state channel may have a steady-state amplitude (referred to as a base amplitude) and a time delay. Furthermore, the amplitude of the time domain pulses of the filtered TD-CRI at different sensing measurement times may be variable, for example due to noise or due to movement of the object.

Fig. 11 depicts an example representation 1100 of the amplitude and time delay of a multipath time domain pulse at the sensing receiver 502, in accordance with some embodiments. In fig. 11, reference numeral "1102" denotes a line-of-sight time domain pulse, and reference numeral "1104" denotes a time domain pulse with maximum (or maximum) amplitude of the filtered TD-CRI. In the example of fig. 11, the time delay of the line-of-sight time domain pulse refers to zero time delay as previously described. In the example of fig. 11, the line-of-sight pulse has a maximum amplitude, and the reflected time domain pulse is shown to have a lower amplitude.

Fig. 12 depicts an example representation 1200 of the amplitude of a received multipath signal with a single reflected time domain pulse altered or modulated by the motion of an object, in accordance with some embodiments. In fig. 12, a line-of-sight temporal pulse (represented by reference numeral "1202") and a reflected temporal pulse (represented by reference numeral "1204") are shown, wherein the amplitude of the reflected temporal pulse varies over time due to the movement of an object in the reflected transmission path. Further, reference numeral "1206" represents an amplitude variation of the reflected time domain pulse over time.

For ease of explanation and understanding, the line-of-sight time domain pulse may be referred to hereinafter as "pulse 0", and the time domain pulse having the greatest amplitude may be referred to hereinafter as "pulse k".

Referring back to fig. 5, once the sensing agent 516 determines the plurality of channel representation information (i.e., the series of time domain pulse sets), the sensing agent 516 may identify characteristic pulses that occur in the time domain pulse sets. Examples are described below in which the sense agents 516 identify characteristic pulses that occur in a time domain set of pulses. In an embodiment, the identification process of the characteristic pulse may be described in two phases, training phase 1 and training phase 2 (collectively referred to as training phases).

A) Training phase 1

In an embodiment, in training phase 1, there is no movement of the object in any reflected transmission path and only noise of the sensing space is considered. Such a scenario is considered to be a steady or pseudo steady state of the sensing space.

In training phase 1, the amplitudes of the time domain pulses (e.g., pulse k as depicted in fig. 11) are measured at the sense receiver 502 at different sense measurement times, e.g., t ₁、t₂、……、t_l、……、t_N (i.e., in a series of time domain pulse sets), wherein the sense measurement times are not necessarily equidistant. In an embodiment, the amplitude of pulse k at sensing measurement time t _l may be expressed mathematically as:

A(t_l)…(12)

furthermore, the fundamental amplitude of pulse k over N time samples can be expressed mathematically as:

In an embodiment, the waveform amplitude change of pulse k from fundamental amplitude a _{basic, basic} at sensing measurement time t _l can be expressed mathematically as:

a(t_l)＝A(t_l)-A_{basic, basic}…(14)

In an embodiment, the maximum waveform amplitude variation of pulse k over the sensing measurement time (e.g., t ₁、t₂、……、t_l、……、t_N) can be expressed mathematically as:

a _max =maximum value { |a (t ₁)|,|a(t₂)|,…,|a(t_l)|,…,|a(t_N) | } … (15)

According to an embodiment, the percentage of change in waveform amplitude of pulse k may be expressed mathematically as:

a% _noise=a _max/A_{basic, basic} … (16)

In an embodiment, in training phase 1, the fundamental amplitude a _{basic, basic} of pulse k may be nearly constant and the percentage of change in waveform amplitude a% _noise of pulse k may be very small. In an example, the percentage of change in waveform amplitude of pulse k may be referred to as a noise floor.

B) Training phase 2

In an embodiment, in training phase 2, there is motion of the object (in addition to noise of the sensing space) in any reflected transmission path. In training phase 2, the fundamental amplitude a _{basic, basic} of pulse k is the same as in training phase 1, since noise in the sensing space is still present.

In an embodiment, if the amplitude of a time domain pulse (e.g., pulse k) is measured at the sensing receiver 502 at a different sensing measurement time, such as t ₁、t₂、……、t_l、……、t_N, the change in waveform amplitude of pulse k at measurement time t _l can be expressed mathematically as:

a(t_l)＝A(t_l)-A_{basic, basic}…(17)

Further, the maximum waveform amplitude variation of pulse k in a sensing measurement time, such as t ₁、t₂、……、t_l、……、t_N, can be expressed mathematically as:

a _max =maximum { |a (t ₁)|,|a(t₂)|,…,|a(t_l)|,…,|a(t_N) | } … (18)

a% _object=a _max/A_{basic, basic} … (19)

In an embodiment, the percentage of change a% of waveform amplitude of pulse k caused by the motion of the object (and noise of the sensing space) the object may be greater than the percentage of change a% of waveform amplitude caused by noise alone. In an example, when the percentage of waveform amplitude change a% _ object of pulse k is greater than the percentage of waveform amplitude change a% _ noise, the waveform amplitude change of pulse k may be higher than the noise floor. In an example, the pulse k waveform amplitude change percentage a_object must be greater than the waveform amplitude change percentage a_noise by a minimum threshold value so that the pulse k waveform amplitude change percentage a_object is considered to be a waveform amplitude change above the noise floor.

According to an embodiment, a large amount of small movements of the object in the sensing space may have waveform frequency characteristics. In an example, a small motion may be considered to have a waveform frequency characteristic if a parameter of the motion of the object (e.g., displacement) varies along the timeline in a waveform (or sinusoidal form) with periodic properties at a particular frequency. For example, respiratory movements of a static person (e.g., a sleeping person) may have a waveform frequency characteristic, because the displacement of respiratory movements of a human chest varies in a waveform (which may be sinusoidal) along a timeline with a substantially periodic nature at a particular frequency. In an example, if a small motion of the object (e.g., respiratory movement of a static person) with waveform frequency characteristics is in the path of reflected time domain pulses of the transmitted signal, it may cause waveform amplitude variations (or modulations) of one or more of the reflected time domain pulses at the sensing receiver.

In an implementation, the waveform amplitude variation (or modulation) of the reflected time domain pulse may be sampled at the sensing receiver at time intervals corresponding to the sensing measurement time. The time intervals of the sensing measurement time at the sensing receiver may be uniform (equidistant) or non-uniform based on the degree of regularity that a successful sensing measurement may be made. According to an embodiment, the fundamental waveform frequency characteristics of small motions may be identified from these uniform or non-uniform time intervals at which sensing measurements are made of the reflected time domain pulses of the sensing transmission.

Fig. 13 depicts an example representation 1300 of small motions with waveform frequency features in the path of the system 500, in accordance with some embodiments. According to an implementation, a wireless signal may propagate in a transmission channel between sensing transmitter 1304 and sensing receiver 1302, and there may be multiple propagation paths, producing multiple time domain pulses at the sensing receiver. Fig. 13 depicts three propagation paths (i.e., three time domain pulses) between the sense transmitter 1304 and the sense receiver 1302. In an example, the three time-domain pulses include a line-of-sight time-domain pulse (represented by reference numeral "1308"), a first reflected time-domain pulse (represented by reference numeral "1310"), and a second reflected time-domain pulse (represented by reference numeral "1312"). As depicted in fig. 13, a first reflected time domain pulse is generated due to reflector 1306 in the transmission channel. Furthermore, there is small movement of the object 1314 (e.g., respiratory movement of a static person) in the path of the second reflected time domain pulse. Small movements of object 1314 may cause waveform amplitude variations of the characteristic pulse, which may be a second reflected time domain pulse or may be a different time domain pulse, depending on how the reflected signals combine constructively or destructively at the receiver. In an embodiment, a small motion of the object 1314 may be considered to have a waveform frequency characteristic if a parameter (e.g., displacement) of the small motion of the object 1314 changes in waveform (or sinusoidal form) along the timeline with a substantially periodic nature at a particular frequency.

According to an embodiment, the TD-CRI may be used to represent received multipath signals in the time domain. Among the received time-domain pulses in the multipath signal, one or more of the received time-domain pulses may have a waveform amplitude variation (or modulation) above a background noise above a base amplitude. Such received time domain pulses may be referred to as wobble pulses. Further, among the wobble pulses having waveform amplitude variation higher than the noise floor, if one specific wobble pulse has a maximum value waveform amplitude variation percentage "a%" during a sensing measurement time such as t ₁、t₂、……、t_l、……、t_N or the like, it may be a characteristic pulse. In an embodiment, the characteristic pulse may represent a plurality of corresponding pulses, each of the corresponding pulses occurring in a respective one of a set of time domain pulses occurring at each of the sensing measurement times t ₁、t₂、……、t_N. In an embodiment, the sense agent 516 may select received time domain pulses (i.e., characteristic pulses) from among the wobble pulses that exhibit amplitude variations across the set of time domain pulses (interchangeably referred to as waveform amplitude variations). In an implementation, the sense agent 516 may select a received temporal pulse (i.e., a characteristic pulse) from among the wobble pulses that has the largest amplitude variation across the set of temporal pulses.

In an embodiment, the time interval of the sensing measurement time at the sensing receiver 502 may be uniform or non-uniform. According to an embodiment, the waveform amplitude variation of the characteristic pulse may represent a waveform frequency characteristic caused by small movements (e.g., human respiration). In an embodiment, the amplitude of the characteristic pulse may have different absolute values at different sensing measurement times, and the amplitude variation of the characteristic pulse may have different values at different sensing measurement times.

Fig. 14 depicts an example representation 1400 of the amplitude of a received time domain pulse including a characteristic pulse, in accordance with some embodiments. Specifically, fig. 14 depicts the amplitudes of the characteristic pulses. In fig. 14, reference numeral "1402" denotes a characteristic pulse (also referred to as a wobble pulse k). The characteristic pulse may have a waveform amplitude variation. As shown in fig. 14, the waveform amplitude variations of the characteristic pulses may vary in a generally sinusoidal manner over time, however, these amplitude variations may be measured at irregular intervals, as the measurement time depends on when the sense transmitter is capable of sensing transmissions to the sense receiver.

15A, 15B, and 15C depict example representations of the amplitudes of received time domain pulses containing characteristic pulses at different sensing measurement times, according to some embodiments. In particular, fig. 15A, 15B, and 15C depict example representations of amplitudes of a characteristic pulse (represented by reference numeral "1502") at different sensing measurement times, in accordance with some embodiments. In particular, fig. 15A depicts the amplitude of the characteristic pulse at the sensing receiver 502 at the sensing measurement time t ₁. Fig. 15B depicts the amplitude of the characteristic pulse at the sensing receiver 502 at the sensing measurement time t ₂. Fig. 15C depicts the amplitude of the characteristic pulse at the sensing receiver 502 at the sensing measurement time t _N. As can be seen from fig. 15A, 15B and C, the amplitude variation of the characteristic pulse is captured or sampled at the moment the sensing measurement is made, depending on the timing of the sensing transmission. Each sensing measurement results in a discrete amplitude measurement of the characteristic pulse.

Referring back to FIG. 5, after identification of the characteristic pulses, the sense agent 516 may store or record a series of amplitudes of the characteristic pulses in the time domain pulse set as a function of the sensed measurement time at which the amplitudes were recorded. In an embodiment, the sense agent 516 may determine the value of the amplitude of the characteristic pulse (e.g., swing pulse k) at different sense measurement times, such as t ₁、t₂、……、t_l、……、t_N, at training phase 1 and training phase 2. As indicated previously, the intervals of the sensing measurement time at the sensing receiver 502 may be uniform or non-uniform based on the successful sensing measurement time. In an embodiment, the sensing agent 516 may record the series of amplitudes of the characteristic pulses in the time domain pulse set (i.e., different absolute values of absolute values determined at different sensing measurement times) in the data store 518 along with the measurement times. According to an embodiment, the sense agent 516 may record changes in the amplitudes of the characteristic pulses (i.e., values representing the differences between the absolute amplitude values and the fundamental amplitudes of the characteristic pulses in the time domain pulse set) determined at different sense measurement times in the data storage 518 along with the measurement times. In an example, the series of amplitudes or amplitude variations of the characteristic pulse may have uniform timing between each point in the series. In some examples, the series of amplitudes or amplitude variations of the characteristic pulse may have non-uniform timing between each point in the series. Further, the timing between the series of amplitudes or amplitude variations may be based on the timing of at least one of the sensing transmission and the sensing measurement. In some embodiments, the sense agent 516 may record or store the fundamental amplitude "A _{basic, basic}", amplitude "A (t _l)" and waveform amplitude variation "a (t _l)" of the characteristic pulse at different sense measurement times in the data storage 518.

Examples of the fundamental amplitude "a _{basic, basic}", amplitude "a (t _l)" and waveform amplitude variation "a (t _l)" of the characteristic pulses at different sensing measurement times stored in the data storage 518 are shown in table 1 provided below.

Table 1: examples of the fundamental amplitude "A _{basic, basic}", amplitude "A (t _l)" and waveform amplitude variation "a (t _l)" of the characteristic pulse at different sensing measurement times stored in the data storage 518

Fig. 16 depicts an example representation 1600 of the series of waveform amplitude variations of a characteristic pulse in accordance with some embodiments. In fig. 16, reference numeral "1602" denotes a waveform frequency characteristic of a small motion of an object. Further, fig. 16 depicts changes in the amplitude of the characteristic pulse (waveform amplitude change of the characteristic pulse relative to the base amplitude) recorded at different sensing measurement times, such as t ₁ (denoted by reference numeral "1604"), t ₂ (denoted by reference numeral "1606"), … …, t _N (denoted by reference numeral "1608"). Further, reference numeral "1610" denotes a waveform amplitude variation of the characteristic pulse at the sensing measurement time t ₂, and reference numeral "1612" denotes a waveform amplitude variation of the characteristic pulse at the sensing measurement time t _N.

Referring back to fig. 5, in identifying the series of waveform amplitude variations that occur in and create the characteristic pulses in the time-domain pulse set, the sense agent 516 may identify waveform frequency features of small movements of the object (e.g., respiratory movements of a human) that occur in the sensing space corresponding to the sense receiver 502 based on the series of waveform amplitude variations of the characteristic pulses. In an example, the sensing space may correspond to a transmission path between the sensing transmitter 504 and the sensing receiver 502. In an embodiment, the sensing agent 516 may identify the waveform frequency signature of a small motion by evaluating the series of waveform amplitude variations s of the signature pulse relative to a reasonable frequency waveform. According to an embodiment, the sensing agent 516 may create a fourier basis function from the series of waveform amplitude variations and the reasonable frequency waveform of the signature.

Examples of waveform frequency characteristics in which the sensing agent 516 identifies small movements of an object occurring in a sensing space corresponding to the sensing receiver 502 are described in more detail below.

In an embodiment, the sensing agent 516 may determine a reasonable set of frequencies of waveform frequency characteristics of small movements. In an example, the normal human respiratory rate when an adult is at rest may range from 10 to 20 breaths per minute (60 s). If a reasonable range of 10 to 20 breaths per minute (60 s) is considered, the physiologically reasonable frequency range of human breath may be in the range of 0.166Hz (10/60 s) to 0.333Hz (20/60 s). An example of a reasonable frequency set of waveform frequency characteristics of human respiration is listed in table 2 (provided below), with a predefined accuracy resolution epsilon (e.g., epsilon=0.01 Hz).

Table 2: an example of a reasonable frequency set of waveform frequency characteristics of human breath, with a predefined accuracy resolution epsilon = 0.01Hz

In table 2 above, the set of reasonable frequencies (f _i) of the waveform frequency characteristics of human breath is defined as (f ₁、f₂、f₃、……、f₁₅、f₁₆、f₁₇、f₁₈). In an example, the predefined accuracy resolution may provide a way to list frequency values within a reasonable range of waveform frequency characteristics for further processing. In an example, the predefined accuracy resolution may have different values (e.g., ε=0.01 Hz, 0.001Hz, 0.002Hz, 0.0001Hz, etc.).

According to an embodiment, the sensing agent 516 may create a fourier basis function from the series of waveform amplitude variations of the signature pulses and their associated time stamps and reasonable frequency sets. In an embodiment, the sensing agent 516 may create a fourier basis function based on the base amplitude "a _{basic, basic}", amplitude "a (t _l)" and waveform amplitude variation "a (t _l)" of the characteristic pulse at different sensing measurement times stored in the data storage 518 and as shown in table 1.

In an embodiment, a fourier basis function may be used to determine a frequency value from a reasonable set of frequencies that best represents the waveform frequency characteristics of small movements (e.g., human respiration).

According to an embodiment, the fourier basis function is expressed mathematically as follows.

Where D (f _j) represents the fourier basis function at frequency f _j, a (t _l) represents the waveform amplitude variation of the characteristic pulse at t _l, t _l represents the sensing measurement time at the sensing receiver 502, and f _j represents the frequency from the reasonable set of frequencies.

According to an embodiment, the sensing agent 516 may be configured to perform multiplication and addition with a fourier basis function, such as calculating an intensity metric (i.e., ||d (f _j) |) for a particular frequency (f _j) from a reasonable set of frequencies for a series of sensing measurement times. In an embodiment, the calculation of the intensity metric for a particular frequency from the reasonable set of frequencies is expressed mathematically as follows.

Where ||d (f _j) || represents an intensity measure (or simply intensity) of a particular frequency f _j from the reasonable set of frequencies for the series of sensing measurement times, and f _j represents a particular frequency from the reasonable set of frequencies for the series of sensing measurement times.

Table 3 below provides an example of the calculation of the intensity metric for a particular frequency from the reasonable set of frequencies. In an example, the values of t _l and a (t _l) may be taken from table 2.

Table 3: examples of computation of intensity metrics for specific frequencies from a reasonable set of frequencies (||D (f _j) ||) (for human respiration)

According to an embodiment, the sensing agent 516 identifies the maximum value of the intensity metric as equal to the maximum value ||d (f _j) |. Further, the sensing agent 516 may identify a particular reasonable frequency (f _m) of maximum D (f _m) as the discovered waveform frequency characteristic of small movements. In an example, if ||d (0.250) | is the maximum among all values of ||d (f _j) | as described in table 3 for the human respiratory case, f _m =0.250 Hz (15 breaths per minute) is a waveform frequency characteristic of small movements (e.g., human respiration).

Fig. 17A depicts an example of a reasonable frequency (f _m) that is well aligned with the frequency of the waveform amplitude variation of the characteristic pulse, according to some embodiments. In fig. 17A, reference numeral "1702" denotes a waveform frequency characteristic of a small motion of the object, and reference numeral "1704" denotes a rational frequency waveform of a rational frequency (f _m). Further, fig. 17A depicts variations in the amplitude of the characteristic pulse recorded at different sensing measurement times, such as t ₁ (denoted by reference numeral "1706"), t ₂ (denoted by reference numeral "1708"), … …, t _N (denoted by reference numeral "1710"). As depicted in fig. 17A, the reasonable frequency (f _m) is well aligned with the frequency of the waveform amplitude variation of the characteristic pulse.

Fig. 17B depicts an example of a reasonable frequency (f _n) that is not well aligned with the frequency of the waveform amplitude variation of the characteristic pulse, according to some embodiments. In fig. 17B, reference numeral "1702" denotes a waveform frequency characteristic of a small motion of the object, and reference numeral "1704" denotes a rational frequency waveform of a rational frequency (f _n). Further, fig. 17B depicts the variation of the amplitude of the characteristic pulse recorded at different sensing measurement times, such as t ₁ (denoted by reference numeral "1706"), t ₂ (denoted by reference numeral "1708"), … …, t _N (denoted by reference numeral "1710"). As depicted in fig. 17B, the reasonable frequency (f _n) is not well aligned with the frequency of the waveform amplitude variation of the characteristic pulse.

Fig. 18 depicts a flow diagram 1800 for identification of waveform frequency features for small movements occurring in a sensing space, in accordance with some embodiments.

In a brief overview of an implementation of the flow chart 1800, at step 1802, a series of time domain pulse sets may be obtained that are determined from a series of sensing measurements based on a series of sensing transmissions transmitted by the sensing transmitter 504 and received by a networked device operating as a sensing receiver over a time interval. At step 1804, characteristic pulses occurring in the set of time-domain pulses are identified. At step 1806, a series of amplitudes of the characteristic pulses in the time domain pulse set are recorded. At step 1808, waveform frequency features of small motions occurring in a sensing space corresponding to the networked device are identified based on the series of amplitudes of the feature pulses.

Step 1802 includes obtaining a series of time domain pulse sets determined from a series of sensing measurements based on a series of sensing transmissions transmitted by sensing transmitter 504 and received via a networked device within a time interval. In an implementation, the networked device is operable as a sensing receiver 502. According to an embodiment, the sensing receiver 502 may be configured to obtain the series of time domain pulse sets determined from the series of sensing measurements based on the series of sensing transmissions transmitted by the sensing transmitter 504 and received by the sensing receiver 502 within a time interval.

Step 1804 includes identifying characteristic pulses occurring in the set of time-domain pulses. According to an embodiment, a networked device operating as a sensing receiver 502 may be configured to identify characteristic pulses occurring in a time domain pulse set. In an embodiment, the characteristic pulse may represent a plurality of corresponding pulses, each of the corresponding pulses occurring in a respective one of the sets of time-domain pulses. Further, in an implementation, a networked device operating as a sense receiver 502 may identify a signature pulse based on selecting a signature pulse from among a plurality of wobble pulses that exhibits a change in amplitude of a time domain pulse set. In an implementation, a networked device operating as a sense receiver 502 may identify a characteristic pulse based on selecting a swing pulse with a largest amplitude variation from among a plurality of swing pulses.

Step 1806 includes recording a series of amplitudes of the characteristic pulses in the time domain pulse set. According to an embodiment, the networked device operating as the sensing receiver 502 may be configured to record the series of amplitudes of the characteristic pulses in the time domain pulse set. In an example, the series of amplitudes of the characteristic pulse may have uniform timing between the amplitudes. In some example, the series of amplitudes of the characteristic pulse may have non-uniform timing between the amplitudes. In an example, the timing between amplitudes in the series of amplitudes may be based on the timing of at least one of the sensing transmission and the sensing measurement. In an implementation, a networked device operating as the sensing receiver 502 may record changes in the amplitude of the characteristic pulse. In some embodiments, the change in amplitude of the characteristic pulse may be described as a waveform amplitude change that represents an amplitude difference from the fundamental amplitude of the time domain pulse.

Step 1808 includes identifying waveform frequency features of small motions occurring in a sensing space corresponding to the networked device based on the series of amplitudes of the feature pulses. According to an embodiment, a networked device operating as a sensing receiver 502 may be configured to identify waveform frequency features of small motions occurring in a sensing space corresponding to the sensing receiver 502 (i.e., the networked device) based on the series of amplitudes of the feature pulses. In an embodiment, a networked device operating as a sensing receiver 502 may be configured to identify waveform frequency features based on evaluating the series of amplitudes of the feature pulses relative to a reasonable frequency waveform. In an example, evaluating the series of amplitudes of the characteristic pulse includes evaluating the series of waveform amplitude variations of the characteristic pulse. According to an implementation, a networked device operating as a sensing receiver 502 may create a fourier basis function from one of the series of amplitudes of the characteristic pulse or the series of waveform amplitude variations and a reasonable frequency waveform. In an example, evaluating the series of amplitudes or the series of waveform amplitude variations of the characteristic pulse includes creating a fourier basis function from the series of amplitudes or the series of waveform amplitude variations of the characteristic pulse and based on a rational frequency waveform of a rational frequency of the one or more possible rational frequencies. In an example, the sensing space corresponds to a transmission path between the sensing transmitter 504 and a networked device operating as the sensing receiver 502. In an example, the sensing space corresponds to a transmission path between a networked device operating as a sensing receiver 502 and a sensing transmitter 504.

While various embodiments of methods and systems have been described, these embodiments are illustrative and in no way limit the scope of the described methods or systems. Changes in the form and details of the described methods and systems may be made by those skilled in the relevant art without departing from the broadest scope of the described methods and systems. Thus, the scope of the methods and systems described herein should not be limited by any of the illustrative embodiments, but should be defined only in accordance with the following claims and their equivalents.

Claims

1. A method for Wi-Fi sensing performed by a networked device, the networked device operating as a sensing receiver, the networked device comprising at least one processor configured to execute instructions, the method comprising:

obtaining, by the at least one processor, a series of time-domain pulse sets determined from a series of sensing measurements based on a series of sensing transmissions transmitted by a sensing transmitter and received by the networked device within a time interval;

identifying, by the at least one processor, a characteristic pulse occurring in the time domain pulse set;

recording, by the at least one processor, a series of amplitudes of the characteristic pulses in the time domain pulse set;

A waveform frequency characteristic of a small motion occurring in a sensing space corresponding to the networked device is identified by the at least one processor based on the series of amplitudes of the characteristic pulses.

2. The method of claim 1, wherein the characteristic pulse represents a plurality of corresponding pulses, each of the corresponding pulses occurring in a corresponding one of the time-domain pulse sets.

3. The method of claim 1, wherein identifying the characteristic pulse comprises selecting the characteristic pulse that exhibits amplitude variation of the time-domain pulse set from among a plurality of swing pulses.

4. The method of claim 3, wherein identifying the characteristic pulse comprises selecting the swing pulse having the largest amplitude variation from among the plurality of swing pulses.

5. The method of claim 1, wherein the series of amplitudes of the characteristic pulses have uniform timing between amplitudes.

6. The method of claim 1, wherein the series of amplitudes of the characteristic pulses have non-uniform timing between amplitudes.

7. The method of claim 1, wherein timing between amplitudes in the series of amplitudes is based on timing of at least one of the sensing transmissions and the sensing measurements.

8. The method of claim 1, wherein recording the series of amplitudes of the characteristic pulses comprises recording changes in the amplitudes of the characteristic pulses.

9. The method of claim 1, wherein identifying the waveform frequency characteristic comprises evaluating the series of amplitudes of the characteristic pulses relative to a reasonable frequency waveform.

10. The method of claim 9, wherein evaluating the series of amplitudes of the characteristic pulse relative to a plausible frequency waveform comprises creating a Fourier basis function from the series of amplitudes of the characteristic pulse and the plausible frequency waveform.

11. The method of claim 1, wherein the sensing space further corresponds to a transmission path between the networked device and the sensing transmitter.

12. A system for Wi-Fi sensing, comprising:

A networked device configured to operate as a sensing receiver, the networked device comprising at least one processor configured to:

obtaining a series of time-domain pulse sets determined from a series of sensing measurements based on a series of sensing transmissions transmitted by a sensing transmitter and received by the networked device within a time interval;

Identifying a characteristic pulse occurring in the time domain pulse set;

Recording a series of amplitudes of the characteristic pulses in the time domain pulse set;

Waveform frequency characteristics of small motions occurring in a sensing space corresponding to the networked device are identified based on the series of amplitudes of the characteristic pulses.

13. The system of claim 12, wherein the characteristic pulse represents a plurality of corresponding pulses, each of the corresponding pulses occurring in a corresponding one of the time-domain pulse sets.

14. The system of claim 12, wherein the processor is configured to identify the characteristic pulse by selecting the characteristic pulse that exhibits amplitude variation of the time-domain pulse set from among a plurality of swing pulses.

15. The system of claim 14, wherein the processor is configured to identify the characteristic pulse by selecting the wobble pulse having the largest amplitude variation from among the plurality of wobble pulses.

16. The system of claim 12, wherein the series of amplitudes of the characteristic pulses have uniform timing between amplitudes.

17. The system of claim 12, wherein the series of amplitudes of the characteristic pulses have non-uniform timing between amplitudes.

18. The system of claim 12, wherein timing between amplitudes in the series of amplitudes is based on timing of at least one of the sense transmissions and the sense measurements.

19. The system of claim 112, wherein the processor is configured to record the series of amplitudes of the characteristic pulses comprises recording changes in the amplitudes of the characteristic pulses.

20. The system of claim 12, wherein the processor is configured to identify the waveform frequency characteristic by evaluating the series of amplitudes of the characteristic pulses relative to a reasonable frequency waveform.

21. A system according to claim 20, wherein the processor is configured to evaluate the series of amplitudes of the characteristic pulses relative to the reasonable frequency waveform by creating Fourier basis functions from the series of amplitudes of the characteristic pulses and the reasonable frequency waveform.

22. The system of claim 12, wherein the sensing space further corresponds to a transmission path between the networked device and the sensing transmitter.