Patent Description:
The following description relates to motion detection and motion localization.

Motion detection systems have been used to detect movement, for example, of objects in a room or an outdoor area. In some example motion detection systems, infrared or optical sensors are used to detect movement of objects in the sensor's field of view. Motion detection systems have been used in security systems, automated control systems and other types of systems. An example for a system using a database which stores characteristics of radio frequency signals associated with the various regions of the signal transmission for location tracking of persons, assets, equipment, etc. in a Wi-Fi network is provided in <CIT>.

The invention is defined by the subject matter as claimed in independent claims <NUM> and <NUM>. According to the invention, detected motion is localized based on channel response characteristics. For example, in some instances, a motion detection system performs machine learning to associate motion of an object within a distinct region within a space with characteristics shared by channel responses obtained while motion of the object occurred within the distinct region. Also, the motion detection system performs RF motion localization to identify a distinct region within the space based on the machine-learned associations stored in a motion detection database. Each machine-learned association includes a shared channel response characteristic associated with a distinct region within the space.

The systems and techniques described here may provide one or more advantages in some instances. For example, motion may be detected using wireless signals transmitted through a space. In addition, a location of the motion may be by using only two wireless communication devices, and without triangulation. Also, a neural network can enable the motion detection system to dynamically improve its ability to identify shared characteristics within channel responses obtained while motion of an object occurred within a distinct region over time, for example, by repeating machine learning processes over several training periods.

<FIG> illustrates an example wireless communication system <NUM>. The example wireless communication system <NUM> 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 example wireless communication system <NUM> 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 example wireless communication devices 102A, 102B, 102C can 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 configured to operate according to one or more of the <NUM> family of standards developed by IEEE (e.g., Wi-Fi networks), and others. Examples of PANs include networks that operate according to short-range communication standards (e.g., BLUETOOTH®, Near Field Communication (NFC), ZigBee), millimeter wave communications, and others.

In some implementations, the wireless communication devices 102A, 102B, 102C may be configured to communicate in a cellular network, for example, according to a cellular network standard. Examples of cellular networks include networks configured according to <NUM> standards such as Global System for Mobile (GSM) and Enhanced Data rates for GSM Evolution (EDGE) or EGPRS; <NUM> 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); <NUM> standards such as Long-Term Evolution (LTE) and LTE-Advanced (LTE-A); and others.

In the example shown in <FIG>, the wireless communication devices 102A, 102B, 102C can be, or they may include, standard wireless network components. For example, the wireless communication devices 102A, 102B, 102C may be commercially-available Wi-Fi access points or another type of wireless access point (WAP) performing one or more operations as described herein that are embedded as instructions (e.g., software or firmware) on the modem of the WAP. In some cases, the wireless communication devices 102A, 102B, 102C may be nodes of a wireless mesh network, such as, for example, a commercially-available mesh network system (e.g., GOOGLE WIFI). In some cases, another type of standard or conventional Wi-Fi transmitter device may be used. The wireless communication devices 102A, 102B, 102C may be implemented without Wi-Fi components; for example, other types of standard or non-standard wireless communication may be used for motion detection. In some cases, the wireless communication devices 102A, 102B, 102C can be, or they may be part of, a dedicated motion detection system. For example, the dedicated motion detection system can include a hub device and one or more beacon devices (as remote sensor devices), and the wireless communication devices 102A, 102B, 102C can be either a hub device or a beacon device in the motion detection system.

As shown in <FIG>, the example wireless communication device 102C includes a modem <NUM>, a processor <NUM>, a memory <NUM>, and a power unit <NUM>; any of the wireless communication devices 102A, 102B, 102C in the wireless communication system <NUM> may include the same, additional or different components, and the components may be configured to operate as shown in <FIG> or in another manner. In some implementations, the modem <NUM>, processor <NUM>, memory <NUM>, and power unit <NUM> of a wireless communication device are housed together in a common housing or other assembly. In some implementations, one or more of the components of a wireless communication device can be housed separately, for example, in a separate housing or other assembly.

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

In some cases, a radio subsystem in the modem <NUM> can include one or more antennas and radio frequency circuitry. The radio frequency circuitry can include, for example, circuitry that filters, amplifies or otherwise conditions analog signals, circuitry that up-converts baseband signals to RF signals, circuitry that down-converts RF signals to baseband signals, etc. Such circuitry may include, for example, filters, amplifiers, mixers, a local oscillator, etc. The radio subsystem can be configured to communicate radio frequency wireless signals on the wireless communication channels. As an example, the radio subsystem may include a radio chip, an RF front end, and one or more antennas. A radio subsystem may include additional or different components. In some implementations, the radio subsystem can be or include the radio electronics (e.g., RF front end, radio chip, or analogous components) from a conventional modem, for example, from a Wi-Fi modem, pico base station modem, etc. In some implementations, the antenna includes multiple antennas.

In some cases, a baseband subsystem in the modem <NUM> can include, for example, digital electronics configured to process digital baseband data. As an example, the baseband subsystem may include a baseband chip. A 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, to communicate wireless network traffic through the radio subsystem, to detect motion based on motion detection signals received through the radio subsystem or to perform other types of processes. For instance, the baseband subsystem may include one or more chips, chipsets, or other types of devices that are configured to encode signals and deliver the encoded signals to the 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 instances, the radio subsystem in the example modem <NUM> receives baseband signals from the baseband subsystem, up-converts the baseband signals to radio frequency (RF) signals, and wirelessly transmits the radio frequency signals (e.g., through an antenna). In some instances, the radio subsystem in the example modem <NUM> wirelessly receives radio frequency signals (e.g., through an antenna), down-converts the radio frequency signals to baseband signals, and sends the baseband signals 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., a digital-to-analog converter, an analog-to-digital converter) and exchanges analog signals with the radio subsystem. In some examples, the radio subsystem includes conversion circuitry (e.g., a digital-to-analog converter, an analog-to-digital converter) and exchanges digital signals with the baseband subsystem.

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

The example processor <NUM> can execute instructions, for example, to generate output data based on data inputs. The instructions can include programs, codes, scripts, or other types of data stored in memory. Additionally or alternatively, the instructions can be encoded as pre-programmed or re-programmable logic circuits, logic gates, or other types of hardware or firmware components. The processor <NUM> may be or include a general-purpose microprocessor, as a specialized co-processor or another type of data processing apparatus. In some cases, the processor <NUM> performs high level operation of the wireless communication device 102C. For example, the processor <NUM> may be configured to execute or interpret software, scripts, programs, functions, executables, or other instructions stored in the memory <NUM>. In some implementations, the processor <NUM> may be included in the modem <NUM>.

The example memory <NUM> can include computer-readable storage media, for example, a volatile memory device, a non-volatile memory device, or both. The memory <NUM> can include one or more read-only memory devices, random-access memory devices, buffer memory devices, or a combination of these and other types of memory devices. In some instances, one or more components of the memory can be integrated or otherwise associated with another component of the wireless communication device 102C. The memory <NUM> may store instructions that are executable by the processor <NUM>. For example, the instructions may include instructions for time-aligning signals using an interference buffer and a motion detection buffer, such as through one or more of the operations of the example processes <NUM>, <NUM> of <FIG>, <NUM>.

The example power unit <NUM> provides power to the other components of the wireless communication device 102C. For example, the other components may operate based on electrical power provided by the power unit <NUM> through a voltage bus or other connection. In some implementations, the power unit <NUM> includes a battery or a battery system, for example, a rechargeable battery. In some implementations, the power unit <NUM> includes an adapter (e.g., an AC adapter) that receives an external power signal (from an external source) and coverts the external power signal to an internal power signal conditioned for a component of the wireless communication device 102C. The power unit <NUM> may include other components or operate in another manner.

In the example shown in <FIG>, the wireless communication devices 102A, 102B transmit wireless signals (e.g., according to a wireless network standard, a motion detection protocol, or otherwise). For instance, wireless communication devices 102A, 102B may broadcast wireless motion probe signals (e.g., reference signals, beacon signals, status signals, etc.), or they may send wireless signals addressed to other devices (e.g., a user equipment, a client device, a server, etc.), and the other devices (not shown) as well as the wireless communication device 102C may receive the 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 repeated periodically, for example, according to a wireless communication standard or otherwise.

In the invention, the wireless communication device 102C processes the wireless signals from the wireless communication devices 102A, 102B to detect motion of an object in a space accessed by the wireless signals, to determine a location of the detected motion, or both. For example, the wireless communication device 102C may perform one or more operations of the example processes described below with respect to FIGS. <NUM>-<NUM>, or another type of process for detecting motion or determining a location of detected motion. The space accessed by the wireless signals can be an indoor or outdoor space, which may include, for example, one or more fully or partially enclosed areas, an open area without enclosure, etc. The space can be or can include an interior of a room, multiple rooms, a building, or the like. In some cases, the wireless communication system <NUM> can be modified, for instance, such that the wireless communication device 102C can transmit wireless signals and the wireless communication devices 102A, 102B can processes the wireless signals from the wireless communication device 102C to detect motion or determine a location of detected motion.

The wireless signals used for motion detection can include, for example, a beacon signal (e.g., Bluetooth Beacons, Wi-Fi Beacons, other wireless beacon signals), another standard signal generated for other purposes according to a wireless network standard, or non-standard signals (e.g., random signals, reference signals, etc.) generated for motion detection or other purposes. In some examples, the wireless signals propagate through an object (e.g., a wall) before or after interacting with a moving object, which may allow the moving object's movement to be detected without an optical line-of-sight between the moving object and the transmission or receiving hardware. Based on the received signals, the third wireless communication device 102C may generate motion detection data. In some instances, the third wireless communication device 102C may communicate the motion detection data to another device or system, such as a security system, that may include a control center for monitoring movement within a space, such as a room, building, outdoor area, etc..

In some implementations, the wireless communication devices 102A, 102B can be modified to transmit motion probe signals (which may include, e.g., a reference signal, beacon signal, or another signal used to probe a space for motion) on a separate wireless communication channel (e.g., a frequency channel or coded channel) from wireless network traffic signals. For example, the modulation applied to the payload of a motion probe signal and the type of data or data structure in the payload may be known by the third wireless communication device 102C, which may reduce the amount of processing that the third wireless communication device 102C performs for motion sensing. The header may include additional information such as, for example, an indication of whether motion was detected by another device in the communication system <NUM>, an indication of the modulation type, an identification of the device transmitting the signal, etc..

In the example shown in <FIG>, the wireless communication system <NUM> is a wireless mesh network, with wireless communication links between each of the respective wireless communication devices <NUM>. In the example shown, the wireless communication link between the third wireless communication device 102C and the first wireless communication device 102A can be used to probe a first motion detection field 110A, the wireless communication link between the third wireless communication device 102C and the second wireless communication device 102B can be used to probe a second motion detection field 110B, and the wireless communication link between the first wireless communication device 102A and the second wireless communication device 102B can be used to probe a third motion detection field 110C. In some instances, each wireless communication device <NUM> detects motion in the motion detection fields <NUM> accessed by that device by processing received signals that are based on wireless signals transmitted by the wireless communication devices <NUM> through the motion detection fields <NUM>. For example, when the person <NUM> shown in <FIG> moves in the first motion detection field 110A and the third motion detection field 110C, the wireless communication devices <NUM> may detect the motion based on signals they received that are based on wireless signals transmitted through the respective motion detection fields <NUM>. For instance, the first wireless communication device 102A can detect motion of the person in both motion detection fields 110A, 110C, the second wireless communication device 102B can detect motion of the person <NUM> in the motion detection field 110C, and the third wireless communication device 102C can detect motion of the person <NUM> in the motion detection field 110A.

In some instances, the motion detection fields <NUM> can include, for example, air, solid materials, liquids, or another medium through which wireless electromagnetic signals may propagate. In the example shown in <FIG>, the first motion detection field 110A provides a wireless communication channel between the first wireless communication device 102A and the third wireless communication device 102C, the second motion detection field 110B provides a wireless communication channel between the second wireless communication device 102B and the third wireless communication device 102C, and the third motion detection field 110C provides a wireless communication channel between the first wireless communication device 102A and the second wireless communication device 102B. In some aspects of operation, wireless signals transmitted on a wireless communication channel (separate from or shared with the wireless communication channel for network traffic) are used to detect movement of an object in a space. The objects can be any type of static or moveable object, and can be living or inanimate. For example, the object can be a human (e.g., the person <NUM> shown in <FIG>), an animal, an inorganic object, or another device, apparatus, or assembly), an object that defines all or part of the boundary of a space (e.g., a wall, door, window, etc.), or another type of object. In some implementations, motion information from the wireless communication devices may be analyzed to determine a location of the detected motion. For example, as described further below, one of the wireless communication devices <NUM> (or another device communicably coupled to the devices <NUM>) may determine that the detected motion is nearby a particular wireless communication device.

<FIG> illustrates an example motion probe signal <NUM>. The example motion probe signal <NUM> can be transmitted, for example, in a wireless communication system to monitor for motion in a space. In some examples, the motion probe signal <NUM> is implemented as a packet. For instance, the motion probe signal <NUM> can include binary data that is converted to an analog signal, up-converted to radio frequency, and wirelessly transmitted by an antenna.

The motion probe signal <NUM> shown in <FIG> includes control data <NUM> and a motion data <NUM>. A motion probe signal <NUM> may include additional or different features, and may be formatted in another manner. In the example shown, the control data <NUM> may include the type of control data that would be included in a conventional data packet. For instance, the control data <NUM> may include a preamble (also called a header) indicating the type of information contained in the motion probe signal <NUM>, an identifier of a wireless device transmitting the motion probe signal <NUM>, a MAC address of a wireless device transmitting the motion probe signal <NUM>, a transmission power, etc. The motion data <NUM> is the payload of the motion probe signal <NUM>. In some implementations, the motion data <NUM> can be or include, for example, a pseudorandom code or another type of reference signal. In some implementations, the motion data <NUM> can be or include, for example, a beacon signal broadcast by a wireless network system.

In an example, the motion probe signal <NUM> is transmitted by a wireless device (e.g., the wireless communication device 102A shown in <FIG>) and received at a motion detection device (e.g., the wireless communication device 102C shown in <FIG>). In some cases, the control data <NUM> changes with each transmission, for example, to indicate the time of transmission or updated parameters. The motion data <NUM> can remain unchanged in each transmission of the motion probe signal <NUM>. The receiving wireless communication device can process the received signals based on each transmission of the motion probe signal <NUM>, and analyze the motion data <NUM> for changes. For instance, changes in the motion data <NUM> may indicate movement of an object in a space accessed by the wireless transmission of the motion probe signal <NUM>. The motion data <NUM> can then be processed, for example, to generate a response to the detected motion.

<FIG> are diagrams showing example wireless signals communicated between wireless communication devices 304A, 304B, 304C. The wireless communication devices 304A, 304B, 304C can be, for example, the wireless communication devices 102A, 102B, 102C shown in <FIG>, or other types of wireless communication devices. The example wireless communication devices 304A, 304B, 304C transmit wireless signals through a space <NUM>. The example space <NUM> can be completely or partially enclosed or open at one or more boundaries of the space <NUM>. The space <NUM> can be or can include an interior of a room, multiple rooms, a building, an indoor area, outdoor area, or the like. A first wall 302A, a second wall 302B, and a third wall 302C at least partially enclose the space <NUM> in the example shown.

In the example shown in <FIG>, the first wireless communication device 304A is operable to transmit wireless signals repeatedly (e.g., periodically, intermittently, at scheduled, unscheduled or random intervals, etc.). The transmitted signals may be formatted like the motion probe signal <NUM> of <FIG>, or in another manner. The second and third wireless communication devices 304B, 304C are operable to receive signals based on those transmitted by the wireless communication device 304A. The wireless communication devices 304B, 304C each have a modem (e.g., the modem <NUM> shown in <FIG>) that is configured to process received signals to detect motion of an object in the space <NUM>.

As shown, an object is in a first position 314A in <FIG>, and the object has moved to a second position 314B in <FIG>. In <FIG>, the moving object in the space <NUM> is represented as a human, but the moving object can be another type of object. For example, the moving object can be an animal, an inorganic object (e.g., a system, device, apparatus, or assembly), an object that defines all or part of the boundary of the space <NUM> (e.g., a wall, door, window, etc.), or another type of object.

As shown in <FIG>, multiple example paths of the wireless signals transmitted from the first wireless communication device 304A are illustrated by dashed lines. Along a first signal path <NUM>, the wireless signal is transmitted from the first wireless communication device 304A and reflected off the first wall 302A toward the second wireless communication device 304B. Along a second signal path <NUM>, the wireless signal is transmitted from the first wireless communication device 304A and reflected off the second wall 302B and the first wall 302A toward the third wireless communication device 304C. Along a third signal path <NUM>, the wireless signal is transmitted from the first wireless communication device 304A and reflected off the second wall 302B toward the third wireless communication device 304C. Along a fourth signal path <NUM>, the wireless signal is transmitted from the first wireless communication device 304A and reflected off the third wall 302C toward the second wireless communication device 304B.

In <FIG>, along a fifth signal path 324A, the wireless signal is transmitted from the first wireless communication device 304A and reflected off the object at the first position 314A toward the third wireless communication device 304C. Between <FIG>, a surface of the object moves from the first position 314A to a second position 314B in the space <NUM> (e.g., some distance away from the first position 314A). In <FIG>, along a sixth signal path 324B, the wireless signal is transmitted from the first wireless communication device 304A and reflected off the object at the second position 314B toward the third wireless communication device 304C. The sixth signal path 324B depicted in <FIG> is longer than the fifth signal path 324A depicted in <FIG> due to the movement of the object from the first position 314A to the second position 314B. In some examples, a signal path can be added, removed, or otherwise modified due to movement of an object in a space.

The example wireless signals shown in <FIG> may experience attenuation, frequency shifts, phase shifts, or other effects through their respective paths and may have portions that propagate in another direction, for example, through the walls 302A, 302B, and 302C. In some examples, the wireless signals are radio frequency (RF) signals. The wireless signals may include other types of signals.

In the example shown in <FIG>, the first wireless communication device 304A can repeatedly transmit a wireless signal. In particular, <FIG> shows the wireless signal being transmitted from the first wireless communication device 304A at a first time, and <FIG> shows the same wireless signal being transmitted from the first wireless communication device 304A at a second, later time. The transmitted signal can be transmitted continuously, periodically, at random or intermittent times or the like, or a combination thereof. The transmitted signal can have a number of frequency components in a frequency bandwidth. The transmitted signal can be transmitted from the first wireless communication device 304A in an omnidirectional manner, in a directional manner or otherwise. In the example shown, the wireless signals traverse multiple respective paths in the space <NUM>, and the signal along each path may become attenuated due to path losses, scattering, reflection, or the like and may have a phase or frequency offset.

As shown in <FIG>, the signals from various paths <NUM>, <NUM>, <NUM>, <NUM>, 324A, and 324B combine at the third wireless communication device 304C and the second wireless communication device 304B to form received signals. Because of the effects of the multiple paths in the space <NUM> on the transmitted signal, the space <NUM> may be represented as a transfer function (e.g., a filter) in which the transmitted signal is input and the received signal is output. When an object moves in the space <NUM>, the attenuation or phase offset affected upon a signal in a signal path can change, and hence, the transfer function of the space <NUM> can change. Assuming the same wireless signal is transmitted from the first wireless communication device 304A, if the transfer function of the space <NUM> changes, the output of that transfer function-the received signal-will also change. A change in the received signal can be used to detect movement of an object.

Mathematically, a transmitted signal f(t) transmitted from the first wireless communication device 304A may be described according to Equation (<NUM>): <MAT> where ωn represents the frequency of nth frequency component of the transmitted signal, cn represents the complex coefficient of the nth frequency component, and t represents time. With the transmitted signal f(t) being transmitted from the first wireless communication device 304A, an output signal rk(t) from a path k may be described according to Equation (<NUM>): <MAT> where αn,k represents an attenuation factor (or channel response; e.g., due to scattering, reflection, and path losses) for the nth frequency component along path k, and φn,k represents the phase of the signal for nth frequency component along path k. Then, the received signal R at a wireless communication device can be described as the summation of all output signals rk(t) from all paths to the wireless communication device, which is shown in Equation (<NUM>): <MAT> Substituting Equation (<NUM>) into Equation (<NUM>) renders the following Equation (<NUM>): <MAT>.

The received signal R at a wireless communication device can then be analyzed. The received signal R at a wireless communication device can be transformed to the frequency domain, for example, using a Fast Fourier Transform (FFT) or another type of algorithm. The transformed signal can represent the received signal R as a series of n complex values, one for each of the respective frequency components (at the n frequencies ωn). For a frequency component at frequency ωn, a complex value Hn may be represented as follows in Equation (<NUM>): <MAT>.

The complex value Hn for a given frequency component ωn indicates a relative magnitude and phase offset of the received signal at that frequency component ωn. When an object moves in the space, the complex value Hn changes due to the channel response αn,k of the space changing. Accordingly, a change detected in the channel response can be indicative of movement of an object within the communication channel. In some instances, noise, interference, or other phenomena can influence the channel response detected by the receiver, and the motion detection system can reduce or isolate such influences to improve the accuracy and quality of motion detection capabilities. According to the invention, the overall channel response is represented as: <MAT>.

In some instances, the channel response hch for a space can be determined, for example, based on the mathematical theory of estimation. For instance, a reference signal Ref can be modified with candidate channel responses (hch), and then a maximum likelihood approach can be used to select the candidate channel which gives best match to the received signal (Rcvd). In some cases, an estimated received signal (R̂cvd) is obtained from the convolution of the reference signal (Ref) with the candidate channel responses (hch), and then the channel coefficients of the channel response (hch) are varied to minimize the squared error of the estimated received signal (R̂cvd). This can be mathematically illustrated as: <MAT> with the optimization criterion <MAT> The minimizing, or optimizing, process can utilize an adaptive filtering technique, such as Least Mean Squares (LMS), Recursive Least Squares (RLS), Batch Least Squares (BLS), etc. The channel response can be a Finite Impulse Response (FIR) filter, Infinite Impulse Response (IIR) filter, or the like. As shown in the equation above, the received signal can be considered as a convolution of the reference signal and the channel response. The convolution operation means that the channel coefficients possess a degree of correlation with each of the delayed replicas of the reference signal. The convolution operation as shown in the equation above, therefore shows that the received signal appears at different delay points, each delayed replica being weighted by the channel coefficient.

<FIG> are plots showing examples of channel responses <NUM>, <NUM> computed from the wireless signals communicated between wireless communication devices 304A, 304B, 304C in <FIG>. <FIG> also show a frequency domain representation <NUM> of an initial wireless signal transmitted by the wireless communication device 304A. In the examples shown, the channel response <NUM> in <FIG> represents the signals received by the wireless communication device 304B when there is no motion in the space <NUM>, and the channel response <NUM> in <FIG> represents the signals received by the wireless communication device 304B in <FIG> after the object has moved in the space <NUM>.

In the example shown in <FIG>, for illustration purposes, the wireless communication device 304A transmits a signal that has a flat frequency profile (the magnitude of each frequency component f<NUM>, f<NUM>, and f<NUM> is the same), as shown in the frequency domain representation <NUM>. Because of the interaction of the signal with the space <NUM> (and the objects therein), the signals received at the wireless communication device 304B that are based on the signal sent from the wireless communication device 304A look different from the transmitted signal. In this example, where the transmitted signal has a flat frequency profile, the received signal represents the channel response of the space <NUM>. As shown in <FIG>, the channel responses <NUM>, <NUM> are different from the frequency domain representation <NUM> of the transmitted signal. When motion occurs in the space <NUM>, a variation in the channel response will also occur. For example, as shown in <FIG>, the channel response <NUM> that is associated with motion of object in the space <NUM> varies from the channel response <NUM> that is associated with no motion in the space <NUM>.

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

<FIG> are diagrams showing example channel responses <NUM>, <NUM> associated with motion of an object <NUM> in distinct regions <NUM>, <NUM> of the space <NUM>. In the examples shown, the space <NUM> is a building, and the space <NUM> is divided into a plurality of distinct regions-a first region <NUM>, a second region <NUM>, a region <NUM>, a fourth region <NUM>, and a fifth region <NUM>. The example space <NUM> may include additional or fewer regions, in some instances. As shown in <FIG>, the regions within a space may be defined by walls between rooms. In addition, the regions may be defined by ceilings between floors of a building. For example, the space <NUM> may include additional floors with additional rooms. In addition, in some instances, the plurality of regions of a space can be or include a number of floors in a multistory building, a number of rooms in the building, or a number of rooms on a particular floor of the building. In the example shown in <FIG>, an object located in the region <NUM> is represented as a person <NUM>, but the moving object can be another type of object, such as an animal or an inorganic object.

In the example shown, a first wireless communication device 402A is located in the region <NUM> of the space <NUM>, a second wireless communication device 402B is located in the region <NUM> of the space <NUM>, and a third wireless communication device 402C is located in the region <NUM> of the space <NUM>. The example wireless communication devices <NUM> can operate in the same or similar manner as the wireless communication devices <NUM> of <FIG>. For instance, the wireless communication devices <NUM> may be configured to transmit and receive wireless signals, and detect whether motion has occurred in the space <NUM> based on the received signals. As an example, the wireless communication devices <NUM> may periodically or repeatedly transmit motion probe signals (e.g., signals formatted similar to the motion probe signal <NUM> of <FIG>) through the space <NUM>, and receive signals based on the motion probe signals. The wireless communication devices <NUM> can analyze the received signals to detect whether an object has moved in the space <NUM>, such as, for example, by analyzing channel responses associated with the space based on the received signals. In addition, in some implementations, the example wireless communication devices <NUM> can analyze the received signals to identify a location of detected motion within the space <NUM>. For example, the wireless communication devices <NUM> can analyze characteristics of the channel response to determine whether the channel responses share the same or similar characteristics to channel responses known to be associated with the regions <NUM>, <NUM>, <NUM>, <NUM>, <NUM> of the space <NUM>.

In the examples shown, one (or more) of the wireless communication devices <NUM> repeatedly transmits a motion probe signal (e.g., a reference signal) through the space <NUM>. The motion probe signals may have a flat frequency profile in some instances, wherein the magnitude of each frequency componentf<NUM>, f<NUM>, and f<NUM>. For example, the motion probe signals may have a frequency response similar to the frequency domain representation <NUM> shown in <FIG>. The motion probe signals may have a different frequency profile in some instances. Because of the interaction of the reference signal with the space <NUM> (and the objects therein), the signals received at another wireless communication device <NUM> that are based on the motion probe signal transmitted from the other wireless communication device <NUM> are different from the transmitted reference signal.

Based on the received signals, the wireless communication devices <NUM> can determine a channel response for the space <NUM>. When motion occurs in distinct regions within the space, distinct characteristics may be seen in the channel responses. For example, while the channel responses may differ slightly for motion within the same region of the space <NUM>, the channel responses associated with motion in distinct regions may generally share the same shape or other characteristics. For instance, the channel response <NUM> of <FIG> represents an example channel response associated with motion of the object <NUM> in the region <NUM> of the space <NUM>, while the channel response <NUM> of <FIG> represents an example channel response associated with motion of the object <NUM> in the region <NUM> of the space <NUM>. The channel responses <NUM>, <NUM> are associated with signals received by the same wireless communication device <NUM> in the space <NUM>.

<FIG> are plots showing the example channel responses <NUM>, <NUM> of <FIG> overlaid on an example of channel response <NUM> associated with no motion occurring in the space. When motion occurs in the space <NUM>, a variation in the channel response will occur relative to the "no-motion" channel response <NUM>, and thus, motion of an object in the space <NUM> can be detected by analyzing variations in the channel responses. In addition, a relative location of the detected motion within the space <NUM> can be identified. For example, the shape of channel responses associated with motion can be compared with reference information (e.g., using a trained neural network) to categorize the motion as having occurred within a distinct region of a space.

When there is no motion in the space <NUM> (e.g., when the object <NUM> is not present), a wireless communication device <NUM> may compute a "no-motion" channel response <NUM>. Slight variations may occur in the channel response due to a number of factors; however, multiple "no-motion" channel responses associated with different periods of time may share one or more characteristics. In the example shown, the "no-motion" channel response <NUM> has a decreasing frequency profile (the magnitude of each frequency component f<NUM>, f<NUM>, and f<NUM> is less than the previous). The profile of a no-motion channel response <NUM> may differ in some instances (e.g., based on different room layouts or placement of the devices <NUM>).

When motion occurs in the space <NUM>, a variation in the channel response will occur. For instance, in the examples shown in <FIG>, the channel response <NUM> (associated with motion of the object <NUM> in region <NUM>) differs from the "no-motion" channel response <NUM> and the channel response <NUM> (associated with motion of the object <NUM> in region <NUM>) also differs from the "no-motion" channel response <NUM>. The channel response <NUM> has a concave-parabolic frequency profile (the magnitude of the middle frequency componentf<NUM> is less than the outer frequency components f<NUM> and f<NUM>), while the channel response <NUM> has a convex-asymptotic frequency profile (the magnitude of the middle frequency component f<NUM> is greater than the outer frequency components f<NUM> and f<NUM>). The profiles of the channel responses <NUM>, <NUM> may differ in some instances (e.g., based on different room layouts or placement of the devices <NUM>).

Analyzing channel responses may be considered similar to analyzing a digital filter. In other words, a channel response has been formed through the reflections of objects in a space as well as reflections created by a moving or static human. When a reflector (e.g., a human) moves, it changes the channel response. This may translate to a change in equivalent taps of a digital filter, which can be thought of as having poles and zeros (poles shoot up the frequency components of a channel response and appear as peaks or high points in the response, while zeros pull down the frequency components of a channel response and appear as low point or nulls in the response). A changing digital filter can be characterized by the locations of its peaks and valleys, and a channel response may be characterized similarly by its peaks and valleys. For example, in some implementations, analyzing nulls and peaks in the frequency components of a channel response (e.g., by marking their location on the frequency axis and their magnitude), motion can be detected.

In some implementations, a time series aggregation can be used to detect motion. A time series aggregation may be performed by observing the features of a channel response over a moving window and aggregating the windowed result by using statistical measures (e.g., mean, variance, principal components, etc.). During instances of motion, the characteristic digital-filter features would be displaced in location and flip-flop between some values due to the continuous change in the scattering scene. That is, an equivalent digital filter exhibits a range of values for its peaks and nulls (due to the motion). By looking this range of values, unique "signatures" or profiles may be identified for distinct regions within a space.

In some implementations, the profiles of the channel responses associated with motion in distinct regions of the space <NUM> can be "learned. " For example, machine learning may be used to categorize channel response characteristics with motion of an object within distinct regions of a space. In some cases, a user associated with the wireless communication devices <NUM> (e.g., an owner or other occupier of the space <NUM>) can assist with the learning process. For instance, referring to the examples shown in <FIG>, the user can move in each of the distinct regions <NUM>, <NUM>, <NUM>, <NUM>, <NUM> 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. For example, while the user is moving through the region <NUM> (e.g., as shown in <FIG>) the user may indicate on a mobile computing device that he/she is in the region <NUM> (and may name the region as "bedroom", "living room", "kitchen", or another type of room of a building, as appropriate). Channel responses may be obtained as the user moves through the region, and the channel responses may be "tagged" with the user's indicated location (region). The user may repeat the same process for the other regions of the space <NUM>.

The tagged channel responses can then be processed (e.g., by machine learning software) to identify unique characteristics of the channel responses associated with motion in the distinct regions. Once identified, the identified unique characteristics may used to determine a location of detected motion for newly computed channel responses. For example, a neural network (convolutional or fully connected) may be trained using the tagged channel responses, and once trained, newly computed channel responses can be input to the neural network, and the neural network can output a location of the detected motion. For example, in some cases, mean, range, and absolute values are input to a neural network. In some instances, magnitude and phase of the complex channel response itself may be input as well. These values allow the neural network to design arbitrary front-end filters to pick up the features that are most relevant to making accurate predictions with respect to motion in distinct regions of a space. In some implementations, the neural network is trained by performing a stochastic gradient descent. For instance, channel response variations that are most active during a certain zone may be monitored during the training, and the specific channel variations may be weighted heavily (by training and adapting the weights in the first layer to correlate with those shapes, trends, etc.). The weighted channel variations may be used to create a metric that activates when a user is present in a certain region.

For extracted features like channel response nulls and peaks, a time-series (of the nulls/peaks) may be created using an aggregation within a moving window, taking a snapshot of few features in the past and present, and using that aggregated value as input to the network. Thus, the network, while adapting its weights, will be trying to aggregate values in a certain region to cluster them, which can be done by creating a logistic classifier based decision surfaces. The decision surfaces divide different clusters and subsequent layers can form categories based on a single cluster or a combination of clusters.

In some implementations, a neural network includes two or more layers of inference. The first layer acts as a logistic classifier which can divide different concentration of values into separate clusters, while the second layer combines some of these clusters together to create a category for a distinct region. Additional, subsequent layers can help in extending the distinct regions over more than two categories of clusters. For example, a fully-connected neural work may include an input layer corresponding to the number of features tracked, a middle layer corresponding to the number of effective clusters (through iterating between choices), and a final layer corresponding to different regions. Where complete channel response information is input to the neural network, the first layer may act as a shape filter that can correlate certain shapes. Thus, the first layer may lock to a certain shape, the second layer may generate a measure of variation happening in those shapes, and third and subsequent layers may create a combination of those variations and map them to different regions within the space. The output of different layers may then be combined through a fusing layer.

<FIG> is a flow diagram showing an example process <NUM> of associating a shared channel response characteristic with a distinct region (e.g., the regions <NUM>, <NUM> of <FIG>) within a space (e.g., space <NUM> of <FIG>). Operations in the example process <NUM> may be performed by a data processing apparatus of a wireless communication device (e.g., the processor <NUM> of the example wireless communication device 102C in <FIG>) to associated channel response characteristics with motion in distinct regions of a space (e.g., the regions <NUM>, <NUM>, <NUM>, <NUM>, <NUM> of the space <NUM>). The example process <NUM> may be performed by another type of device. For instance, operations of the process <NUM> may be performed by a system other than the wireless communication device, such as, for example, a computer system connected to the wireless communication devices. The example process <NUM> may include additional or different operations, and the operations may be performed in the order shown or in another order. In some cases, one or more of the operations shown in <FIG> are implemented as processes that include multiple operations, sub-processes or other types of routines. In some cases, operations can be combined, performed in another order, performed in parallel, iterated, or otherwise repeated or performed another manner.

At <NUM>, one or more channel responses associated with motion of an object in a distinct region within a space are obtained. The channel responses are based on wireless signals transmitted through the space by one or more wireless communication devices of a wireless communication system (e.g., the wireless communication system <NUM> of <FIG>). Referring to the example shown in <FIG>, the channel response is based on the wireless signals transmitted through the space <NUM> by the wireless communication device 304A and received at one of the wireless communication devices 304B, 304C. Further, referring to the example shown in <FIG>, the channel response is based on wireless signals transmitted through the space <NUM> by one or more of the wireless communication devices <NUM> and received at one or more of the wireless communication devices <NUM>.

In some implementations, the channel responses are obtained while the wireless communication system is in a "training mode". For instance, the wireless communication devices (or another computing device communicably coupled to the wireless communication devices, e.g., a remote server) can receive user input that indicates a training mode has begun or is to begin. As an example, the wireless communication system may receive user input may specify a distinct region in the space using a region identifier (e.g., "kitchen," "office <NUM>," "office <NUM>," "upstairs balcony"), and may prompt the user to move within the specified region. Accordingly, the channel responses obtained during the training mode can become tagged data. For example, the channel responses may be tagged in association with the region identifier of the distinct region in the space.

In some implementations, the channel responses obtained while the wireless communication system is in a training mode are obtained during a training period. For example, the wireless communication system may notify the user of the start of the training period, and the end or the duration of the training period. The wireless communication system can present (e.g., via an audio playback or visual display) an indicator to the user instructing the user to move within the distinct region of the space during the training period, or another indicator to the that instructs the user to provide no motion (e.g., not move) within the region of the space during the training period. For instance, referring to the example shown in <FIG>, a wireless communication device <NUM> can obtain channel responses similar to the channel response <NUM> during a first training period that includes a user moving within the region <NUM>, and can obtain channel responses similar to the channel response <NUM> during a training period that includes a user moving within the region <NUM>.

At <NUM>, one or more characteristics shared by each of the channel responses are identified in the obtained channel responses. A shared characteristic is identified by analyzing the channel responses obtained at <NUM>, for example, by comparing the obtained channel responses, by combining the obtained channel responses with each other, or by detecting magnitudes of frequency components in each of the obtained channel responses and identifying repetitive patterns associated with each distinct region with the space. For instance, referring to the example shown in <FIG>, the wireless communication system can identify the concave-parabolic frequency profile as a shared characteristic of the channel responses <NUM> obtained during a first training period during which a user is moving with the region <NUM>, and can identify the convex-asymptotic frequency profile as a shared characteristic of the channel responses <NUM> obtained during a second training period during which a user is moving with the region <NUM>.

In some implementations, the shared characteristics may be determined by using machine learning. For example, a neural network (convolutional or fully connected) may be trained (e.g., as described above) using tagged channel responses obtained at <NUM>. Through training, the neural network may "learn" the shared characteristics for channel responses associated with motion in each of the distinct regions of the space.

At <NUM>, the identified characteristics are associated with the distinct region within the space. In some instances, the associations are made in a motion detection database that stores information regarding the shared characteristics identified at <NUM> as being associated with region identifiers specified at <NUM>. The motion detection database enables the wireless communication system to recall identified shared characteristics associated with a given distinct area of the space, and to recall the distinct area of the space associated with a given shared characteristic or set of shared characteristics. The motion detection database can be stored in memory of a wireless communication device (e.g., the memory <NUM> in <FIG>) of the wireless communication system, or another device communicably coupled to the wireless communication system. The motion detection database may be implemented as a traditional database, or as a neural network (e.g., functions with certain weightings applied to various variables, where the weightings are based on training of the neural network).

In some implementations, the association at <NUM> is executed by the wireless communication system in connection with a neural network. For example, a modem of a wireless communication device can connect (via a computer network such as the Internet) to a cloud-computing system that includes a neural network implementing the motion detection database. The wireless communication device may transmit channel responses obtained at <NUM> to the neural network as tagged data. In response, the neural network may analyze the tagged channel responses, identify one or more characteristics shared by each of the obtained channel responses, and store the shared characteristics in the motion detection database (e.g., as a function with various weightings). In some instances, the neural network may associate a pattern detected in a majority of the obtained channel responses with motion of an object in the distinct region within the space. The associations generated by the neural network are stored in the motion detection database of the cloud-computing systems, and may be accessed by the wireless communication devices of the wireless communication system or another device communicably coupled to the wireless communication system. For instance, referring to the example shown in <FIG>, the wireless communication system (via the neural network) can associate the concave-parabolic frequency profile with motion of the person <NUM> in the region <NUM> of the space <NUM>, and can associate the convex-asymptotic frequency profile with motion of the person <NUM> in the region <NUM> of the space <NUM>.

At <NUM>, after associating the identified characteristics with motion of the object in the distinct area of the space, the associations made at <NUM> are used in a motion localization process. In some instances, at <NUM>, the wireless communication system is no longer in the "training mode". For example, additional channel responses may be obtained based on wireless signals transmitted through the space. The wireless communication system can use the additional channel in a motion localization process, such as, for example, the process <NUM> of <FIG>.

In some implementations, the process <NUM> can be implemented to associate channel response characteristics with particular categories of motion that may be detected. For instance, referring to the example shown in <FIG>, when a dog moves within the region <NUM>, the channel response associated with that motion may be different from the channel response <NUM> associated with motion by a human in the region <NUM>. The channel response associated with dog movement in the region <NUM> may accordingly have its own unique characteristics, and the process <NUM> can be implemented to associate dog motion with certain characteristics (e.g., so that motion by a dog versus a human can be distinguished).

<FIG> is a flow diagram showing an example process <NUM> of motion localization based on channel response characteristics. Operations in the example process <NUM> may be performed by a data processing apparatus of a wireless communication device (e.g., the processor <NUM> of the example wireless communication device 102C in <FIG>) to associated channel response characteristics with motion in distinct regions of a space (e.g., the regions <NUM>, <NUM>, <NUM>, <NUM>, <NUM> of the space <NUM>). The example process <NUM> may be performed by another type of device. For instance, operations of the process <NUM> may be performed by a system other than the wireless communication device, such as, for example, a computer system connected to the wireless communication devices. The example process <NUM> may include additional or different operations, and the operations may be performed in the order shown or in another order. In some cases, one or more of the operations shown in <FIG> are implemented as processes that include multiple operations, sub-processes or other types of routines. In some cases, operations can be combined, performed in another order, performed in parallel, iterated, or otherwise repeated or performed another manner.

At <NUM>, channel responses are obtained based on wireless signals transmitted through a space between wireless communication devices. In some instances, at <NUM>, the wireless communication system is not in a training mode. The channel response may be obtained by each of the wireless communication devices in the wireless communication system based on signals received at that wireless communication device.

At <NUM>, a motion detection process is executed to detect motion of an object in the space based on the channel responses obtained at <NUM>. In some implementations, the motion detection process may analyze changes in the channel responses obtained at different points in time to detect whether motion has occurred in the space accessed by the wireless signals. That is, the motion detection process may detect motion at <NUM> in response to the detection of a change in the channel response over the different time points. The motion detection process may analyze other aspects of the received wireless signals to detect motion in the space. In some implementations, motion may be detected by providing the channel responses to a trained neural network. For example, the channel responses obtained at <NUM> may be provided as inputs to a neural network, and the neural network may provide an output that indicates whether motion is present in the space. The output of the neural network may be based on a function with various weightings determined during a training process. The neural network may be a convolutional neural network, a fully connected neural network, or a combination thereof.

At <NUM>, the channel responses are analyzed to identify a location of the motion within one of a plurality of regions within the space. In other words, a location of the motion within one of a plurality of regions within the space is identified based on results of analyzing the channel responses. In some implementations, the channel responses are obtained over a series of time points, and the location of the motion is identified based on a characteristic shared by the channel responses from each of the respective time points in the series. In the example shown, channel responses are analyzed to identify a location of the motion within one of a plurality of regions within the space by identifying, at <NUM>, a characteristic of one or more of the channel responses, and identifying, at <NUM>, a location of the detected motion based on comparing the identified characteristic with reference characteristics associated with multiple distinct locations within the space.

The channel response characteristic(s) may be identified by identifying a shape or contour defined by frequency components of the channel responses. The identification of the shape of the contour could be achieved by implementing curve fitting techniques, or by implementing predictive estimation techniques (e.g., interpolation or extrapolation). For example, referring to <FIG>, the shared characteristic among the channel responses obtained at <NUM> could be identified as the concave-parabolic frequency profile of the channel response <NUM>, the convex-asymptotic frequency profile of the channel response <NUM>, or the decreasing frequency profile of the no-motion channel response <NUM>. The reference characteristics may include, for example, entries in a motion detection database that associates each of the channel response characteristics with one distinct region from among the plurality of regions within the space. If a concave-parabolic frequency profile is identified at <NUM>, then the region <NUM> may be identified as the location of the detected motion based on a comparison of the concave-parabolic frequency profile of the newly obtained channel response with reference characteristics stored in the motion detection database and associated with the region <NUM>, the region <NUM>, or another region in the space <NUM> (or with channel responses associated with no motion).

In some implementations, identifying and comparing a channel response characteristic with reference characteristics includes providing the channel response obtained at <NUM> as inputs to a trained neural network, and identifying the location of the detected motion is based on an output of the neural network. For example, a neural network may be trained using tagged channel responses, as described above. After training, newly obtained channel responses can be input to the trained neural network, and the neural network can output an identifier associated with a distinct region of the space. The output of the neural network may be based on a function with various weightings determined during a training process. The neural network may be a convolutional neural network, a fully connected neural network, or a combination thereof. The channel responses may be analyzed in another manner to identify a location of detected motion.

In some implementations, analyzing the channel responses at <NUM> may include analyzing changes in the characteristics identified at <NUM>. Detected changes in the identified characteristics of the channel response may indicate that the motion is changing locations. By analyzing the changes in the channel response over time, motion of an object through the space may be tracked. For instance, referring to the example shown in <FIG>, as the object <NUM> moves from the region <NUM> to the region <NUM>, the channel response may slowly change from the shape shown in channel response <NUM> to the shape shown in channel response <NUM>. By analyzing the change in the characteristics of the channel response over time, motion by the object <NUM> can be tracked over time.

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

Some of the operations described in this specification can be implemented as operations performed by a data processing apparatus on data stored in memory (e.g., on one or more computer-readable storage devices) or received from other sources. In some instances, the data processing apparatus includes a set of processors. The set of processors may be co-located (e.g., multiple processors in the same computing device) or located in different location from one another (e.g., multiple processors in distributed computing devices). The memory storing the data executed by the data processing apparatus may be co-located with the data processing apparatus (e.g., a computing device executing instructions stored in memory of the same computing device), or located in a different location from the data processing apparatus (e.g., a client device executing instructions stored on a server device).

A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code).

Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random-access memory or both. Elements of a computer can include a processor that performs actions in accordance with instructions, and one or more memory devices that store the instructions and data. A computer may also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., non-magnetic drives (e.g., a solid-state drive), magnetic disks, magneto optical disks, or optical disks. Moreover, a computer can be embedded in another device, e.g., a phone, a tablet computer, an electronic appliance, a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, an Internet-of-Things (IoT) device, a machine-to-machine (M2M) sensor or actuator, or a portable storage device (e.g., a universal serial bus (USB) flash drive). Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, flash memory devices, and others), magnetic disks (e.g., internal hard disks, removable disks, and others), magneto optical disks, and CD ROM and DVD-ROM disks. In some cases, the processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, operations can be implemented on a computer having a display device (e.g., a monitor, or another type of display device) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse, a trackball, a stylus, a touch sensitive screen, or another type of pointing device) by which the user can provide input to the computer.

A computer system may include a single computing device, or multiple computers that operate in proximity or generally remote from each other and typically interact through a communication network. The communication network may include one or more of a local area network ("LAN") and a wide area network ("WAN"), an inter-network (e.g., the Internet), a network comprising a satellite link, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks). A relationship of client and server may arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other.

In a general aspect of some of the examples described, a motion detection system performs machine learning to associate motion of an object within a distinct region within a space with characteristics shared by channel responses obtained while motion of the object occurred within the distinct region. Also, the motion detection system performs RF motion localization to identify a distinct region within the space based on the machine-learned associations stored in a motion detection database. Each machine-learned association includes a shared channel response characteristic associated with a distinct region within the space.

While this specification contains many details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features specific to particular examples.

Claim 1:
A computer-implemented motion detection method comprising:
receiving first received signals indicative of wireless signals transmitted through a space over one or more paths by one or more wireless communication devices;
determining, from the first received signals, channel responses (<NUM>) associated with motion of an object in a distinct region within the space;
by operation of one or more processors, identifying a characteristic (<NUM>) shared by each of the channel responses, wherein the characteristic (<NUM>) corresponds to a channel frequency profile of the respective channel response, the channel frequency profile comprising magnitudes of channel frequency components of the respective channel response;
associating, in a motion detection database, the characteristic with the distinct region within the space (<NUM>); and
after associating the characteristic with the distinct region within the space:
receiving second received signals indicative of wireless signals transmitted through space over the one or more paths by one or more communication devices;
determining, from the second received signals, additional channel responses based on wireless signals transmitted through the space over the one or more paths between the wireless communication devices; and
using the motion detection database to identify motion in the distinct region (<NUM>);
wherein each channel response, and each additional channel response is represented as:
<MAT>
where Σk represents a sum over all paths k, <MAT> represents a sum over all frequency components and αn,k represents an attenuation factor for the nth frequency component along path k of respective wireless signals transmitted through the space over the one or more paths by one or more wireless communication devices.