CN114287108A

CN114287108A - Wireless communication with enhanced Maximum Permissible Exposure (MPE) compliance

Info

Publication number: CN114287108A
Application number: CN201980099405.6A
Authority: CN
Inventors: R·瑞米尼; T-C·黄
Original assignee: Qualcomm Inc
Current assignee: Qualcomm Inc
Priority date: 2019-08-22
Filing date: 2019-08-29
Publication date: 2022-04-05
Also published as: TW202109076A; EP4018560A1; WO2021034333A1

Abstract

Aspects of the present disclosure relate to classifying a target object. An electronic device may transmit a detection signal and receive a reflected signal reflected from the target object. The electronic device then determines a category of the target object based on one or more characteristics of the reflected signal and adjusts at least one transmission parameter based on the category. The electronic device then transmits an adjusted signal using the transmission parameter. Other aspects, embodiments, and features are also claimed and described.

Description

Wireless communication with enhanced Maximum Permissible Exposure (MPE) compliance

Priority requirements according to 35 U.S.C. § 119

This application claims priority and benefit from U.S. patent application No.16/548,722 filed on month 8 and 22 of 2019 and U.S. provisional patent application No.62/890,514 filed on month 8 and 22 of 2019, both of which are assigned to the assignee of the present application and are hereby expressly incorporated herein by reference.

Technical Field

The techniques discussed below relate generally to wireless communication and/or object classification systems, and more particularly to machine learning-based wireless transmission control and object classification for controlling maximum allowable exposure. Embodiments can provide and implement techniques for classifying nearby subjects and/or target objects (e.g., those detected by a wireless proximity sensor or other communication-enabled component) and controlling maximum allowable exposure.

INTRODUCTION

Next generation wireless telecommunication systems, such as for example fifth generation (5G) or New Radio (NR) technologies, are being deployed with millimeter wave (mmW) signals. These signals are capable of operating, for example, in the 28GHz and 39GHz frequency spectrums. While higher frequency signals provide a larger bandwidth to efficiently transfer large amounts of information/data, mmW signals may suffer from high path loss (e.g., path attenuation). To compensate for path loss, the transmit power level may be increased, or beamforming may concentrate energy in a particular direction.

As with various types of electronic signal transmissions, there are typically regulatory rules governing the strength of the transmission. For example, for mmW signals, the Federal Communications Commission (FCC) and other regulatory bodies set stringent RF exposure requirements. These rules ensure that the maximum permissible dose (MPE) on human skin does not exceed 1mW/cm²The power density of (a). To meet the targeted guidelines, the electronic device is responsible for balancing performance with transmission power and other constraints. The implementation of such balanced behavior can be challengingAnd particularly for devices having cost, size and other considerations.

Brief summary of some examples

The following presents a simplified summary of one or more aspects of the disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated features of the disclosure, and is intended to neither identify key or critical elements of all aspects of the disclosure, nor delineate the scope of any or all aspects of the disclosure. Its sole purpose is to present some concepts of one or more aspects of the disclosure in a simplified form as a prelude to the more detailed description that is presented later.

According to some aspects, wireless communication devices, methods, and systems are provided that enable MPE compliance and/or human target object perception and detection. For example, an apparatus embodiment (e.g., a mobile device) may include a wireless communication enabled component (e.g., a mmW signal interface). A wireless communication-enabling component (e.g., a transceiver) can not only facilitate wireless communication via receiving and communicating radio frequency signals (e.g., mmW signals), but the transceiver can also utilize mmW signaling to detect objects. Apparatus embodiments can utilize object detection features to determine a class of an object. If it is determined that the object is human, non-human, animate, inanimate, etc., the device can adjust operating parameters of the communication interface (e.g., mmW transceiver) to achieve MPE compliance (e.g., to raise or lower the power of the signal transmission). According to some aspects, signal transmission power adjustments may occur in real time or according to various desired timing arrangements.

In some aspects, the present disclosure provides a method for classification of a target object. The method includes transmitting a detection signal and receiving a reflected signal reflected from the target object. The method further includes determining a category of the target object based on one or more characteristics of the reflected signal, and adjusting at least one transmission parameter based on the category of the target object. The method further includes transmitting an adjusted signal using the transmission parameter.

In a further aspect, the present disclosure provides an electronic device configured for classification of a target object. The electronic device includes a processor, a transceiver communicatively coupled to the processor, and a data storage medium communicatively coupled to the processor. Here, the processor is configured to transmit a detection signal via the transceiver and receive a reflection signal via the transceiver, the reflection signal reflecting from the target object. The processor is further configured to determine a category of the target object based on one or more characteristics of the reflected signal, and adjust at least one transmission parameter based on the category of the target object. The processor is further configured to transmit, via the transceiver, an adjusted signal using the transmission parameter.

In a further aspect, the present disclosure provides an electronic device configured for classification of a target object. The electronic device comprises means for transmitting a detection signal; and means for receiving a reflected signal, the reflected signal being reflected from the target object. The electronic device further includes means for determining a category of the target object based on one or more characteristics of the reflected signal and means for adjusting at least one transmission parameter based on the category of the target object. The electronic device further includes means for transmitting the adjusted signal using the transmission parameter.

In a further aspect, the present disclosure provides a non-transitory computer-readable medium storing computer-executable code. The code includes instructions for causing an electronic device to transmit a detection signal and instructions for causing the electronic device to receive a reflected signal reflected from a target object. The code further includes instructions for causing the electronic device to determine a category of the target object based on one or more characteristics of the reflected signal and instructions for causing the electronic device to adjust at least one transmission parameter based on the category of the target object. The code further includes instructions for causing the electronic device to transmit an adjusted signal using the transmission parameter.

In a further aspect, the present disclosure provides a wireless communication device comprising a housing shaped and dimensioned to carry one or more components including a memory, a wireless transceiver, a power amplifier, and at least one processor. The wireless transceiver is configured to transmit and/or receive millimeter-wave signals via a wireless channel. The wireless transceiver is further configured to sense objects relative to and external to the housing via millimeter wave signaling and to provide object sensing information to the at least one processor. And, the at least one processor is configured to control the power amplifier based on the object sensing information to moderate transmission parameters associated with the wireless transceiver transmitting and/or receiving millimeter waves and to communicate information associated with sensing objects positioned relative to and external to the enclosure.

In a further aspect, the present disclosure provides, in a system for providing information between a plurality of wireless communication devices, a method of providing information to a wireless communication device, the information being capable of assessing an object class of an observed object. Here, the method includes configuring the data store to be in electronic wireless communication with one or more unique wireless communication devices among a plurality of wireless communication devices operating within a wireless network. The method further includes receiving, from the one or more unique wireless communication devices, micro-movement information transmitted via the wireless network, the micro-movement information including data observations indicative of micro-movements associated with the one or more target objects. The method further includes determining object class information about the one or more target objects based at least in part on the received micromovement information and other stored information. The method further includes transmitting the object class information to one or more of the wireless communication devices in the wireless network such that any of the wireless communication devices can moderate wireless transmission parameters associated with their transmission and reception operations.

In a further aspect, the present disclosure provides a wireless communication device configured as a vehicle including a body configured to carry at least one of a payload or a passenger. The vehicle includes a wireless communication interface sized and shaped to be positioned adjacent to or within the vehicle body, wherein the wireless communication interface is configured to transmit and/or receive millimeter wave signals via a wireless channel. The wireless communication interface is further configured to sense an object relative to the body via millimeter wave signaling and configured to provide object sensing information to at least one processor. The at least one processor is configured to control transmission parameters associated with the wireless communication interface transmitting and/or receiving millimeter waves based on the object sensing information and to communicate information associated with sensing an object relative to the vehicle body.

In a further aspect, the present disclosure provides a wireless communication device configured for gaming, wherein the wireless communication device includes a housing sized and shaped for gaming to allow a user to participate in an electronic gaming environment. The wireless communication device includes a wireless communication interface sized and shaped to be positioned adjacent to or within the housing, wherein the wireless communication interface is configured to transmit and/or receive millimeter-wave signals via a wireless channel. The wireless communication interface is further configured to sense an object relative to the housing via millimeter wave signaling and configured to provide object sensing information to the at least one processor. The at least one processor is configured to control transmission parameters associated with the wireless communication interface transmitting and/or receiving millimeter waves based on the object sensing information and to communicate information associated with sensing an object relative to the housing.

These and other aspects of the present invention will be more fully understood after a review of the following detailed description. Other aspects, features and embodiments will become apparent to those ordinarily skilled in the art upon review of the following description of specific exemplary embodiments in conjunction with the accompanying figures. While various features may be discussed below with respect to certain embodiments and figures, all embodiments can include one or more of the advantageous features discussed herein. In other words, while one or more embodiments may be discussed as having certain advantageous features, one or more such features may also be used in accordance with the various embodiments discussed herein. In a similar manner, although example embodiments may be discussed below as device, system, or method embodiments, it should be appreciated that such example embodiments may be implemented in a variety of devices, systems, and methods.

Brief Description of Drawings

Fig. 1 is a block diagram of a wireless electronic device in accordance with some aspects of the present disclosure.

FIG. 2 is a schematic illustration of an operating environment of an electronic device utilizing a radar-based proximity detector in accordance with some aspects of the present disclosure.

Fig. 3 is a block diagram illustrating additional details of a portion of an electronic device in accordance with some aspects of the present disclosure.

Fig. 4 is a series of graphs showing radar echo signatures for different target objects, in accordance with some aspects of the present disclosure.

Fig. 5 is a series of diagrams of a two-dimensional feature space illustrating how target object classification can be achieved based on the use of suitable features, according to some aspects of the present disclosure.

Fig. 6 is a diagram illustrating how a Service Vector Machine (SVM) can establish optimal boundaries separating different classes of target objects according to some aspects of the present disclosure.

Fig. 7 is a flow diagram illustrating an exemplary process for building a target object classifier in accordance with some aspects of the present disclosure.

Fig. 8 is a flow diagram illustrating an example process for controlling one or more transmission parameters with a target object classifier in accordance with some aspects of the present disclosure.

Detailed Description

The detailed description set forth below in connection with the appended drawings is intended as a description of various configurations and is not intended to represent the only configurations in which the concepts described herein may be practiced. The detailed description includes specific details to provide a thorough understanding of the various concepts. It will be apparent, however, to one skilled in the art that these concepts may be practiced without these specific details. In some instances, well-known structures and components are shown in block diagram form in order to avoid obscuring such concepts.

Although aspects and embodiments are described herein by way of illustration of some examples, those skilled in the art will appreciate that additional implementations and use cases may be generated in many different arrangements and scenarios. The innovations described herein may be implemented across many different platform types, devices, systems, shapes, sizes, packaging arrangements. For example, embodiments and/or uses can be generated via integrated chip embodiments and other non-module component-based devices (e.g., end-user devices, vehicles, communication devices, computing devices, industrial equipment, retail/shopping devices, medical devices, AI-enabled devices, etc.). While some examples may or may not be specific to each use case or application, broad applicability of the described innovations may occur. Implementations may range from chip-level or modular components to non-module, non-chip-level implementations, and further to aggregated, distributed, or OEM devices or systems incorporating one or more aspects of the described innovations. In some practical environments, a device incorporating the described aspects and features may also include additional components and features as necessary to implement and practice the various embodiments as claimed and described. For example, the transmission and reception of wireless signals must include several components for analog and digital purposes (e.g., hardware components including antennas, RF chains, power amplifiers, modulators, buffers, processors, interleavers, summers/summers, etc.). The innovations described herein are intended to be practiced in a wide variety of devices, chip-level components, systems, distributed arrangements, end-user devices, and the like, of various sizes, shapes, and configurations.

Electronic wireless communication devices utilizing millimeter wave (mmW) signals may use high transmit power to compensate for path loss associated with signals at these frequencies. Many of these electronic devices, such as mobile User Equipment (UE), are capable of being physically operated by a user. The physical proximity of such electronic devices presents an opportunity for radiation to exceed given guidelines, such as Maximum Permissible Exposure (MPE) limits as determined by the Federal Communications Commission (FCC) or other regulatory bodies. Because of these issues, it would be advantageous to enable devices to moderate one or more transmission parameters (including but not limited to transmit power) based on the proximity of users.

Some proximity detection techniques may use dedicated sensors, such as cameras, Infrared (IR) sensors, radar sensors, etc., to detect the user. However, these sensors can be bulky and expensive. Further, a single electronic device may include multiple antennas located on different surfaces (e.g., on the top, bottom, or opposite sides). To account for each of these antennas, according to some aspects, it may be necessary to install multiple cameras or sensors near each of these antennas, which further increases the cost and size of the electronic device.

In further aspects and/or examples, the same wireless transceiver used for wireless communication may also perform proximity detection. For example, Local Oscillator (LO) circuitry within a wireless transceiver may generate one or more reference signals that enable both proximity detection and wireless communication. The LO circuitry may enable Frequency Modulated Continuous Wave (FMCW) signals or multi-tone signals to be transmitted for radar-based proximity detection. By analyzing reflections from any of these signals, a distance to the object (e.g., distance or slope) and, in some examples, a material composition of the object may be determined.

To ensure compliance with MPE requirements, according to some aspects, a proximity detector (including, but not limited to, an integrated FMCW-based radar function) may detect the presence of an object and/or nearby targets. Objects may be located around the device and some objects may be objects of interest. For example, the detector may determine whether the target is within 20cm of the radiating element of the device. The detection of multiple objects may be used to create a virtual map of an item or subject having a spatial relationship with the device. Based on such proximity detection, the device may adjust one or more transmission parameters for wireless communication accordingly (such as by reducing transmission power, by switching to a different transmit antenna, etc.). By actively measuring the distance to one or more objects, the electronic device may continuously monitor its surroundings and may incrementally adjust one or more transmission parameters to account for movement of the objects (e.g., adjustment may increase or decrease transmission power generally or in a particular direction via beamformed mmW waves or RF waves).

In general, radar signal processing is tailored to extract location-based information of a target (such as its range, velocity, angle, position, etc.). However, typical radars do not provide information about the nature of the target (such as whether the target is a living animal/creature, a human, etc.). According to an aspect of the present disclosure, it may be advantageous to adjust one or more transmission parameters for wireless communication based not only on proximity detection of nearby targets, but also based on classification of the targets. That is, MPE requirements generally only apply to exposure to humans. If an inanimate object, such as a coffee cup or wall, is in close proximity to the electronic device, the MPE requirements may not apply and high transmission parameters may continue to be utilized, for example. Thus, including information regarding classification of objects spaced apart in spatial location (e.g., nearby objects or subjects located away from the electronic device) or detected nearby objects may help optimize power transfer of the electronic device.

Fig. 1 illustrates an example electronic device 102 for implementing a Machine Learning (ML) algorithm for classifying target objects detected with a radar-based proximity detector, in accordance with some aspects of the present disclosure. In the example environment 100, the electronic device 102 communicates with the base station 104 over a wireless communication link 106 (wireless link 106). For example, the electronic device 102 and the base station 104 may be part of a system for providing information between a plurality of unique wireless communication devices. In fig. 1, the electronic device 102 is illustrated as a smartphone, a vehicle, or a gaming device to provide some examples. However, the electronic device 102 may be any suitable stationary or mobile apparatus that includes a wireless transceiver. Within this disclosure, the term electronic device generally refers to a wide variety of devices and technologies. An electronic device may include several hardware structural components sized, shaped, and arranged to facilitate communication; such components may include antennas, antenna arrays, RF chains, amplifiers, one or more processors, and so forth, electrically coupled to each other. For example, some non-limiting examples of mobile devices include mobile devices, cellular (cell) phones, smart phones, Session Initiation Protocol (SIP) phones, laptops, Personal Computers (PCs), notebooks, netbooks, smartbooks, tablets, Personal Digital Assistants (PDAs), and a wide variety of embedded systems, e.g., corresponding to the "internet of things" (IoT). The mobile device may additionally be a self-propelled or other transportation vehicle, a remote sensor or actuator, a robot or robotic device, a satellite radio, a Global Positioning System (GPS) device, an object tracking device, a drone, a multi-axis vehicle, a quadcopter, a remote control device, a consumer, and/or a wearable device, such as glasses, wearable cameras, virtual reality devices, smart watches, health or fitness trackers, digital audio players (e.g., MP3 players), cameras, game consoles, gaming devices (e.g., interfaces that enable a user to participate in or play electronic games), and so forth. The mobile device may additionally be a digital home or intelligent home appliance, such as a home audio, video, and/or multimedia device, appliance, vending machine, intelligent lighting device, home security system, smart meter, augmented reality device, virtual reality device, mixed reality device, and the like. The mobile device may additionally be a smart energy device, a security device, a solar panel or array, a municipal infrastructure device (e.g., a smart grid) that controls power, lighting, water, etc.; industrial automation and enterprise equipment; a logistics controller; agricultural equipment; military defense equipment, vehicles, airplanes, boats, weapons, and the like. Still further, the mobile device may provide networked medical or telemedicine support, such as remote health care. The remote healthcare devices may include remote healthcare monitoring devices and remote healthcare supervisory devices whose communications may be given priority or preferential access over other types of information, for example in the form of prioritized access to critical service data transmissions and/or associated QoS for critical service data transmissions. In some examples, the electronic device 102 may be a wireless communication device that includes a housing shaped and sized to carry one or more components (such as those described below).

In one example, the electronic device 102 may be a vehicle or a portion of a vehicle that includes a body configured to carry at least one of a payload or a passenger. In this example, the wireless transceiver 120 may be sized and shaped to be placed in a position adjacent to and/or within the vehicle body. In another example, the electronic device 102 may be a gaming device or a portion of a gaming device that includes a housing sized and shaped to allow a user to participate in an electronic gaming environment. In this example, the wireless transceiver 120 may be sized and shaped to be placed in a position adjacent to the housing. Further, the gaming device may include a visual interface field defining a visual display configured to visually convey object-sensing information to a user, and/or one or more user interfaces located proximate to the housing for receiving user input and conveying object-sensing information to the user in response.

The base station 104 communicates with the electronic device 102 via a wireless link 106, which wireless link 106 may be implemented as any suitable type of wireless link. Although depicted as a cellular network tower, the base station 104 may represent or be implemented as another device, such as a satellite, a cable head end, a terrestrial television broadcast tower, an access point, a peer device, a mesh network node, a small cell node, a fiber optic line, and so forth. Thus, the electronic device 102 may communicate with the base station 104 or another device via a wired connection, a wireless connection, or a combination thereof.

The wireless link 106 may include a downlink for data or control information communicated from the base station 104 to the electronic device 102 and an uplink for other data or control information communicated from the electronic device 102 to the base station 104. The wireless link 106 may be implemented using any suitable communication protocol or standard, such as third generation partnership project long term evolution (3GPP LTE), fifth generation new radio (5G NR), IEEE 802.11, IEEE 802.16, bluetooth (TM), etc. In some implementations, the wireless link 106 may provide power wirelessly and the base station 104 may include a power source instead of, or in addition to, providing a data link.

The electronic device 102 includes an application processor 108 and a computer-readable storage medium 110(CRM 110). The application processor 108 may include any type of processor (e.g., an application processor, a Digital Signal Processor (DSP), or a multi-core processor) that executes processor executable code stored by the CRM 110. CRM 110 may include any suitable type of data storage medium, such as volatile memory (e.g., Random Access Memory (RAM)), non-volatile memory (e.g., flash memory), optical media, magnetic media (e.g., a disk or tape), and so forth. In the context of the present disclosure, the CRM 110 is implemented to store instructions 112, data 114, and other information and software for the electronic device 102. For example, CRM 110 may include a memory for storing data configured to enable processor/DSP 128 to process object sensing information for the stored data, thereby enabling classification of target objects into different object type categories. The CRM 110 may reside in the application processor 108, be external to the application processor 108, or be distributed across multiple entities including the application processor 108. The CRM 110 may be implemented in a computer program product. By way of example, a computer program product may include a computer-readable medium in packaging material. Those skilled in the art will recognize how best to implement the described functionality presented throughout this disclosure, depending on the particular application and the overall design constraints imposed on the overall system.

The one or more processors 108 may execute software. Software should be construed broadly to mean instructions, instruction sets, code segments, program code, programs, subprograms, software modules, applications, software applications, packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, etc., whether referred to in software, firmware, middleware, microcode, hardware description language, or other terminology.

The electronic device 102 may also include input/output ports 116(I/O ports 116) and a display 118. The I/O ports 116 enable data exchange or interaction with other devices, networks, or users. The I/O ports 116 can include serial ports (e.g., Universal Serial Bus (USB) ports), parallel ports, audio ports, Infrared (IR) ports, and the like. The display 118 presents graphics of the electronic device 102, such as a user interface associated with an operating system, program, or application. Alternatively or additionally, the display 118 may be implemented as a displayport or virtual interface through which graphical content of the electronic device 102 is presented.

The wireless transceiver 120 of the electronic device 102 may include a wireless transmitter 122 and a wireless receiver 124. The wireless transceiver 120 provides connectivity to the respective network and other electronic devices connected thereto. Additionally, the electronic device 102 may include a wired transceiver (such as an ethernet or fiber optic interface) for communicating over a local network, an intranet, or the internet. The wireless transceiver 120 may facilitate communication over any suitable type of wireless network, such as a wireless lan (wlan), a peer-to-peer (P2P) network, a mesh network, a cellular network, a Wireless Wide Area Network (WWAN), and/or a Wireless Personal Area Network (WPAN). In the context of the example environment 100, the wireless transceiver 120 enables the electronic device 102 to communicate with the base station 104 and the networks connected thereto.

The wireless transceiver 120 includes circuitry and logic for transmitting and receiving signals via the antenna 126. For example, the wireless transceiver 120 may be configured to transmit and/or receive mmW signals via a wireless channel and further to sense objects relative to and external to the housing of the electronic device 192 by utilizing mmW signaling. The wireless transceiver 120 may be configured to engage in mmW communication and mmW object sensing substantially simultaneously. Further, the wireless transceiver 120 may be configured to sense objects over a range from about 1 degree to about 360 degrees to enable the processor/DSP 128 to generate an object sensing map of objects outside the housing of the electronic device 102.

The wireless transceiver 120 includes circuitry and logic for transmitting and receiving signals via the antenna 126. The components of the wireless transceiver 120 may include amplifiers, mixers, switches, analog-to-digital converters, filters, etc. for conditioning signals. The wireless transceiver 120 may further include logic to perform in-phase/quadrature (I/Q) operations such as combining, encoding, modulating, decoding, demodulating, and so forth. The wireless transceiver 120 may include one or more components or features for adjusting or controlling transmission parameters. For example, the wireless transceiver 120 may provide object sensing information to the processor DSP 128. In some cases, the components of the wireless transceiver 120 are implemented as separate transmitter 122 and receiver entities. Additionally or alternatively, the wireless transceiver 120 may be implemented using multiple or different parts to implement respective transmit and receive operations (e.g., separate transmit and receive chains). Although the examples described below generally refer to an integrated wireless transceiver 120 that performs both wireless communication and object sensing operations, aspects of the present disclosure are not limited to this scenario. For example, the electronic device 102 may include interface circuitry for interfacing with auxiliary and/or auxiliary sensing devices that are spaced apart from a housing of the electronic device 102. The auxiliary and/or auxiliary sensing devices may include remote wireless devices capable of communicating with the electronic device 102 (e.g., game controllers, wearable devices, augmented/virtual reality devices, and other types of mobile devices described above). Here, an interface circuit (not illustrated) enables communication between the electronic device 102 and the auxiliary sensing device to cause the electronic device 102 to receive object sensing information from the auxiliary sensing device. In response to the object sensing information, the electronic device 102 may moderate transmission parameters associated with the wireless transceiver 120 transmitting and/or receiving mmW signals. Moderation of the transmission power may include controlling and/or modifying the transmission power, such as increasing and/or decreasing or otherwise changing the transmission power level. The electronic device 102 also includes a processor/Digital Signal Processor (DSP)128 that is coupled to the wireless transceiver 120. The processor/DSP 128, which may include a modem, may be implemented within the wireless transceiver 120 or separate from the wireless transceiver 120. Although not explicitly shown, the processor/DSP 128 may comprise a portion of the CRM 110, or may access the CRM 110 for computer-readable instructions. The processor/DSP 128 controls the wireless transceiver 120 and enables wireless communications or proximity detection to be performed. For example, the processor/DSP 128 may control a power amplifier at the transceiver 120 based on the object sensing information to moderate transmission parameters. The processor/DSP 128 may include baseband circuitry to perform high-rate sampling processes, which may include analog-to-digital conversion, digital-to-analog conversion, fourier transformation, gain correction, tilt correction, frequency translation, and so forth. The processor/DSP 128 may provide the communication data to the wireless transceiver 120 for transmission. The processor/DSP 128 may also process a baseband version of the signals obtained from the wireless transceiver 120 to generate data that may be provided to other portions of the electronic device 102 via the communication interface for wireless communication or proximity detection.

In some examples, the electronic device 102 (e.g., via the processor/DSP 128) may be configured to generate, output, or produce a map. The map may be based at least on the object sensing information. The map may include spatial locations and other information associated with objects surrounding the electronic device 102 (e.g., communication status, operational status, relative object locations, directions, movements, types, and/or categories). Additionally, the map may be used to display and/or identify objects, people, and/or animals relative to the electronic device 102. The map may be temporally static or may be temporally dynamic. The map may be used to visually display objects around the device ranging from a full 360 degrees and other or smaller (e.g., from about 1 degree to about 360 degrees). The map enables a user to access an augmented/virtual reality environment (e.g., (electronic) gaming, (remote) health and/or education). According to some aspects, the map may be provided as output to the user via a display screen (e.g., the display screen shown for electronic device 102) or other user interface. The electronic device 102 may transmit map information to other devices in the network. According to some aspects, sharing map information in this manner may assist in diffusing map and/or spatial positioning information within a network.

Although not explicitly depicted, the wireless transceiver 120 or the processor/DSP 128 may also include a controller. The controller may include at least one processor and at least one CRM, such as an application processor 108 and a CRM 110. The CRM may store computer-executable instructions, such as instructions 112. The processor and CRM may be located at one module or one integrated circuit chip, or may be distributed across multiple modules or chips. The processor and associated instructions together may be implemented in separate circuitry, fixed logic circuitry, hard coded logic, or the like. The controller may be implemented as part of the wireless transceiver 120, the processor/DSP 128, a dedicated processor configured to perform MPE techniques, a general purpose processor, some combination thereof, and so forth.

The processor/DSP 128 may include feature extraction circuitry 130 and SVM classification circuitry 132. Feature extraction circuitry 130 may be used to extract features of the reflected signal that are indicative of micro-motion characteristics of the human target. The SVM classification circuitry 132 may be used to determine a class or classification of the target object based on one or more extracted features of the reflected signal. For example, the SVM classification circuitry 132 may apply the extracted features to a classification model, wherein the SVM classification circuitry 132 determines the location of the target object within the feature space relative to boundaries that distinguish the object within the feature space into classes. Based on the location, the SVM classification circuitry 132 may identify a category of the target object, including human tissue, non-human objects, or a combination thereof.

In some examples, the base station 104 may be a data store or may be in communication with one or more data stores that receive micro-movement information from wireless communication devices via a wireless network. In this manner, the data store may communicate object classification information between wireless communication devices in the network. Based on this information, the data store may determine object classification information for one or more target objects based at least in part on the received micromovement information and, in some examples, other stored information. The object classification information may then be communicated to other wireless communication devices so that any of the wireless communication devices in the network may moderate wireless transmission parameters associated with their transmission and reception operations. In a further example, the data store may convey information indicating that any one or more of the wireless communication devices have moderated their power transmission levels, e.g., based on a target object classification.

Fig. 2 illustrates an example operating environment 200 for classifying target objects detected with a radar-based proximity detector. In the example environment 200, a user's hand 214 holds the electronic device 102. In an aspect, the electronic device 102 communicates with the base station 104 by transmitting an uplink signal 202(UL signal 202) or receiving a downlink signal 204(DL signal 204) via at least one antenna 126. However, the user's thumb may represent a proximate target object 206 that may be exposed to radiation generated via the uplink signal 202. To determine the distance to the target object 206, the electronic device 102 transmits the proximity detection signal 208-1 via at least one of the antennas 126 and receives the reflected proximity detection signal 208-2 via at least another one of the antennas 126.

In one implementation, the proximity detection signal 208-1 includes a Frequency Modulated Continuous Wave (FMCW) signal 216. Generally, the frequency of the PMCW signal 216 increases or decreases across a time interval. Different types of frequency modulation may be used, including Linear Frequency Modulation (LFM) (e.g., chirp), sawtooth frequency modulation, triangular frequency modulation, and so forth. The FMCW signal 216 enables radar-based ranging techniques to be used to determine the range to the target object 206. To achieve finer range resolution (e.g., on the order of centimeters (cm)) for short-range applications, larger bandwidths may be utilized, such as 1 gigahertz (GHz), 4GHz, 8GHz, and so forth. For example, the FMCW signal 216 may have a bandwidth of approximately 4GHz and include a frequency of approximately between 26 and 30 GHz. Finer range resolution improves range accuracy and enables multiple objects 206 to be distinguished by range. The FMCW signal 216 may provide accurate range measurements for various ranges based on bandwidth (e.g., between about 4-20 cm for a 4GHz bandwidth). The amount of time for performing proximity detection may be relatively short (such as within about one microsecond) when using the FMCW signal 216.

In another implementation, the proximity detection signal 208 may be a multi-tone signal 218 that includes at least three tones (e.g., frequencies). The multi-tone signal 218 may be generated using existing components within the wireless transceiver 120 that are also used to generate the uplink signal 202. For example, the multi-tone signal 218 may be generated using an existing phase-locked loop (PLL), using Orthogonal Frequency Division Multiplexing (OFDM), or using a multi-tone transmit signal generated at baseband via a digital signal generator. Depending on the technique used, the amount of time for performing proximity detection via the multi-tone signal 218 may be on the order of approximately one microsecond and 400 microseconds. The frequency spacing between tones may be on the order of megahertz (MHz) or GHz. The bandwidth of the multi-tone signal 218 may be, for example, about 800MHz or 2 GHz. The distance to the object 206 is determined by analyzing the phase change across each of these tones. To improve distance accuracy, a larger bandwidth (e.g., spacing between tones) or a larger number of tones may be used. The multi-tone signal 218 may be used to measure distances of between about 0 and 7 cm.

In some electronic devices 102, the antenna 126 may include at least two different antennas, at least two antenna elements 212 of the antenna array 210, at least two antenna elements 212 associated with different antenna arrays 210, or any combination thereof. As shown in fig. 2, antenna 126 corresponds to antenna element 212 within antenna array 210, and antenna element 212 may include a plurality of antenna elements 212-1 through 212-N, where N represents a positive integer. The wireless transceiver 120 may use at least one of the antenna elements 212 to transmit the proximity detection signal 208-1 while using at least another one of the antenna elements 212 to receive the reflected proximity detection signal 208-2. In other words, the wireless transceiver 120 may receive the reflected proximity detection signal 208-2 via the first antenna element 212-1 during a portion of the time that the proximity detection signal 208-1 is transmitted via the second antenna element 212-2. The antenna 124 and/or elements thereof may be implemented using any type of antenna, including patch antennas, dipole antennas, and the like.

If the electronic device 102 includes multiple antennas 126 located on different sides (e.g., top, bottom, or opposite sides) of the electronic device 102, the described techniques may enable a user to be detected with respect to each antenna 126. In this manner, the transmission parameters may be independently adjusted with respect to the distance of the object 206 relative to each antenna 126. Such independent detection thus enables two or more of the antennas 126 to be configured for different purposes. For example, one of the antennas 126 may be configured for enhanced communication performance while the other of the antennas 126 is simultaneously configured to comply with FCC requirements. As described in further detail with reference to fig. 3, some components of the wireless transceiver 120 may be used for both wireless communication and proximity detection at different times or simultaneously. In some examples, the electronic device may perform radar sensing and proximity detection during unused time slots of the wireless communication protocol. For example, in mmW communications, a communication frame may include one or more unused time slots for random channel access (RACH); the electronic device 102 may perform radar sensing and proximity detection during these otherwise unused RACH slots.

In some examples, when the electronic device 102 operates in a wireless communication network, the electronic device 102 may transmit the object send data to one or more other devices within the network. In this way, the electronic device 102 and the other devices may share target object sensing data with each other. Accordingly, the electronic device 102 and other devices may determine surrounding target object information for objects located around them.

Fig. 3 illustrates an example implementation of the wireless transceiver 120 and the processor/DSP circuitry 128 for a Machine Learning (ML) algorithm for classifying target objects detected with a radar-based proximity detector in accordance with some aspects of the present disclosure. The wireless transceiver 120 may include a transmitter 122 and a receiver 124 that are respectively coupled between the processor/DSP 128 and the antenna array 210. Transceiver 120 includes a Power Amplifier (PA)302 configured to dynamically provide power to selected ones of antenna elements 210 for adapting transmission parameters and/or for beamforming. The transceiver 120 further includes a Low Noise Amplifier (LNA)304 for amplifying signals received by the receive antenna 210-2. Local Oscillator (LO) circuitry 306 is coupled to

mixers

308 and 310. LO circuitry 306 generates at least one reference signal that enables

mixers

308 and 310 to up-convert or down-convert analog signals within a transmit chain or a receive chain, respectively. LO circuitry 306 may be further configured to generate one or more different types of reference signals to support both proximity detection and wireless communication. In some examples, LO circuitry 306 may be configured to generate one or more in-phase and quadrature (I/Q) reference signals. In this manner, the transmission from transmit antenna 210-1 may include I and Q components. And further, after receiving the reflected signal from receive antenna 210-2, the I and Q components of the reflected signal may be separated from each other for processing.

Transceiver 120 may also include other additional components not depicted in fig. 3. These additional components may include bandpass filters, additional mixers, switches, etc. Further, as described above, the transceiver 120 may be configured not only for target object ranging and detection, as will be described below, but also for wireless communication.

Although not explicitly depicted, the wireless transceiver 120 and/or the processor/DSP 128 may also include a controller. The controller may include at least one processor and at least one CRM, such as an application processor 108 and a CRM 110. The CRM may store computer-executable instructions, such as instructions 112. The processor and CRM may be located at one module or one integrated circuit chip, or may be distributed across multiple modules or chips. The processor and associated instructions together may be implemented in separate circuitry, fixed logic circuitry, hard coded logic, or the like. The controller may be implemented as part of the wireless transceiver 120, the processor/DSP 128, a dedicated processor configured to perform MPE techniques, a general purpose processor, some combination thereof, and so forth.

The Voltage Controlled Oscillator (VCO)312 may be configured to generate a sinusoidal signal having a frequency dependent on the voltage of the input signal v (t). That is, by appropriately varying the input signal v (t) to the VCO 312, the VCO 312 may generate, for example, a sine wave with increasing frequency over time, often referred to as a chirp signal. The chirp signal may be used for FMCW-based radar. Of course, other suitable input signals v (t) and other suitable radar configurations may be used for proximity detection and target object sampling within the scope of the present disclosure.

The chirp signal may be amplified by PA 302 and mixed (i.e., upconverted) with the LO signal at mixer 308 for transmission from transmit antenna 210-1. The transmitted signal may reflect off of the target object 314, reflected back to the receive antenna 210-2. The reflected signal at receive antenna 210-2 may be mixed (i.e., downconverted) with the LO signal at mixer 310 and amplified by LNA 304.

The output of the LNA 304 (i.e., the amplified received signal) may be mixed with the chirp signal at mixer 316. With FMCW-based radar, the mixing generates a beat signal that represents the frequency offset between the radio frequency transmit signal and the radio frequency receive signal. In general, the frequency of the beat signal is proportional to the distance of the target object 314.

The beat signal may be processed by baseband circuitry 318, and baseband circuitry 318 is configured to perform various baseband functions including, but not limited to, gain correction, skew correction, frequency translation, and so forth. The output from the baseband circuitry 318 may be converted to the digital domain using one or more analog-to-digital converters (ADCs) 320. In examples where the radar transmission includes I and Q components, as discussed above, the output from baseband circuitry 318 may include separate I and Q signals, and ADC 320 may include two ADCs for converting each of the I and Q components to the digital domain, respectively. The digital output from the ADC 320 may then be provided to the processor/DSP circuitry 128. In some implementations, the processor/DSP circuitry 128 may be a DSP or any suitable functional component for performing the processes described below.

An undesirable side effect of having a transmit antenna 210-1 and a receive antenna 210-2 in close proximity, as may occur in small electronic devices, is Mutual Coupling (MC). That is, a portion of the transmitted energy may be coupled back to the receiver. Such mutual coupling is a problem well known in the art. Within the processor/DSP circuitry 128, MC cancellation circuitry 322 may provide cancellation of undesired energy coupled between the transmit antenna 210-1 and the receive antenna 210-2. To remove the MC component from the received signal, MC cancellation circuitry 322 uses the transmit signal to cancel the MC component. Although not explicitly shown, MC cancellation may be performed in the time domain or the frequency domain via MC cancellation circuitry 322.

After MC is eliminated, Discrete Fourier Transform (DFT) circuitry 324 may convert the received beat signal to the frequency domain and provide samples of the beat signal in the domain. For example, if 30 measurements of the target object 314 are obtained from 30 sequential target object reflections, the output x from the DFT circuitry 324 includes x_i＝[x₁,x₂,…,x₃₀]As its output. Here, each sample x_iCorresponding to a spectrum measured from a single radar reflection. These samples x_iAnd may then be communicated to feature extraction circuitry 326. That is, according to aspects of the present disclosure, one or more features may be extracted from the spectrum of the radar sample sequence of the target object 314 (E.g., M features, as shown in the diagram). The extracted features may be used to classify the target object as human or non-human, for example, as described further below. That is, features indicative of micro-movement characteristics of the human target object may thus be used for classification of the target object.

The M extracted features may then be provided to classification circuitry 328. In some examples, classification circuitry 328 may be a Support Vector Machine (SVM) that utilizes Machine Learning (ML) to classify target objects. As described further below, the SVM classification circuitry 328 may determine the distance of the extracted features relative to boundaries in the defined feature space. The SVM classification circuitry 328 may then provide a determination (e.g., human or non-human) of a classification of the target object 314 based on a distance from and/or a location relative to such boundaries in the defined feature space. Also, as described further below, based on the classification of the target object 314, the processor/DSP circuitry 128 may generate transmission parameters that control one or more transmission properties for wireless communication. By specifying the transmission parameters, the processor/DSP circuitry 128 may, for example, cause the transceiver 120 to decrease the transmit power if the target object 314 near the electronic device 102 is a human, or to increase the transmit power if the target object 314 is far from the electronic device 102 and/or is not a human. For example, the power amplifier 302 may be dynamically controlled based on the target object classification. If the target object 314 is determined not to be a human, the processor 122 may, for example, leave the transmission parameters unchanged. The transmission parameters may adjust a power level, beam steering angle, frequency, selected antenna or antenna array, or communication protocol used to transmit the uplink signal and/or receive the downlink signal. The ability to determine the distance to the target object 314 and the class of the target object 314 and to control the transceiver 120 enables the processor 122 to balance the performance with the compliance or radiation requirements of the electronic device 102.

processor/DSP circuitry 128 may also be coupled to LO circuitry 306, which may enable processor/DSP circuitry 128 to control LO circuitry 306 via a mode signal. For example, the mode signal may cause LO circuitry 306 to switch between generating a reference signal for proximity detection or generating a reference signal for wireless communication. In other implementations, the application processor 108 (see fig. 1) may perform one or more of these functions.

Although wireless transceiver 120 is shown in fig. 3 as a direct conversion transceiver, the described techniques may also be applied to other types of transceivers, such as a superheterodyne transceiver. In general, LO circuitry 306 may be used to perform frequency conversion between any frequency stages (e.g., between baseband and radio frequencies, intermediate and radio frequencies, or between baseband and intermediate frequencies).

Fig. 4 illustrates a series of three charts generated using an exemplary implementation of the electronic device 102. Each illustrated graph shows data from 30 consecutive captures of a reflected radar pulse over a period of 9 seconds, with samples captured at 0.3 second intervals. In each respective graph, the horizontal axis represents time (or sample index) and the vertical axis represents distance from the electronic device (as determined using the distance detection algorithm generally described above). Furthermore, the shading at any given point represents the energy content of the received signal reflected from the target object at the corresponding time and distance from the electronic device. For example, the feature extraction circuitry 326 may determine parameters including the energy content of the received signal at each target distance, as well as other parameters.

The diagram 402 provides a data set corresponding to a stationary non-human target object, such as a coffee cup. As shown, such static stationary target objects are characterized by relatively static data across the sample. The chart 404 illustrates a data set corresponding to a moving human hand as a target object. This exhibited significant changes in the data over time. Also, chart 406 illustrates a data set corresponding to a human hand as a target object, where the person holds their hand stationary. Even when a person tries to stay perfectly stationary, they cannot eliminate micromovements caused by small muscle movements, breathing, blood vessel pulses, etc. Because the integrated FMCW-based radar using mmW spectrum has a wavelength on the order of 1cm or less, it is able to detect very small movements (such as 2 or 3mm) in the target object.

By observing data from various objects, such as these objects, the inventors have recognized that when a detected target object has human properties, the observed data exhibits variations or fluctuations in various metrics or characteristics (such as peak energy of the reflected signal, side lobe variations, etc.). And furthermore, by analyzing these fluctuations on a suitable set of features extracted from the target object, the target object can be reliably and accurately classified as a human target object or a non-human target object. According to various aspects of the present disclosure, a Machine Learning (ML) algorithm for classifying a target object using these and other features is provided. In this way, in some examples, the transmission characteristics may be controlled to dynamically meet MPE requirements for mmW transmission.

Fig. 5 provides two diagrams illustrating an exemplary two-dimensional (2D) feature space. These graphs provide examples of how to combine the use of suitable sets of extracted features to improve the reliability of classifying target objects detected using a radar-based proximity detector as described above. In the context of the present disclosure, a feature refers to a determined parameter of a classification problem that is related to or specific to a target object. That is, the electronic device 102 may extract one or more features to determine whether the target object is a human.

In the graph shown in fig. 5, each point corresponds to a sample of data collected from the measurement, post-processing of the target object. In the first graph 502, the horizontal axis represents the peak power minus the mean power variance of the reflected signal over 30 sequential radar reflections. The vertical axis represents the maximum of the Discrete Fourier Transform (DFT) of the power of the reflected signal minus the average of the DFT of the power of the reflected signal. And, in the second graph 504, the horizontal axis represents the average value of the phase change (Δ) between the sample n and the sample n-1 over 30 sequential radar reflections; while the vertical axis represents the variance of the phase change (delta) between sample n and sample n-1.

In each of these charts, each '×' represents data points from a human target object, and each '. smallcircle' represents data points from a non-human target object. As can be clearly seen, the circle 'o' representing the non-human target object sample forms a cluster in the lower left corner. In another aspect,' representing a sample of human target objects are spread across the chart. Thus, in these exemplary illustrations, simple linear boundary separation can be used to distinguish between human and non-human samples.

Fig. 6 illustrates a further example of a 2D feature space, showing one example of how a classifier algorithm may utilize data from known classified target objects to determine an optimal separation between different classes of target objects. In this illustration, the notation x₁And x₂The axis of (a) represents a feature extracted from the target object. The data points shown with filled circles (●) each correspond to a human target object, while the data points shown with blank circles (∘) each correspond to a non-human target object.

The diagrams in fig. 5 and 6 show a 2D feature space comparing two extracted features to obtain a classification between a human target object and a non-human target object. However, aspects of the present disclosure are not limited to such 2D feature spaces. In general, due to the higher dimension of the feature space, a multi-dimensional comparison between any suitable number of features may be constructed. In such cases, rather than the line 602 serving as a boundary between classes, a plane or hyperplane may be utilized to separate the classes of the target object within the higher dimensional feature space. That is, the example classification circuitry 328 may utilize SVMs to analyze any suitable number of features extracted from a sample set from a target object and determine a classification of the target object.

Referring again to fig. 6, it can be observed that data points of different categories can be easily separated from each other. However, in order to optimally separate the different classes to most reliably classify new data from the target object in the future, the classification model should identify the optimal separation between the classes. For example, an infinite number of lines (such as line 602) can theoretically completely separate measurements in a given data set. However, if the classification model were to utilize the illustrated line 604 to predict a category of a future target object, the prediction may not be reliable. That is, because line 604 is close to a cluster that passes through the measured human target object, even small variations in the extracted features of future measurements of the human target object may cause the measurements to fall on opposite sides of line 604, resulting in the model misclassifying the target object.

In an aspect of the present disclosure, a Machine Learning (ML) algorithm may be used to establish reliable separation into different categories (e.g., human and non-human target objects) between a set of target objects. That is, the classifier algorithm may be built by constructing a large data set (e.g., training data) that includes many human body parts (e.g., different gestures of hands, arms, faces, etc.) as well as many non-human objects typically encountered by electronic devices.

As one example, classification circuitry 328 may be a Support Vector Machine (SVM), which may be used to construct a target object classifier algorithm. SVMs are ML models known in the art for classification of data sets. Broadly, SVMs can be used to analyze a data set and identify boundaries between classes by maximizing the minimum distance between the nearest point in the sample set of each class and the boundary. These boundaries are called support vectors.

Referring again to FIG. 6, the diagram shows data corresponding to eight realizations of the human target object (●) and eight realizations of the non-human target object (∘). Here, the implementation corresponds to a set of radar captures or observations received after reflection from the target object based on the radar transmissions. According to an aspect of the disclosure, by selecting suitable features x extracted from each implementation₁And x₂It can be observed that the two categories of data can be divided into different clusters. In a further aspect, classification circuitry 328 (e.g., an SVM) may be used to compute optimal boundaries between classes based on these data.

As discussed above, in a two-dimensional feature space as illustrated in fig. 6, the boundaries correspond to lines. In one aspect of the present disclosure, the line may be represented by a value of wx-b ═ 0. Here, w is a weight vector; x is a vector in feature space<x₁,x₂>(ii) a And b is a scalar offset or offset. In the formula, the weightThe vector w is configured such that the product wx of the two vectors results in a scalar value.

As seen in FIG. 6, in the two-dimensional feature space, the separation between lines parallel to the boundary 602 that pass through the closest samples in each class has

The value of (c). The boundary 602 may be selected to be centered between these respective lines. Here, | w |' represents the norm of w, | w | is calculated as the root of the sum of the squares of all the elements of the vector (in the illustrated case,

and as further seen in figure 6,

is the distance or offset of the boundary 602 from the origin.

Based on the analysis of the data set, classification circuitry 328 (e.g., an SVM) determines values for w and b for the optimal boundary 602 between classes based on training data given thereto. That is, although the illustration shows a total of 16 samples or realizations, with 8 samples or realizations each from a human target object and a non-human target object, any suitable number of samples may be used as the training data set. In general, the larger and more diverse/varied in nature the training data used, the more reliable the calculated boundary 602 for determining the class of the new incoming sample.

In general, classification circuitry 328 may generate boundaries (e.g., lines, planes, or hyperplanes, depending on the dimensions in the feature space) to separate samples from different classes. That is, the SVM defines a boundary that separates the samples from the different classes such that the minimum distance between the samples in each class and the boundary is maximized. With this boundary, a robust way of distinguishing between different classes may be provided.

By predetermining such boundaries, the computational cost of classifying a new target object by the electronic device 102 may be reduced. That is, a deep learning algorithm or a neural network algorithm may potentially determine the separation between classes in real time. However, the use of such algorithms would come at the expense of high computational requirements, high power usage, long computation times, and even higher system costs.

Some examples of features that may be used to distinguish a human target object from a non-human target object are provided below. According to an aspect of the invention, feature extraction circuitry 326 may analyze the set of realizations (reflected signals) from the target object as described above to extract a set of M features corresponding to the target object.

In some examples, the feature extraction circuitry 326 may utilize Dynamic Time Warping (DTW). DTW is an algorithm known in the art for determining similarity between sequences (e.g., sequences X and Y, each having L samples) and is defined according to the following equation:

wherein X ═ { X ═ X₁,x₂,…,x_LY ═ Y₁,y₂,…,y_L}. Thus, for each sample in X and each sample in Y, DTW depends on a comparison of distances between corresponding values in the DFT domain. In general, the DTW of two very similar sequences may be very small, while the DTW of two very different sequences may be large.

For example, the feature extractable feature of feature extraction circuitry 326 is a variance (Var) of DTW over a series of realizations (e.g., a series of 10 or any suitable number of realizations)_DTW). In the above formula, Var_DTWCorresponding to the variance of the calculated DTW across n realizations. For static (e.g., non-human) objects, the variance of the determined DTW over a series of (e.g., a series of 10) realizations will be very small, as each realization will provide a basisThe same data as above. On the other hand, for a human target object, a series (e.g., a series of 10) realizations will have a significant difference from each other due to movement or micromotion of the human target object. Thus, the variance of the DTW over a range of implementations will be greater than the variance of the non-human target object.

In another example, the feature extractable feature of feature extraction circuitry 326 is a maximum (Max) of DTW over a range of implementations (e.g., a range of 10 or any suitable number of implementations)_DTW). In the above equation, Max_DTWCorresponding to the maximum of the calculated DTW across n realizations. Here, the maximum value of DTW may be considered as an extension between different implementations. By utilizing the maximum DTW, the electronic device may further determine that the maximum DTW is relatively high even though the human target object is fairly stable for, for example, 5 realizations, but then exhibits motion. Accordingly, even for a target object that is temporarily not resident, it is possible to classify the target object as a human with strong movement.

In a further example, the feature extractable by feature extraction circuitry 326 is the variance (var) of the difference between the peak power in each implementation and the average peak power across the sequence of implementations. For example, after DFT circuitry 324 can calculate a DFT for a given implementation. The DFT may provide the received power of the sample at each frequency in a range of frequencies. In examples utilizing FMCW-based radar, these frequencies correspond to distances from the electronic device. Thus, by plotting power P versus distance, one or more local maxima (maxima) or peaks (e.g., i local maxima) may occur for each respective sample. In this extracted feature, an average (avg) peak may be determined across multiple realizations. Here, the change (Δ) in the peak power level detected for each sample n relative to the average peak value may be determined; and across multiple implementations, the variance of the change can be determined. According to an aspect of the disclosure, the value of the feature may be higher for a human target object than for a non-human target object.

In a further example, the features extractable by feature extraction circuitry 326 are variances of differences between distances at which the peak power is located in each implementation and average distances at which the peak power is located across the sequence of implementations. This feature is similar to that described above for Var _ Δ P_{avg_max}The description is very similar. Here, however, rather than looking at the measured power, the feature looks at the measured distance from where the peak power was captured. Similar to the above, for a human target object, the value of the feature may be higher than the value of a non-human target object.

In a further example, the feature extractable by the feature extraction circuitry 326 is a spread (Δ) of the peak power (peak _ pwr) of a DFT of an implementation over a series of n (e.g., a series of 10) implementations. That is, as shown in equation 5 above, spreading is defined as the difference between the maximum (max) peak power of the DFT and the minimum (min) peak power of the DFT across a range of realizations. For stationary non-human target objects, the extension will be expected to be relatively small, whereas for human target objects exhibiting at least micromovements, the extension will be expected to be larger.

In a further example, the feature extractable circuitry 326 may extract a feature that is the variance of the peak power across a series of n (e.g., a series of 10) realizations of DFT. For stationary non-human target objects, the variance would be expected to be relatively small, while for human target objects that exhibit at least micromovements, the variance would be expected to be larger.

In a further example, the feature extractable circuitry 326 may extract a feature that is a variance of the measured power change (Δ) in consecutive captures. In equations 1-6 above, the features extracted depend on the relationship across the entire implementation sequence. However, in equation 7 (and equation 8 below), the extracted features depend on the relationship between consecutive or sequential individual captures or realizations. When the target object is human, there may be relatively large variations in peak power from one capture to the next due to micromovements that may occur at any given time.

By using the variance of the parameter as the extracted feature, micro-movement characteristics of the human target object may be identified.

In a further example, the feature extractable circuitry 326 may extract a feature that is a variance of a change in distance (Δ) over which peak power occurs in consecutive or sequential captures or realizations. When the target object is human, there may be relatively significant variation in peak power distance from one capture to the next due to micromovements that may occur at any given time. By using the variance of the parameter as the extracted feature, micro-movement characteristics of the human target object may be identified.

In some further examples, feature extraction circuitry 326 may extract features based on in-phase and quadrature (I/Q) samples in the time domain after removing mutual coupling. As one example, a sequence of n consecutive time domain samples (e.g., 10 samples) may be collected using FMCW-based radar. The real part of the time domain samples may be determined according to:

and further, the imaginary part of the time domain samples may be determined according to:

when collecting samples of a human target object, even in the time domain, there may be 'noise' or variations in the measured power of consecutive samples, e.g. due to micromovements. However, when collecting samples of a stationary non-human target object, the measured power of consecutive samples may generally be relatively stable. Accordingly, the electronic device 102 may utilize the calculated parameters (such as the variance of the time domain samples) to classify the target object. In a further example, the average of the I/Q samples may be removed (mean removal). For example, the calculated average or mean across the sample set may be subtracted from the value of each sample. In this way, any bias or offset that may affect the final result may be accounted for.

Thus, in one example, the features that may be extracted by feature extraction circuitry 326 are time domain samples (IQ)_n) Variance (Var { }) of the real part (Re ()), with mean removal as described above.

In a further similar example, the feature extractable by feature extraction circuitry 326 is a variance of the imaginary part (Im ()) of the time domain samples, with mean removal as described above.

Fig. 7 is a flow diagram illustrating an example process for building a human target object classifier in accordance with some aspects of the present disclosure. In various examples, some or all of the illustrated features may be omitted from a particular implementation within the scope of the disclosure. Furthermore, some illustrated features may not be required for implementation of the particular example. In some examples, the process in fig. 7 may be performed by a manufacturer, vendor, or retailer of the electronic device 102. In some examples, process 7 in fig. 7 may be performed by any suitable equipment or means for performing the functions or algorithms described below.

At block 702, a data collection process may be performed. For example, the electronic device 102 may collect radar capture datasets for multiple humans (e.g., humans of different genders, races, ages, sizes, etc.). Such data may be collected from various body parts of the person under test. Further, the electronic device 102 may collect further radar capture data for a plurality of non-human target objects of various types and characteristics. Here, it may be advantageous to maximize the size of the data set and the variety of target objects.

At block 704, the data set may undergo post-processing to extract features that may be used to distinguish between human target objects and non-human target objects. For example, one or more of the features described above with respect to equations 1-10, as well as any other suitable features that may be used to distinguish a target object, may be extracted from the dataset.

At block 706, a classifier model may be constructed and trained based on the extracted features and the collected data set. That is, the extracted features can be utilized to train and verify the performance of the classifier based on known classifications of samples in the dataset. Also, at block 708, the SVM may be established by mapping the feature space and calculating the distance from the determined boundary (e.g., in the multi-dimensional feature space, hyperplane boundary) to the mapped data point. At block 710, the performance of the constructed human classifier may be tested for accuracy in real-time based on the detection of target objects unknown to both humans and non-humans (for the classifier). When the reliability of the classifier is suitably high, then the classifier can be deployed to the user.

Fig. 8 is a flow diagram illustrating an exemplary process for classifying a target object in accordance with some aspects of the present disclosure. Although a human being may be used as an example, any living animal may be the basis for adjusting the transmission power. As described below, some or all of the illustrated features may be omitted in particular implementations within the scope of the present disclosure, and some of the illustrated features may not be required to implement all embodiments. In some examples, the process may be performed by the electronic device 102 illustrated in fig. 1 or 2. In some examples, the process may be performed by various components of an electronic device, including but not limited to the transceiver 120 and the processor or DSP circuitry 122 as illustrated in fig. 3. In some examples, the process of fig. 8 may be performed by any suitable equipment or means for performing the functions or algorithms described below.

At block 802, the electronic device 102 may transmit a detection signal. For example, transceiver 120 may utilize one or more antennas (such as transmit antenna 210-1) to transmit pulses, FMCW signals, multi-tone signals, or any other suitable signals for radar-based proximity detection. At block 804, the electronic device 102 may receive a reflected signal reflected from the target object. For example, transceiver 120 may utilize one or more antennas (such as receive antenna 210-2) to receive the reflected signal.

At block 806, the electronic device 102 may extract one or more features of the reflected signal. For example, the feature extraction circuitry 326 may process information corresponding to the reflected signal (such as a spectrum of one or more radar samples of the reflected signal) to determine one or more features for characterizing the target object. In some examples, a given feature may correspond to a single realization or reflection from a target object. In other examples, a given feature may correspond to multiple implementations, such as a sequence of any suitable number of implementations.

At block 808, the electronic device 102 can apply the extracted features to a classification model. For example, the SVM 810 may be configured according to a set of training data 812 to establish one or more boundaries 814. The one or more boundaries may be configured to separate classes of target objects in a feature space based on features extracted from reflected signals received back from the target objects. Establishing boundaries 814 based on training data 812 may be according to a classification model 816 established using the procedures described above and illustrated in fig. 7.

At block 818, the electronic device 102 may determine a category of the target object based on one or more characteristics of the reflected signal. For example, the electronic device 102 can determine a location of the target object relative to the boundary 814 within a feature space of the classification model 816. With this location, the electronic device 102 may identify a category of the target object based on the location within the feature space (e.g., on which side of the boundary 814 the target object is located within the feature space).

If the class of the target object indicates that the target object is a human, at block 820, the electronic device 102 may adjust at least one transmission parameter of the transmission signal (such as a mmW uplink signal) to provide a Maximum Permissible Exposure (MPE) for the human target object that is not greater than the mmW signal. For example, the electronic device 102 may adjust at least one of a power level of the uplink signal, a beam steering angle of the uplink signal, a frequency of the uplink signal, a selected antenna of the uplink signal, a communication protocol of the uplink signal, or a combination thereof, such that the power of the uplink signal at the human target object is not greater than the MPE regulatory requirements. On the other hand, if the class of the target object indicates that the target object is non-human, then at block 822, the electronic device 102 may adjust at least one transmission parameter of the transmission signal without accounting for MPE regulatory rules. For example, the electronic device may cause the power of the transmitted signal at the non-human target object to exceed the MPE level to adjust the transmission parameter. At block 824, the electronic device 102 may transmit the adjustment signal using the adjusted transmission parameters, as described above.

The process shown in fig. 8 may include additional aspects, such as any single aspect or any combination of aspects described below and/or in conjunction with one or more other processes described elsewhere herein.

In a first aspect, an electronic device may adjust one or more transmission parameters based on a determined category of a target object. Here, the transmission parameter may be at least one of a power level, a beam steering angle, a frequency, a selected antenna, a communication protocol, or some combination thereof.

In a second aspect, alone or in combination with the first aspect, the category of the target object is one of: a human target object, or a non-human target object; an animal target object, or a non-animal target object; or a live target object or a non-live target object.

In a third aspect, alone or in combination with one or more of the first and second aspects, an electronic device may determine a category of a target object based on one or more characteristics of the reflected signal. Here, the one or more features of the reflected signal may include one or more features indicative of micro-movement characteristics of the human target object.

In a fourth aspect, alone or in combination with one or more of the first to third aspects, determining the category of the target object may comprise: the method includes extracting one or more features of the reflected signal, applying the one or more extracted features to a classification model configured with boundaries that classify objects into classes within a feature space, determining a location of the target object within the feature space relative to the boundaries, and identifying the class of the target object based on the location within the feature space.

In a fifth aspect, alone or in combination with one or more of the first to fourth aspects, the classification model corresponds to a Support Vector Machine (SVM). Here, the boundary within the feature space is determined based on the training data set.

In a sixth aspect, alone or in combination with one or more of the first through fifth aspects, the adjusted signal comprises a millimeter wave (mmW) signal. Here, the class of target object is a human target object, and adjusting the at least one transmission parameter includes configuring the adjusted signal to provide a Maximum Permissible Exposure (MPE) for the human target object that is no greater than the mmW signal.

In a seventh aspect, alone or in combination with one or more of the first to sixth aspects, the one or more characteristics of the reflected signal comprise at least one of: sampling a series of implemented dynamic time warping variances of the target object; sampling a series of realized maximum values of dynamic time warping of the target object; sampling a variance of a difference between a peak power in each of a series of realizations of the target object and an average peak power across the series of realizations sampling the target object; a variance of a difference between a distance at which a peak power is located in each of the series of realizations of sampling the target object and an average distance at which the peak power is located in the series of realizations of sampling the target object; sampling a spread of peak power of a series of implemented Discrete Fourier Transforms (DFT) of the target object; sampling a variance of peak power of a series of realized DFTs of the target object; a variance of the measured power variations in sampling consecutive realizations of the target object; a variance of distances at which peak power occurs in sampling consecutive realizations of the target object; sampling a variance of a real part of the realized time-domain samples of the target object; sampling a variance of an imaginary part of realized time-domain samples of the target object; or a combination thereof.

In an eighth aspect, alone or in combination with one or more of the first through seventh aspects, an electronic device or a wireless communication device includes a housing shaped and dimensioned to carry one or more components including a memory, a wireless transceiver, a power amplifier, and at least one processor. Here, the memory stores data configured to enable the at least one processor to process object sensing information for the stored data, thereby enabling classification of one or more objects into one or more object type categories.

In a ninth aspect, alone or in combination with one or more of the first to eighth aspects, the wireless transceiver is configured to participate in mmW communication and mmW object sensing substantially simultaneously.

In a tenth aspect, alone or in combination with one or more of the first to ninth aspects, the wireless communication device comprises an antenna module having an array of antenna elements, wherein the power amplifier is configured to dynamically provide power to selected ones of the antenna elements for adapting transmission parameters and/or for beamforming.

In an eleventh aspect, alone or in combination with one or more of the first through tenth aspects, the wireless communication device includes an interface circuit for interfacing with an auxiliary device spaced apart from the housing. The interface circuit is configured to enable communication between the wireless communication device and the auxiliary device such that the wireless communication device receives object sensing information from the auxiliary device and, in response, moderates transmission parameters associated with the wireless transceiver transmitting and/or receiving millimeter waves.

In a twelfth aspect, alone or in combination with one or more of the first to eleventh aspects, the at least one processor is configured to determine whether the sensed object is capable of being associated with one or more object categories, wherein the one or more object categories include: non-human tissue, or a combination thereof.

In a thirteenth aspect, alone or in combination with one or more of the first to twelfth aspects, the wireless transceiver is configured to sense an object over a range from about 1 degree to about 360 degrees to enable the at least one processor to generate an object sensing map of the object outside the enclosure.

In a fourteenth aspect, alone or in combination with one or more of the first through thirteenth aspects, the at least one processor is configured to generate a map based at least on the object sensing information, wherein the map identifies objects with respect to the wireless communication device.

In a fifteenth aspect, alone or in combination with one or more of the first to fourteenth aspects, the wireless transceiver is configured to sense objects relative to the housing and external to the housing via mmW signaling by repeated transmission of millimeter wave signals toward and reception of mmW signals from one or more objects such that the wireless transceiver is configured to observe micro-movements occurring by the one or more objects.

In a sixteenth aspect, alone or in combination with one or more of the first to fifteenth aspects, the wireless communication device receives signaling from one or more other wireless communication devices indicating that any of the other wireless communications have moderated one or more wireless transmission parameters based on the object classification information.

In a seventeenth aspect, alone or in combination with one or more of the first to sixteenth aspects, the wireless communication device determines surrounding information of objects surrounding one or more other wireless communication devices.

In an eighteenth aspect, the wireless transmission parameter relates to the power of the transmitted or received millimeter wave signal, alone or in combination with one or more of the first to seventeenth aspects.

In a nineteenth aspect, the wireless communication device is configured as a vehicle, alone or in combination with one or more of the first through eighteenth aspects. Here, the at least one processor is configured to control one or more operating parameters of the vehicle body based at least in part on the object sensing information.

In a twentieth aspect, alone or in combination with one or more of the first to nineteenth aspects, the wireless communication device is configured for gaming. Here, the one or more user interfaces are located proximate a housing of the gaming device, and the at least one processor is further configured to receive user input via the one or more user interfaces located proximate the housing and, in response, convey the object sensing information to the user.

In a twenty-first aspect, alone or in combination with one or more of the first to twentieth aspects, the gaming device comprises a visual interface field defining a visual display configured to visually convey object sensing information to a user.

In one configuration, the electronic device 102 includes: the apparatus includes means for transmitting a detection signal, means for receiving a reflected signal reflected from a target object, and means for transmitting an adjusted signal using a transmission parameter. In one aspect, the aforementioned means may be the transceiver 120. In one aspect, the aforementioned means may be the processor or DSP circuitry 128 shown in fig. 1 and 3 that is configured to perform the functions recited by the aforementioned means. In another aspect, the aforementioned means may be circuitry or any apparatus configured to perform the functions recited by the aforementioned means. The electronic device 102 may further include: the method includes determining a class of a target object based on one or more characteristics of the reflected signal, and adjusting at least one transmission parameter based on the class of the target object. In one aspect, the aforementioned means may be the processor or DSP circuitry 128 shown in fig. 1 and 3 that is configured to perform the functions recited by the aforementioned means. In another aspect, the aforementioned means may be circuitry or any apparatus configured to perform the functions recited by the aforementioned means.

Of course, in the above examples, the circuitry included in the processor or DSP circuitry 128 is provided as an example only, and other means for performing the described functions may be included within aspects of the present disclosure, including but not limited to instructions stored in the computer-readable storage medium 110, or any other suitable apparatus or device described in any of fig. 1, 2, and/or 3 and utilizing, for example, the processes and/or algorithms described herein with respect to fig. 8.

Within this disclosure, the word "exemplary" is used to mean "serving as an example, instance, or illustration. Any implementation or aspect described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects of the disclosure. Likewise, the term "aspect" does not require that all aspects of the disclosure include the discussed feature, advantage or mode of operation. The term "coupled" is used herein to refer to a direct or indirect coupling between two objects. For example, if object a physically contacts object B, and object B contacts object C, objects a and C may still be considered to be coupled to each other even though they are not in direct physical contact with each other. For example, a first object may be coupled to a second object even though the first object is never in direct physical contact with the second object. The terms "circuitry" and "circuitry" are used broadly and are intended to include both hardware implementations of electronic devices and conductors that when connected and configured enable the functions described in this disclosure to be performed, without limitation as to the type of electronic circuitry, and software implementations of information and instructions that when executed by a processor enable the functions described in this disclosure to be performed.

One or more of the components, steps, features and/or functions illustrated in fig. 1-8 may be rearranged and/or combined into a single component, step, feature or function or implemented in several components, steps or functions. Additional elements, components, steps, and/or functions may also be added without departing from the novel features disclosed herein. The apparatus, devices, and/or components illustrated in fig. 1-8 may be configured to perform one or more of the methods, features, or steps described herein. The novel algorithms described herein may also be efficiently implemented in software and/or embedded in hardware.

It is to be understood that the specific order or hierarchy of steps in the methods disclosed is an illustration of exemplary processes. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the methods may be rearranged. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented unless specifically recited herein.

The previous description is provided to enable any person skilled in the art to practice the various aspects described herein. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects. Thus, the claims are not intended to be limited to the aspects shown herein, but is to be accorded the full scope consistent with the language claims, wherein reference to an element in the singular is not intended to mean "one and only one" unless specifically so stated, but rather "one or more. The term "some" or "an" refers to one or more, unless specifically stated otherwise. A phrase referring to at least one of a list of items refers to any combination of those items, including a single member. By way of example, "at least one of a, b, or c" is intended to encompass: a; b; c; a and b; a and c; b and c; and a, b and c. All structural and functional equivalents to the elements of the various aspects described throughout this disclosure that are known or later come to be known to those of ordinary skill in the art are expressly incorporated herein by reference and are intended to be encompassed by the claims. Moreover, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims. No element of the claims should be construed under the provisions of 35 u.s.c. § 112(f), unless the element is explicitly recited using the phrase "means for … …" or in the case of method claims the element is recited using the phrase "step for … …".

Claims

1. A method for classification of a target object, the method comprising:

transmitting a detection signal;

receiving a reflected signal reflected from the target object;

determining a category of the target object based on one or more characteristics of the reflected signal;

adjusting at least one transmission parameter based on the category of the target object; and

transmitting an adjusted signal using the transmission parameter.

2. The method of claim 1, wherein the transmission parameters comprise at least one of power level, beam steering angle, frequency, selected antenna, communication protocol, or a combination thereof.

3. The method of claim 1, wherein the category of the target object is one of:

a human target object, or a non-human target object;

an animal target object, or a non-animal target object; or

A live target object, or a non-live target object.

4. The method of claim 1, wherein the one or more features of the reflected signal include one or more features indicative of micro-movement characteristics of a human target object.

5. The method of claim 1, wherein determining the category of the target object comprises:

extracting the one or more features of the reflected signal;

applying the one or more extracted features to a classification model configured with boundaries that classify objects into classes within a feature space;

determining a position of the target object within the feature space relative to the boundary; and

identifying the category of the target object based on the location within the feature space.

6. The method of claim 1, wherein the first and second light sources are selected from the group consisting of a red light source, a green light source, and a blue light source,

wherein the adjusted signal comprises a millimeter-wave signal,

wherein the class of the target object is a human target object, and

wherein adjusting at least one transmission parameter comprises configuring the adjusted signal to provide no more than a maximum permitted exposure of the human target object to the millimeter wave signal.

7. The method of claim 1, wherein the one or more characteristics of the reflected signal comprise at least one of:

sampling a series of implemented dynamic time-warping variances of the target object;

sampling a maximum of the dynamic time warping of the series of realizations of the target object;

sampling a variance of a difference between a peak power in each of the series of realizations of the target object and an average peak power across the series of realizations sampling the target object;

a variance of a difference between a distance at which a peak power is located in each of the series of realizations of sampling the target object and an average distance at which the peak power is located in the series of realizations of sampling the target object;

sampling a spread of peak power of the series of implemented Discrete Fourier Transforms (DFTs) of the target object;

sampling a variance of the peak power of the DFT for the series of realizations of the target object;

a variance of measured power changes in sampling consecutive realizations of the target object;

a variance of distances at which peak power occurs in sampling consecutive realizations of the target object;

sampling a variance of real parts of realized time-domain samples of the target object;

sampling a variance of an imaginary part of the realized time-domain samples of the target object;

or a combination thereof.

8. The method of claim 1, wherein the method is operable at an electronic device, the method further comprising:

sensing one or more target objects including the target object relative to the electronic device; and

communicating information associated with the one or more target objects positioned relative to the electronic device.

9. An electronic device configured for classification of a target object, the electronic device comprising:

a processor;

a transceiver communicatively coupled to the processor; and

a data storage medium communicatively coupled to the processor,

wherein the processor is configured to:

transmitting a detection signal via the transceiver;

receiving, via the transceiver, a reflected signal, the reflected signal reflecting from the target object;

transmitting, via the transceiver, an adjusted signal using the transmission parameters.

10. The electronic device of claim 9, wherein the transmission parameter comprises at least one of a power level, a beam steering angle, a frequency, a selected antenna, a communication protocol, or a combination thereof,

wherein the one or more features of the reflected signal include one or more features indicative of micro-movement characteristics of a human target object, and

wherein the category of the target object is one of:

a human target object, or a non-human target object;

an animal target object, or a non-animal target object; or

A live target object, or a non-live target object.

11. The electronic device of claim 9, wherein the processor configured to determine the category of the target object is further configured to:

extracting the one or more features of the reflected signal;

12. The electronic device as set forth in claim 9,

wherein the adjusted signal comprises a millimeter-wave signal,

wherein the class of the target object is a human target object, and

wherein the processor configured to adjust at least one transmission parameter is further configured to adjust the signal to provide no more than a maximum permitted exposure of the millimeter wave signal to the human target object.

13. The electronic device of claim 9, wherein the one or more characteristics of the reflected signal comprise at least one of:

or a combination thereof.

14. The electronic device of claim 9, wherein the processor is further configured to sense, with the transceiver, one or more target objects, including the target object, relative to the electronic device and communicate information associated with the one or more target objects positioned relative to the electronic device.

15. A wireless communication device, comprising:

a housing shaped and sized to carry one or more components including a memory, a wireless transceiver, a power amplifier, and at least one processor;

the wireless transceiver is configured to transmit and/or receive millimeter wave signals via a wireless channel;

the wireless transceiver is further configured to sense objects relative to and external to the enclosure via millimeter wave signaling and to provide object sensing information to the at least one processor; and is

Wherein the at least one processor is configured to control the power amplifier based on the object sensing information to moderate transmission parameters associated with the wireless transceiver transmitting and/or receiving millimeter waves and to communicate information associated with sensing objects positioned relative to and external to the enclosure.

16. The wireless communication device of claim 15, wherein the wireless transceiver is configured to participate in millimeter wave communication and millimeter wave object sensing substantially simultaneously.

17. The wireless communication device of claim 15, further comprising an antenna module having an array of antenna elements, wherein the power amplifier is configured to dynamically provide power to selected ones of the antenna elements for adapting the transmission parameters and/or for beamforming.

18. The wireless communication device of claim 15, further comprising an interface circuit for interfacing with an accessory device spaced apart from the housing, the interface circuit configured to enable communication between the wireless communication device and the accessory device such that the wireless communication device receives object sensing information from the accessory device and, in response, moderates transmission parameters associated with the wireless transceiver transmitting and/or receiving millimeter waves.

19. The wireless communication device of claim 15, wherein the at least one processor is configured to determine whether a sensed object can be associated with one or more object categories, wherein the one or more object categories comprise: non-human tissue, or a combination thereof.

20. The wireless communication device of claim 15, wherein the wireless transceiver is further configured to sense an object over a range from about 1 degree to about 360 degrees to enable the at least one processor to generate an object sensing map of an object outside the housing.

21. The wireless communication device of claim 15, wherein the at least one processor is configured to generate a map based at least on the object sensing information, wherein the map identifies objects with respect to the wireless communication device.

22. The wireless communication device of claim 15, wherein the wireless transceiver is configured to sense objects relative to the housing and external to the housing via millimeter wave signaling by repeated transmission of millimeter wave signals toward one or more objects and repeated reception of millimeter wave signals from the one or more objects such that the wireless transceiver is configured to observe micro-movement occurring by the one or more objects.

23. The wireless communication device of claim 15, wherein the at least one processor is further configured to:

extracting one or more features of the object sensing information;

applying the one or more extracted features to a classification model configured with boundaries that classify the object into classes within a feature space;

determining a position of the object within the feature space relative to the boundary; and

identifying the category of the object based on a location of the object within the feature space.

24. A wireless communication device, comprising:

a housing shaped and sized to carry one or more components including a memory and at least one processor;

a wireless communication interface sized and shaped to be positioned adjacent to or within the housing;

the wireless communication interface is configured to transmit and/or receive millimeter wave signals via a wireless channel;

the wireless communication interface is further configured to sense an object relative to the housing via millimeter wave signaling and configured to provide object sensing information to at least one processor; and is

Wherein the at least one processor is configured to control transmission parameters associated with the wireless communication interface transmitting and/or receiving millimeter waves based on the object sensing information and to communicate information associated with sensing an object relative to the housing.

25. The wireless communication device of claim 24, wherein the wireless communication device is configured as a vehicle,

wherein the enclosure comprises a body configured to carry at least one of a payload or a passenger, and

wherein the at least one processor is configured to control one or more operating parameters of the vehicle body based at least in part on the object sensing information.

26. The wireless communication device of claim 24, wherein the wireless communication device is configured for gaming,

wherein the housing is sized and shaped for gaming to allow a user to participate in an electronic gaming environment;

the wireless communication device further comprises one or more user interfaces located proximate to the housing, wherein the at least one processor is further configured to receive user input via the one or more user interfaces located proximate to the housing and, in response, communicate object sensing information to the user.

27. The wireless communication device of claim 26, further comprising a visual interface field defining a visual display configured to visually convey object sensing information to the user.