Abstract: Systems and methods relate to in-vehicle sensing and classification via frequency modulated continuous wave (FMCW) radar. Radar reflection signals are received based on the radar transmission signals, and include a plurality of chirps across a plurality of frames. Range-FFT data is generated by performing a fast Fourier transform (FFT) on each chirp of a particular frame. Doppler-FFT data is generated by performing the FFT on the range-FFT data. Point cloud data of a radar subject is generated using the Doppler-FFT data. Doppler features, including a velocity of the radar subject, are extracted from the Doppler-FFT data. Classification data is generated to indicate a sensing state inside a vehicle based on the point cloud data and the Doppler features. The classification data includes class data that classifies the radar subject. A system response is generated to provide an action concerning the sensing state inside the vehicle based on the classification data.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an image source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: In one embodiment a mobile device includes a long-range transceiver, a medium-range transceiver, and a controller. The controller is configured to receive, from the long-range transceiver, a target ID associated with a remote medium-range transceiver of a remote system, transmit, via the long-range transceiver, an ID, channel, and band of the medium-range transceiver of the remote system, receive packets from the remote medium-range transceiver on the channel, extract received signal strength indicator (RSSI) data from the packets, filter the RSSI data to obtain a maximum RSSI signal within a window of time, in response to the maximum RSSI signal exceeding a threshold, output a signal indictive of the remote system being less than a predetermined distance away.

Abstract: In one embodiment, a system for a vehicle includes a radio frequency (RF) transceiver having an identification (ID), and a processor coupled with the RF transceiver. The processor is configured to receive a request, via a first wireless connection, for the vehicle to travel to a location, in response to the vehicle being less than a predetermined distance from the location, receive RF packets, via a second wireless connection, from a target at the location, identify packets based on the ID of the RF transceiver, extract received signal strength indicator (RSSI) data from received signals associated with the identified packets, filter the RSSI data to obtain a maximum RSSI signal within a window of time, and in response to the maximum RSSI signal exceeding a threshold, output a signal indictive of the target being less than a predetermined distance from the vehicle.

Abstract: A system and method is disclosed for determining a particular vehicle state based on a UWB signal received at a plurality of receiving nodes. A plurality of channel-impulse responses (CIRs) may be computed from the UWB signal received from the plurality of receiving nodes. A plurality of peak-based features based on a selected position and amplitude may be extracted from the plurality of CIRs. A plurality of correlation-based features may be generated by correlating the plurality of CIRs to a corpus of reference CIRs relating to a plurality of vehicle states. A plurality of maximum likelihood vehicle matrices may be generated by correlating the plurality of CIRs to the corpus of reference CIRs relating to the plurality of vehicle states. The vehicle state may then be determined by processing the plurality of peak-based features and correlation-based features using the machine learning classification algorithm.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and/or a radar source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: A system and method is disclosed for determining a particular vehicle state based on a UWB signal received at a plurality of receiving nodes. A plurality of channel-impulse responses (CIRs) may be computed from the UWB signal received from the plurality of receiving nodes. A plurality of peak-based features based on a selected position and amplitude may be extracted from the plurality of CIRs. A plurality of correlation-based features may be generated by correlating the plurality of CIRs to a corpus of reference CIRs relating to a plurality of vehicle states. A plurality of maximum likelihood vehicle matrices may be generated by correlating the plurality of CIRs to the corpus of reference CIRs relating to the plurality of vehicle states. The vehicle state may then be determined by processing the plurality of peak-based features and correlation-based features using the machine learning classification algorithm.

Abstract: A method relates to managing communications among a set of system nodes. The set of system nodes is configured to sense a predetermined region. The method includes establishing, via a processor, a schedule that includes a communication timeslot and a sensing timeslot, which are non-overlapping. A first system node or a second system node is operable to transmit a first message wirelessly during the communication timeslot. The second system node is operable to transmit a radar transmission signal during the sensing timeslot. The second system node is operable to receive a radar reflection signal during the sensing timeslot. The radar reflection signal is based on the radar transmission signal. The first system node or the second system node is operable to transmit a second message wirelessly during the sensing timeslot. The method includes determining channel state data of the second message via a subset of the set of system nodes during the sensing timeslot.

Abstract: A system and method is disclosed where an operating state may be determined by selecting one or more transmitting nodes for transmitting one or more radio-frequency (RF) signals. One or more receiving nodes may receive the one or more RF signals that may include one or more channel state data. The operating state may be determined based on one or more features extracted from the one or more channel state data. Another system and method is disclosed where a range compensation value may be determined by transmitting a radio-frequency (RF) signal from at least one transmitting node. The one or more receiving nodes may receive the RF signal and a channel state data may be estimated using the signal. The range compensation value may be determined using the channel state data and a position value indicating a location of the at least one transmitting node.

Abstract: Occupancy sensing using ultra-wideband (UWB) keyless infrastructure is provided. Channel impulse response (CIR) measurements are received from a plurality of UWB transceiver nodes arranged about a plurality of locations. A classification model it utilized to predict occupancy of each of the plurality of locations based on CIR tensors formed from the CIR measurements for each of the UWB transceiver nodes.

Abstract: Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and/or a radar source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Abstract: Occupancy sensing using ultra-wideband (UWB) keyless infrastructure is provided. Channel impulse response (CIR) measurements are received from a plurality of UWB transceiver nodes arranged about a plurality of locations. A classification model it utilized to predict occupancy of each of the plurality of locations based on CIR tensors formed from the CIR measurements for each of the UWB transceiver nodes.

Abstract: Occupancy sensing using ultra-wideband (UWB) keyless infrastructure is provided. Channel impulse response (CIR) measurements are received from a plurality of UWB transceiver nodes arranged about a plurality of locations. A classification model it utilized to predict occupancy of each of the plurality of locations based on CIR tensors formed from the CIR measurements for each of the UWB transceiver nodes.

Abstract: In some examples, a system can include a processing resource and a memory resource. The memory resource can store machine readable instructions to cause the processing resource to: (1) collect feedback from a physical (PHY)-layer of a client at a plurality of access points, before the client is associated with any one of the plurality of access points and (2) select an access point using a client channel correlation value calculated from the PHY-layer feedback, wherein the selected access point serves the client and a client group concurrently using precoding.

Abstract: A system and method is disclosed where an operating state may be determined by selecting one or more transmitting nodes for transmitting one or more radio-frequency (RF) signals. One or more receiving nodes may receive the one or more RF signals that may include one or more channel state data. The operating state may be determined based on one or more features extracted from the one or more channel state data. Another system and method is disclosed where a range compensation value may be determined by transmitting a radio-frequency (RF) signal from at least one transmitting node. The one or more receiving nodes may receive the RF signal and a channel state data may be estimated using the signal. The range compensation value may be determined using the channel state data and a position value indicating a location of the at least one transmitting node.

Abstract: A system and method is disclosed for determining a particular vehicle state based on a UWB signal received at a plurality of receiving nodes. A plurality of channel-impulse responses (CIRs) may be computed from the UWB signal received from the plurality of receiving nodes. A plurality of peak-based features based on a selected position and amplitude may be extracted from the plurality of CIRs. A plurality of correlation-based features may be generated by correlating the plurality of CIRs to a corpus of reference CIRs relating to a plurality of vehicle states. A plurality of maximum likelihood vehicle matrices may be generated by correlating the plurality of CIRs to the corpus of reference CIRs relating to the plurality of vehicle states. The vehicle state may then be determined by processing the plurality of peak-based features and correlation-based features using the machine learning classification algorithm.

Abstract: In some examples, a system can include a processing resource and a memory resource. The memory resource can store machine readable instructions to cause the processing resource to: (1) collect feedback from a physical (PHY)-layer of a client at a plurality of access points, before the client is associated with any one of the plurality of access points and (2) select an access point using a client channel correlation value calculated from the PHY-layer feedback, wherein the selected access point serves the client and a client group concurrently using precoding.

Abstract: The disclosed embodiments relate to the design of a system that identifies a person. During operation, the system receives channel state information (CSI) for a set of orthogonal frequency division modulation (OFDM) subcarriers while the person moves in a region that includes two or more nodes that use the set of OFDM subcarriers to communicate with one another. Next, the system analyzes the CSI to obtain an analysis result. The system then determines the identity of the person based on the analysis result.

Abstract: In some examples, a method includes estimating a signal-to-interference-plus noise ratio (SINR) value between a wireless client and each Access Point (AP) of a plurality of APs based on Channel State Information (CSI) between the wireless client and each AP of the plurality of APs and selecting an AP of the plurality of APs to be associated with the wireless client based on a channel bandwidth of the wireless client and the estimated SINR value between the wireless client and each AP of the plurality of APs.

Abstract: The disclosed embodiments relate to the design of a system that identifies a person. During operation, the system receives channel state information (CSI) for a set of orthogonal frequency division modulation (OFDM) subcarriers while the person moves in a region that includes two or more nodes that use the set of OFDM subcarriers to communicate with one another. Next, the system analyzes the CSI to obtain an analysis result. The system then determines the identity of the person based on the analysis result.