This disclosure describes techniques for using radar cross-section (RCS) data to classify objects detected by autonomous vehicles within driving environments. In some examples, the variance of the RCS data associated with an object may be evaluated to determine signal interference caused by multipath fading. The variance of the RCS data may be used to classify the object and to determine whether the autonomous vehicle can safely drive over the object. For instance, objects such as manhole covers, storm drains, and expansion joints may provide a significant radar signal, but low RCS variance indicating that they can be driven over by the vehicle. Based on the classification of the object, the autonomous vehicle may determine a trajectory around the object or directly over the object.

A method includes receiving a first wireless signal detected by a first device in an environment, the first wireless signal including a first distortion pattern caused by an object moving in the environment, receiving a second wireless signal detected by a second device in the environment, the second wireless signal including a second distortion pattern caused by the object moving in the environment, determining, by comparing the first distortion pattern to the second distortion pattern, that the first distortion pattern and the second distortion pattern correspond to a same movement event associated with the object moving in the environment, determining a timing offset between the first device and the second device based on information associated with the first distortion pattern and the second distortion pattern, and determining, based on the timing offset, temporal correspondences between data generated by the first device and data generated by the second device.

Techniques are provided for managing transmit and receive beams in a millimeter wave (mmW) communication system for use in bistatic radio frequency (RF) sensing. An example method of tracking targets with bistatic radio frequency sensing includes receiving one or more sensing reference signals, generating a signal report based at least in part on the one or more sensing reference signals, transmitting the signal report, receiving tracking signal configuration information, receiving one or more tracking reference signals identified in the tracking signal configuration information, and tracking one or more targets associated with the one or more tracking reference signals.

Methods, apparatus and systems for sound sensing based on radio signals are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless channel of a venue; a receiver configured to receive a second wireless signal through the wireless channel, wherein the second wireless signal comprises a reflection of the first wireless signal by at least one object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (CI) of the wireless channel based on the second wireless signal, determining a presence of a vibrating object in the venue based on the time series of CI (TSCI), extracting a sound signal from the TSCI, and reconstructing at least one speech based on the sound signal.

This document describes techniques and devices for non-line-of-sight radar-based gesture recognition. Through use of the techniques and devices described herein, users may control their devices through in-the-air gestures, even when those gestures are not within line-of-sight of their device's sensors. Thus, the techniques enable users to control their devices in many situations in which control is desired but conventional techniques do permit effective control, such as to turn the temperature down in a room when the user is obscured from a thermostat's gesture sensor, turn up the volume on a media player when the user is in a different room than the media player, or pause a television program when the user's gesture is obscured by a chair, couch, or other obstruction.

Disclosed are techniques for wireless sensing. In an aspect, a user equipment (UE) measures at least a line-of-sight (LOS) path and a non-line-of-sight (NLOS) path of a first downlink positioning reference signal (DL-PRS) from a first transmission-reception point (TRP), measures at least an LOS path and an NLOS path of a second DL-PRS from a second TRP, measures at least an LOS path and an NLOS path of a third DL-PRS from a third TRP, and enables a location of a non-participating target object to be determined based, at least in part, on reference signal time difference (RSTD) measurements between a time of arrival (ToA) of the LOS path of the first DL-PRS and the ToAs of the NLOS paths of the first, second, and third DL-PRS. In an aspect, the non-participating target object does not participate in determining its own location.

A radar device. The radar device includes a transceiver unit that includes at least three transmitting antennas and at least three receiving antennas, the transceiver unit being designed to emit radar radiation with the aid of the transmitting antennas, to receive radar radiation with the aid of the receiving antennas, and to generate radar data based on the received radar radiation. The radar device further includes an evaluation unit, which is configured to estimate, by evaluating the radar data, at least one angle of at least one target using a 2-target angle estimation model, the 2-target angle estimation model taking the propagation of the radar radiation along four paths into account.

A method and system for detection of a target by a passive radar system exploiting multichannel-per-carrier cellular illuminator sources, the method including: receiving a reference signal from a reference source, said reference signal being received at a reference element of a radar receiver of the passive radar system; receiving, at a surveillance element of said radar receiver, a reflected signal originating from said reference source and reflected off the target, said reflected signal including interference; deciphering components of said signals; and reconstructing said signals, from said components, excluding said interference.

This document describes techniques and systems to enable a radar system to detect angles in bistatic and monostatic scenarios. In some examples, an automotive radar system includes one or more processors. The processors can obtain electromagnetic (EM) energy reflected by objects and generate, based on the reflected EM energy, a two-dimensional (2D) data matrix. The 2D data matrix has a number of rows corresponding to the number of antenna elements in a transmitter array and a number of columns corresponding to the number of antenna elements in a receiver array. Using the 2D data matrix, the processors can determine DoA estimates and DoD estimates in monostatic and bistatic scenarios. By comparing the DoA estimates to the DoD estimates, the processors can determine an angle associated with the objects. In this way, the described techniques and systems can enable angle detection in monostatic and bistatic conditions with improved angular resolution and reduced cost.

Methods, apparatus and systems for sound sensing based on radio signals are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless channel of a venue; a receiver configured to receive a second wireless signal through the wireless channel, wherein the second wireless signal comprises a reflection of the first wireless signal by at least one object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (CI) of the wireless channel based on the second wireless signal, determining a presence of a vibrating object in the venue based on the time series of CI (TSCI), extracting a sound signal from the TSCI, and reconstructing at least one speech based on the sound signal.