A method for exposure level estimation, includes transmitting radar signals for object detection and communication signals for wireless communication operations. The method also includes identifying a location of an object relative to the electronic device within a first time duration based on the radar signals, the first time duration including a previous time until a current time. The method further includes determining a radio frequency (RF) exposure measurement associated with the object based on the location of the object over the first time duration. Additionally, the method includes determining a power density budget over a second time duration based on a comparison of the RF exposure measurement to an RF exposure threshold, the second time duration including the current time until a future time. The method also includes modifying the wireless communication operations for the second time duration based on the power density budget.

Disclosed herein are systems and methods for estimating target ranges, angles of arrival, and speed using optimization procedures. Target ranges are estimated by performing an optimization procedure to obtain a denoised signal, performing a correlation of a transmitted waveform and the denoised signal, and using a result of the correlation to determine an estimate of a distance between the sensor and at least one target. Target angles of arrival are estimated by determining ranges at which targets are located, and, for each range, constructing an array signal from samples of received echo signals, and using the array signal, performing another optimization procedure to estimate a respective angle of arrival for each target of the at least one target. Doppler shifts may also be estimated using another optimization procedure. Certain of the optimization procedures use atomic norm techniques.

The concepts, systems and methods described herein are directed towards frequency jump burst-pulse-Doppler (FJB-PD) waveforms and processing to provide wideband, high range resolution (HRR) radar profiling capability in a clutter dense environment. The method includes transmitting a FJB-PD waveform comprising a plurality of frequency steps over a predetermined time period with each frequency step having a plurality of pulses. The method further includes receiving one or more FJB-PD pulse returns corresponding to the FJB-PD waveform and identifying one or more target detections in the one or more FJB-PD pulse returns. A set of range swaths may be extracted for each of the one or more target detections and a wideband spectrum may be generated for each of the sets of range swaths using FJB coherent integration. A clutter suppressed HRR profile may be generated for each of the target detections based on the respective wideband spectrum.

A multiple input multiple output (MIMO) radar system for detecting a moving object is based on an explicit signal model. The explicit signal model accounts for waveform separation residuals by relating measurements of the virtual array to an auto-term including a Kronecker product of object-receiver signatures and transmitter-object signatures; and a cross-term including a Kronecker product of object-receiver signatures and transmitter-object residual signatures. The radar system uses a spatial MIMO object detector that is based on the explicit signal model to detect the moving object.

An electronic device for gesture recognition, includes a processor operably connected to a transceiver. The transceiver is configured to transmit and receive signals for measuring range and speed. The processor is configured to transmit the signals, via the transceiver. in response to a determination that a triggering event occurred, the processor is configured to track movement of an object relative to the electronic device within a region of interest based on reflections of the signals received by the transceiver to identify range measurements and speed measurements associated with the object. The processor is also configured to identify features from the reflected signals, based on at least one of the range measurements and the speed measurements. The processor is further configured to identify a gesture based in part on the features from the reflected signals. Additionally, the processor is configured to perform an action indicated by the gesture.

Disclosed are systems and techniques for wireless communications. For example, a process can include determining a subset of radio frequency (RF) sensing devices from a plurality of available RF sensing devices for performing an RF sensing technique for a target object. The subset of RF sensing devices may be determined based on a plurality of factors associated with the plurality of available RF sensing devices. The process can include transmitting, to at least one RF sensing device of the subset of RF sensing devices, at least one message instructing the subset of RF sensing devices to perform the RF sensing technique for the target object to obtain one or more characteristics of the target object.

A method for real-time THz sensing using true time delay (TTD) is implemented by a base station and includes transmitting, by a transceiver that includes TDD elements and phase shifters configured in the transceiver, simultaneous frequency dependent (SFD) beams to scan an environment at a first granularity to detect a spatial cluster target. Each of the SFD beams corresponds to a different phase angle and different frequency. The method includes determining, among the SFD beams, a subset of beams that detected the spatial cluster target. The method includes beam switching, by the transceiver, using time division multiplexing (TDM) and a TDM bandwidth to scan a portion of the environment at phase angles corresponding to the subset of beams and at a second granularity finer than the first granularity. The method includes combining data received from the SFD beams, by multiple threads that concurrently process data received from the SFD beams.

Example embodiments relate to radar reflection filtering using a vehicle sensor system. A computing device may detect a first object in radar data from a radar unit coupled to a vehicle and, responsive to determining that information corresponding to the first object is unavailable from other vehicle sensors, use the radar data to determine a position and a velocity for the first object relative to the radar unit. The computing device may also detect a second object aligned with a vector extending between the radar unit and the first object. Based on a geometric relationship between the vehicle, the first object, and the second object, the computing device may determine that the first object is a self-reflection of the vehicle caused at least in part by the second object and control the vehicle based on this determination.

Aspects described herein provide sensor data stream processing for enabling camera/radar sensor fusion, with application to road user detection in the context of Autonomous Driving/Assisted Driving (ADAS). In particular, a scheme to extract Region-of-Interests (ROI) from a high-resolution, high-dimensional radar data cube that can then be transmitted to a sensor fusion unit is described. The ROI scheme allows to extract relevant information, thus reducing the latency and data transmission rate to the sensor fusion module, without trading-off accuracy and detection rates. The sensor data stream processing comprises receiving a first data stream from a radar sensor, forming a point cloud by extracting 3D points from the 3D data cube, performing clustering on the point cloud in order to identify high-density regions representing one or ROIs, and extracting one or more 3D bounding boxes from the 3D data cube corresponding to the one or more ROIs and classifying each ROI.

Techniques are disclosed for systems and methods to provide remote sensing imagery for mobile structures. A remote sensing imagery system includes a radar assembly mounted to a mobile structure and a coupled logic device. The radar assembly includes an imaging system coupled to or within the radar assembly and configured to provide image data associated with the radar assembly. The logic device is configured to receive radar returns corresponding to a detected target from the radar assembly and image data corresponding to the radar returns from the imaging system, and then generate radar image data based on the radar returns and the image data. Subsequent user input and/or the sensor data may be used to adjust a steering actuator, a propulsion system thrust, and/or other operational systems of the mobile structure.