A method and a device for separating echo signals of STWE SAR in elevation are provided. The method includes that: aliasing echo signals of multiple sub-swaths are received; for a target sub-swath of the multiple sub-swaths, multiple sub-beams associated with the target sub-swath are generated, the multiple sub-beams pointing to different directions of the target sub-swath respectively, and a null of each of the multiple sub-beams being used for deep nulling suppression on echo signals of sub-swaths except the target sub-swath; and the aliasing echo signals are processed based on the multiple sub-beams and multiple nulls corresponding to the multiple sub-beams to generate a target echo signal of the target sub-swath.

This document describes “Multi-domain Neighborhood Embedding and Weighting” (MNEW) for use in processing point cloud data, including sparsely populated data obtained from a lidar, a camera, a radar, or combination thereof. MNEW is a process based on a dilation architecture that captures pointwise and global features of the point cloud data involving multi-scale local semantics adopted from a hierarchical encoder-decoder structure. Neighborhood information is embedded in both static geometric and dynamic feature domains. A geometric distance, feature similarity, and local sparsity can be computed and transformed into adaptive weighting factors that are reapplied to the point cloud data. This enables an automotive system to obtain outstanding performance with sparse and dense point cloud data. Processing point cloud data via the MNEW techniques promotes greater adoption of sensor-based autonomous driving and perception-based systems.

A method for first path acceptance for secure ranging includes determining a Channel Impulse Response (CIR) of a communication channel for a plurality of channel taps. Each channel tap corresponds to a respective one of a plurality of time slots of the CIR, wherein the CIR includes a plurality of estimated CIR values. A statistical characteristic is extracted from the estimated CIR values within a temporal range of the channel taps. The statistical characteristic is compared to a reference value to detect a distance decreasing attack.

The present application provides techniques for reducing noise in sensor-based systems, such as radar systems. In particular, techniques referred to background supplemental cancellation (BaSC) and background supplemental loading (BaSL) are disclosed and facilitate improved detection of moving targets in certain types of radar systems, such as radar systems based on Reiterative minimum-mean square error (RMMSE) estimation formulations. The BaSC technique may utilize a hard cancellation, where clutter cancellation is performed prior to estimation, while the BaSL technique may utilize a “soft” cancellation technique whereby clutter cancellation is performed jointly with estimation. The clutter cancellation provided via the BaSC and BaSL techniques improves the accuracy of the radar system with respect to performing target detection.

Systems and methods for detection and reporting of small targets to an operational area. Exemplary embodiments are presented to detect targets such as avian species, UAS, UAV, and drones, and transmit unique small target identifier information via data link, such as ADS-B, to an operational area.

A method for classifying tracks in radar detections of a scene acquired by a stationary radar unit, comprises: acquiring radar detections of the scene using the static radar unit; feeding at least a portion of the radar detections into a tracker module for producing track-specific feature data indicating a specific track in the scene, feeding at least a portion of the radar detections into a scene model comprising information about scene-specific features aggregated over time, and information indicating areas in the scene with expected ghost target detections and areas with expected real target detections, wherein at least a subset of the scene-specific features is determined from the radar detections; classifying the specific track as belonging to a real target or to a ghost target by relating the specific track to a position in the scene model.

A method for contactless vital sign monitoring includes transmitting, via a transceiver, radar signals for object detection. The method also includes generating a clutter removed channel impulse response from received reflections of the radar signals a portion of which are reflected off of a living object. The method further includes identifying a set of range bins corresponding to a position of the living object. Additionally, the method includes identifying a first set of signal components representing a respiration rate of the living object and a second set of signal components representing a heart rate of the living object.

Processing of a range-Doppler matrix of a radar system is described. For easy, efficient and rapid ascertainment of a detection threshold of the range-Doppler matrix, only a partial quantity of the cells of the range-Doppler matrix is selected, and the detection threshold is ascertained on the basis of the selected partial quantity of cells of the range-Doppler matrix.

A method and electronic device for radar-based face authentication anti-spoofing for determining access to the electronic device. The electronic device includes a radar transceiver and at least one processor. The at least one processor is configured to transmit, via the transceiver, a first set of signals, generate a channel impulse response (CIR) based on receipt of reflections of the first set of signals, detect a first CIR tap in the CIR, determine a selection of CIR data based on the detected first CIR tap, determine a profile matching metric based on comparison of the selection of CIR data to a set of predetermined reference signals, and determine whether to allow access to the electronic device based on comparison of the profile matching metric to a profile matching threshold.

An airborne radar system and signal interpretation approach that detects slow moving ground targets using angle and Doppler of Keystone formatting process, and is referred to as Angle-Doppler Keystone Formatting (ADK). ADK collapses the clutter ridge to a constant Doppler or to a constant angle, thereby transforming a clutter ridge in angle-Doppler space into a horizontal line of constant Doppler or a vertical line of constant angle. Clutter may then be filtered more effectively, such as by using multiple beams as the source of STAP training data or by using multiple Doppler bins.