A wireless ranging system includes a ranging terminal that transmits a wireless signal including a ranging signal and a communication signal indicating an order of ranging with respect to a first ranging target terminal and a second ranging target terminal, and a first ranging target terminal and a second ranging target terminal that, when receiving the wireless signal, respectively transmits a first response signals and a second response signals consecutively a plurality of times to the ranging terminal. For each time the ranging terminal receives each of the plurality of response signals, the ranging terminal measures an elapsed time from transmission of the wireless signal, and calculates a relative distance between the ranging terminal and the ranging target terminals from a propagation time of each of the plurality of response signals calculated using the elapsed time.

The techniques and systems herein enable target-velocity estimation using position variance. Specifically, a plurality of detections of a target are received for respective times as the target moves relative to a host vehicle. Based on the detections, two-dimensional positions of the target relative to the host vehicle are determined for the respective times. Based on the positions of the target at the respective times, a first variance is determined for a first dimension of the positions, and a second variance is determined for a second dimension of the positions. Based on the first and second variances, an estimated velocity of the target is calculated. By basing the estimated velocity on the variances of the positions, more-accurate estimated velocities may be generated sooner, thus enabling better performance of downstream operations.

A phase correcting device includes a local oscillator that includes an all digital phase-locked loop configured to output a local oscillation signal, a first phase detector configured to detect a phase of the local oscillation signal to output the phase of the local oscillation signal, a reference phase device configured to generate a quasi-reference phase corresponding to a reference phase of the local oscillation signal to output the quasi-reference phase, based on a reference clock, a second phase detector configured to detect a fluctuation amount of a phase of the local oscillator, based on the phase detected by the first phase detector and the quasi-reference phase, and a correction circuit configured to correct the phase of the inputted signal by using a detection result of the second phase detector.

This document describes techniques and systems related to a radar system using a machine-learned model for stationary object detection. The radar system includes a processor that can receive radar data as time-series frames associated with electromagnetic (EM) energy. The processor uses the radar data to generate a range-time map of the EM energy that is input to a machine-learned model. The machine-learned model can receive as inputs extracted features corresponding to the stationary objects from the range-time map for multiple range bins at each of the time-series frames. In this way, the described radar system and techniques can accurately detect stationary objects of various sizes and extract critical features corresponding to the stationary objects.

Aspects of the present disclosure are directed to radar communications with disparate pulse repetition intervals, as may be implemented with radar transmission, receiver and processing circuitry. As may be utilized in accordance with one or more embodiments herein, time division multiplexing (TDM) multi-input multi-output (MIMO) radar signals are transmitted by transmitting sets of successive radar signals, each set having a pulse repetition interval (PRI) that is different than the PRI of sets of radar signals transmitted in another one of the sets. Positional characteristics of a target may be ascertained based on the PRI used in each of the sets and on phase characteristics of ones of the radar signals reflected from the target.

Provided is a method and system for measuring a radar cross section of an object (102). The method comprises: transmitting one or more radar pulses (402) to the object (102), each of the one or more pulses (402) having a predetermined pulse profile; for each of the one or more pulses (402), measuring a pulse return, the pulse return being the radar pulse (402) reflected by the object (102); deconvolving the measured one or more pulse returns using the predetermined pulse profile; and determining the radar cross section of the object (102) using the deconvolved one or more pulse returns.

A track fusion method for an unmanned surface vehicle includes: (a) obtaining perception information of the unmanned surface vehicle, where the perception information includes GPS data information and radar data information; (b) pre-processing the radar data information to obtain target radar information; (c) constructing a track correlation model; and performing track correlation between the GPS data information and the target radar information based on the track correlation model; and (d) constructing a fusion data weight allocation model; and subjecting between the GPS data information and the target radar information correlated therewith to track fusion based on the fusion data weight allocation model. This application further provides a track fusion device for unmanned surface vehicles.

This document describes techniques, apparatuses, and systems for sensor fusion for object-avoidance detection, including stationary-object height estimation. A sensor fusion system may include a two-stage pipeline. In the first stage, time-series radar data passes through a detection model to produce radar range detections. In the second stage, based on the radar range detections and camera detections, an estimation model detects an over-drivable condition associated with stationary objects in a travel path of a vehicle. By projecting radar range detections onto pixels of an image, a histogram tracker can be used to discern pixel-based dimensions of stationary objects and track them across frames. With depth information, a highly accurate pixel-based width and height estimation can be made, which after applying over-drivability thresholds to these estimations, a vehicle can quickly and safely make over-drivability decisions about objects in a road.

This document describes techniques and systems to detect and localize NLOS objects using multipath radar reflections and map data. In some examples, a processor of radar system can identify a detection of an object using reflected EM energy and determine, using map data, whether a direct-path reflection associated with the detection is within a roadway. In response to determining that the direct-path reflection is not located within the roadway, the processor can determine whether a multipath reflection (e.g., a multipath range and multipath angle) associated with the detection is viable. In response to determining that the multipath reflection is viable, the processor can determine that the detection corresponds to an NLOS object. The processor can also provide the NLOS object as an input to an autonomous or semi-autonomous driving system of the vehicle, thereby improving the safety of such systems.

An imaging method using a doppler radar wherein the pointing direction in transmission (d.sub.ei) is modified from recurrence to recurrence; each detection block of duration T comprises a periodic repetition of a number C of pointing cycles, each of these cycles comprising a number P of recurrences, the set of these P recurrences covering the D.sub.e pointing directions (d.sub.ei) of the set; the order of the pointings is modified in a pseudo-random manner from pointing cycle to pointing cycle during a same detection block so as to create an irregular time interval between two pointings in a same direction; at least one beam is formed in reception on each recurrence in a direction included in the transmission-focused angular domain in the pointing direction corresponding to the recurrence.