This surveillance system is provided with: a radar device which generates information indicating the reflection positions of radiated radio waves; and a surveillance device which, on the basis of the information indicating the reflection positions, detects a moving body in a radiation range of the radio waves, and determines whether an occlusion region, which is a region in the radiation range that cannot be reached by the radio waves, has arisen, and which generates surveillance information including information indicating the result of the moving body detection, and information indicating whether an occlusion region has arisen.

A self-location estimation device includes a landmark detection unit that detects a landmark from camera information, an association unit that associates the landmark detected by the landmark detection unit with a radar information group, a landmark sorting unit that performs sorting of the landmarks detected by the landmark detection unit based on the radar information groups associated with the landmarks by the association unit, and a positional relation calculation unit that calculates a positional relation between an own vehicle and the landmark employed by the landmark sorting unit, based on the radar information group associated with the landmark.

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.

It is described a radar system (100), comprising: i) a transmitter (110) having a transmitter carrier characteristic, configured to transmit a code signal (S); ii) a receiver (120) having a receiver carrier characteristic, configured to receive an echo (E) of the code signal (S); and iii) a control unit (130) configured to: a) identify a carrier characteristic tracking path (T) between the transmitter (110) and the receiver (120), b) estimate an offset between the transmitter carrier characteristic and the receiver carrier characteristic based on the identified tracking path (T), and c) compensate for the offset, in particular establish coherency, based on the estimation. iv) The tracking path (T) comprises hereby a communication path that is at least partially independent of the code signal (S) and the echo (E) of the code signal (S).

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.

Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.

A system for providing integrated detection and deterrence against an unmanned vehicle including but not limited to aerial technology unmanned systems using a detection element, a tracking element, an identification element and an interdiction or deterrent element. Elements contain sensors that observe real time quantifiable data regarding the object of interest to create an assessment of risk or threat to a protected area of interest. This assessment may be based e.g., on data mining of internal and external data sources. The deterrent element selects from a variable menu of possible deterrent actions. Though designed for autonomous action, a Human in the Loop may override the automated system solutions.

A system including a radar receiver, a computer readable medium, and a processor. A data structure containing historical information pertaining to weather conditions for multiple locations may reside in the medium. The processor may be configured to: obtain aircraft data including information of an aircraft position; obtain external data; obtain a portion of the historical information pertaining to a location corresponding to the aircraft position; obtain weather radar data; analyze the weather radar data to determine if windshear exceeds a windshear alert threshold; upon an occurrence of the windshear exceeding the windshear alert threshold, determine whether to issue or suppress a windshear alert based on the aircraft data, the external data, and/or the portion of the historical information; and one of a) output the windshear alert for presentation to a user or b) adjust the windshear alert threshold causing the windshear alert to be suppressed and/or suppress the windshear alert.

A method for creating a virtual reception channel in a radar system includes an antenna possessing two physical reception channels (1.sub.r, 2.sub.r) spaced apart by a distance d in a direction x, two emission channels (1.sub.e, 2.sub.e) spaced apart by the same distance d in the same direction x and processing means, the method comprising: dynamically selecting two different waveforms, the waveforms being orthogonal to each other; generating a radar pulse of given central wavelength in each emission channel, each of the emission channels emitting one of the two different waveforms; acquiring with the reception channels echoes due to pulses emitted by the emission channels and reflected by at least one target; compressing the pulses by matched filtering of the echoes acquired by each physical reception channel, this involving correlating them with each of the waveforms generated in the emission channel; and repeating steps a) to c) while randomly changing one of the values of each of the phase codes associated with the generated waveforms until the level of the sidelobes of all the compressed pulses has stabilized; and radar system for implementing such a method.

A radar device that detects one or more information elements, groups the one or more information elements into one or more first groups in each frame, the one or more first groups including information on one or more first objects of which Doppler speeds fall within a determined range, groups the one or more information elements into one or more second groups in each frame, the one or more second groups including information on one or more second objects of which Doppler speeds fall outside the determined range calculates first positions in m-th frame, of positions of groups to be followed of the first groups and the second groups in n-th frame and extracts the groups to be followed in the m-th frame from the first groups and the second groups in the m-th frame using the first positions.