An axial displacement angle estimation device repeatedly calculates an axial displacement angle based on the detection result of the radar apparatus. The axial displacement angle estimation device extracts the axial displacement angle included in a predetermined extraction angle range among a plurality of axial displacement angles, and calculate an average value and a median value of the extracted plurality of axial displacement angles to be an axial displacement angle average value and an axial displacement median value. The axial displacement angle estimation device determines, based on the axial displacement angle average value and the axial displacement angle median value, whether a predetermined allowable condition is met. The axial displacement angle estimation device utilizes, when determined that the predetermined allowable condition is met, the axial displacement angle average value as an estimation result of the axial displacement angle.

Embodiments described herein are directed to tracking objects using a cognitive heterogeneous ad hoc mesh network. A first participant receives a notification signal from a second participant. The first participant determines first positioning information from the notification signal and second positioning information from characteristics of the received signal. If the difference between the first and second positioning information is below a first threshold, then the second participant is within line-of-sight of the first participant. If the difference is above the first threshold and below a second threshold, then the second participant may have a malfunctioning sensor. But if the difference is above the second threshold, then the second participant is not within line-of-sight of the first participant and the received signal was reflected off another object. The positioning information can then be refined or transmitted to other participants.

Methods and devices for generating data for controlling a Self-Driving Car (SDC) are disclosed. The method includes: i) receiving a section of a road map corresponding to surroundings of the SDC and at least one object, ii) generating a plurality of predicted trajectories including a potential future location points of the at least one object, iii) mapping the potential future location points on the section of the road map, iv) computing a score for each of the potential future location points, the score representing an association of a given potential future location point with the plurality of road lanes at a future instance of time, v) computing an aggregated score from the scores corresponding to potential future location points, and vi) based on the aggregated score, determining a predicted location of the at least one object at the future instance of time.

A method for monitoring a data fusion function of an infrastructure system for the infrastructure-supported assistance of motor vehicles during an at least semi-automated driving task within an infrastructure, the infrastructure including multiple infrastructure surroundings sensors for detecting an area of the infrastructure. The method includes: receiving multiple input data sets intended for the data fusion function, each of which includes surroundings data based on the respective detection of the area, which represent the detected area; receiving output data based on a data fusion of the input data sets, output by the data fusion function; checking the input data sets and/or the output data for consistency; outputting a check result of the check. A device, a computer program, and a machine-readable memory medium are also provided.

A ground-based radar system for weather sensing and aircraft tracking includes a ground-based radar that is configured to scan a volume of space associated with a particular aircraft for detecting a weather event in the volume of space, and an electronic control system that is configured to control the ground-based radar. The control system is adapted to track the particular aircraft via tracking data associated with the particular aircraft, and is adapted to detect the weather event via weather data associated with signals from the ground-based radar. The control system is configured to control the ground-based radar to adjust the scan of the volume of space in response to at least the tracking data associated with the particular aircraft being tracked. A geographically diverse radar network that includes multiple ground-based radar systems that communicate with each other also is provided.

A maritime radar system is provided, comprising a transmitter, a receiver, and one or more processors arranged to provide range and azimuth discrimination of a detection area by performing a delay/Doppler analysis of the echo of a single beam transmitted by the transmitter and received by the receiver.

Systems and methods for operating radar systems. The methods comprise, by a processor: receiving point cloud information generated by at least one radar device and a spatial description for an object; generating a plurality of point cloud segments by grouping data points of the point cloud information based on the spatial description; arranging the point cloud segments in a temporal order to define a radar tentative track; performing dealiasing operations using the radar tentative track to generate tracker initialization information; and using the tracker initialization information to generate a track for the object.

Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

This application relates to a passive coherent location (PCL) system. In one aspect, the PCL system includes a signal measurement device configured to receive a plurality of signals from a plurality of illuminators and generate an In-phase signal and a Quadrature signal corresponding to each illuminator using the received signals. The PCL system also includes a signal processing device configured to detect a first target using the In-phase and Quadrature signals and measure a bistatic range of the first target and a bistatic velocity of the first target to generate a plurality of pieces of line track information corresponding to the first target. The PCL system further includes a locating device configured to generate target track information of the first target using the line track information and predict a position vector and a velocity vector of the first target using the target track information.

Examples disclosed herein relate to an autonomous driving system in a vehicle, including a radar system with a reinforcement learning engine to control a beam steering antenna and identity targets in a path and a surrounding environment of the vehicle, and a sensor fusion module to receive information from the radar system on the identified targets and compare the information received from the radar system to information received from at least one sensor in the vehicle.