An apparatus comprising an antenna array comprising a plurality of antennas to receive a plurality of radar signals reflected by a plurality of objects responsive to a transmitted radar signal; a doppler measurement module to determine, for a first reflected radar signal of the plurality of reflected radar signals, a first doppler measurement indicating a velocity component based on a comparison of the first reflected radar signal to the transmitted radar signal; a phase offset measurement module to determine a first phase offset of the first reflected radar signal received at a first antenna of the plurality of antennas relative to a phase of the first reflected radar signal received at a reference antenna of the plurality of antennas; and a phase offset calibration module to determine, for the first antenna, a first phase offset calibration error based on the first doppler measurement and the first phase offset.

A vehicular test system for testing a vehicular sensing system includes a sensor support structure having a proximal end disposed at a vehicle, a distal end extending away from the vehicle, and a force providing element that provides a force to move the distal end of the sensor support structure. A vehicular sensor is disposed at the distal end of the sensor support structure. When the vehicular sensor is approaching a collision with an object, such as during testing of vehicular sensors and vehicular sensing systems, a control controls the force providing element to move the distal end of the sensor support structure and the vehicular sensor to avoid the collision.

Examples of imaging systems are described herein which may implement microwave or millimeter wave imaging systems. Examples described may implement partitioned inverse techniques which may construct and invert a measurement matrix to be used to provide multiple estimates of reflectivity values associated with a scene. The processing may be partitioned in accordance with a relative position of the antenna system and/or a particular beamwidth of an antenna. Examples described herein may perform an enhanced resolution mode of imaging which may steer beams at multiple angles for each measurement position.

The subject disclosure relates to techniques for calibrating radar sensors and in particular, for facilitating intrinsic radar calibrations, e.g., in autonomous vehicle deployments. In some aspects, a radar calibration of the disclosed technology can include steps for receiving a radar signal comprising one or more known signal parameters, performing power compensation calculations based on the received radar signal, and determining if there is a calibration discrepancy in the radar sensor based on the power compensation calculations. In some aspects, the process can further include steps for applying a calibration offset to the radar sensor if it is determined that there is a calibration discrepancy in the radar sensor. Systems and computer-readable media are also provided.

A system for monitoring a traffic situation includes at least one radar sensor unit for recording environmental data in a monitoring region of the radar sensor unit. A central processor evaluates the environmental data from the radar sensor unit and determines the traffic situation within the monitoring region based on the environmental data. A reference signal unit for outputting a reference signal to the radar sensor unit is configured to modulate a radar signal emitted by the radar sensor unit and to reflect a modulated radar signal back to the radar sensor unit as a reference signal. The processor identifies the modulated radar signal as a reference signal from the reference signal unit and recognizes a malfunction of the radar sensor unit if a reference signal is not received by the radar sensor unit.

The present disclosure provides a circuitry for estimating a mounting angle of a radar sensor with respect to a mobile platform coordinate system. The circuitry is configured to estimate a first velocity of a first radar sensor, based on first radar detection data obtained from the first radar sensor, wherein the first radar detection data is indicative of at least two targets; estimate a second velocity of a second radar sensor, based on second radar detection data obtained from the second radar sensor, wherein the second radar detection data is indicative of at least two targets, and estimate the mounting angle of the first radar sensor, based on the estimated first velocity, the estimated second velocity, a predefined first mounting position of the first radar sensor with respect to the mobile platform coordinate system and a predefined second mounting position of the second radar sensor with respect to the mobile platform coordinate system.

The embodiments relate to a radar control device and method. Specifically, a radar control device according to the embodiments may include a transceiver configured to transmit a transmission signal to the surroundings of a host vehicle and receive a reception signal received by reflecting the transmission signal on an object, a determiner configured to generate a first range-Doppler map by performing fast Fourier transform (FFT) on the reception signal and generate a second range-Doppler map based on a comparison group including a plurality of preset temporary lateral distances, and determine a correlation coefficient between the first range-Doppler map and the second range-Doppler map, and an estimator configured to estimate a lateral distance between the host vehicle and the object based on the correlation coefficient.

There is provided an information processing apparatus, an information processing method, and a program, with which an abnormality of a sensor mounted on a vehicle can be detected. The information processing apparatus includes a first data acquisition unit, a second data acquisition unit, a comparison unit, and a detection unit. The first data acquisition unit acquires first data including at least one of position information of an object present in a vehicle surrounding environment or road surface tilt information and a time stamp, the position information and the road surface tilt information being generated by using sensing data of a sensor mounted on a vehicle. The second data acquisition unit acquires second data including at least one of position information of an object present in a vehicle surrounding environment or road surface tilt information and a time stamp, the position information and the road surface tilt information being generated by using sensing data of a sensor other than that of the vehicle. The comparison unit compares the first data with the second data on the basis of the time stamp included in each of the first data and the second data. The detection unit detects an abnormality related to a sensor mounted on the vehicle on the basis of a comparison result by the comparison unit.

The present disclosure provides a system for processing radar data. The system may comprise a frequency generator configured to generate a reference frequency signal; a timing module configured to generate a shared clock signal or a plurality of timing signals; and a plurality of radar modules in communication with the frequency generator and timing module. The radar modules may be configured to: (i) receive the reference frequency signal and at least one of a shared clock signal and a timing signal, (ii) transmit a first set of radar signals based in part on the reference frequency signal and at least one of the shared clock signal and the timing signal, and (iii) receive a second set of radar signals reflected from a surrounding environment. The system may comprise a processor configured to process radar signals received by each radar module, by coherently combining radar signals using phase and timestamp information.

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.