A method of inspecting a static monitoring installation including creating a reference image from signal reflections from static objects in a monitoring space determining a reference value from the reflections of the reference image, creating a comparison image from the signal reflections from the static objects in the space, wherein the comparison image is recorded with a time offset after the reference image, determining at least one comparison value from the reflections of the comparison image, and outputting a fault signal when a deviation of the comparison value and the reference value exceeds a threshold value.

A method for detecting misalignment of a radar sensor positioned on a vehicle. A Doppler spectrum for the radiation emitted and received by the radar sensor is ascertained. For at least one frequency bin of the Doppler spectrum, an angle of incidence is determined in at least a subinterval. The determined angle of incidence is compared to the angle of incidence expected for the frequency bin. A misalignment of the radar sensor is detected as a function of the difference of the measured angle of incidence from the expected angle of incidence.

A calibration utilizes reference data indicative of a position of a target element relative to a reference location, of a position of a reference point on a rotatable support relative to the reference location, orientation data indicative of at least one angular position of the rotatable support, and antenna measurement data indicative of electromagnetic echo signals received by a radar antenna from the target element. A measured position of the target element relative to the radar antenna is determined based on at least a portion of the antenna measurement data. A reference position of the target element relative to the radar antenna is determined based on the reference data and on at least a portion of the orientation data. At least one bias value or function associated with the orientation data and/or the antenna measurement data is determined based on a deviation between the determined measured position and reference position.

This document describes techniques for enabling radar system calibration with bistatic sidelobe compensation. Radar signals reflect off of a flat plate that changes orientation (e.g., elevation and/or azimuth angle) and position relative to a mounting position of a specific radar sensor being calibrated. For each radar sensor, measurements may be obtained across a range of translational positions of the flat plate. Highly accurate calibration errors are determined for each radar sensor this way. By calibrating radar systems repositioning the target during the data collection in this way, the prominence of any bistatic sidelobes appearing in measurements may be reduced or prevented, which may enable less-complex and more-accurate calibration of each unique radar system installation. An indication of each calibration error may be output for use in individually adjusting the mounting position of each specific radar sensor within a radar system.

An object recognition determination produced by a perception system from data received from a ranging sensor system can be verified. A certificate can be produced that includes data for points of readings from the ranging sensor system. The points can have been segmented, by the perception system, into point sets that correspond to objects in an environment of a cyber-physical system. The certificate can also include lists of pairs of points in a point set and a velocity of the point set. A test of information in the certificate can be performed. Based on a result of the test: a rectification can be made to the perception system or the ranging sensor system or a communication can be transmitted to a control signal production module configured to produce, in response to the communication, a control signal to be transmitted to an actuator system configured to control the cyber-physical system.

A method for calibrating two receiving units of a radar sensor that includes an array of receiving antennas formed by two sub-arrays and an evaluation unit, which is designed to carry out an angle estimation for located radar targets based on phase differences between the signals received by the receiving antennas, each receiving unit including parallel reception paths for the signals of the receiving antennas of one of the sub-arrays. The method includes: analyzing the received signals and deciding whether a multi-target scenario or a single-target scenario is present, in the case of a single-target scenario, measuring phases of the signals received in the sub-arrays and calculating a phase offset between the two sub-arrays, and calibrating the phases in the two receiving units based on the calculated offset.

Techniques and apparatuses are described that implement a smart-device-based radar system capable of performing angular estimation using machine learning. In particular, a radar system 102 includes an angle-estimation module 504 that employs machine learning to estimate an angular position of one or more objects (e.g., users). By analyzing an irregular shape of the radar system 102's spatial response across a wide field of view, the angle-estimation module 504 can resolve angular ambiguities that may be present based on the angle to the object or based on a design of the radar system 102 to correctly identify the angular position of the object. Using machine-learning techniques, the radar system 102 can achieve a high probability of detection and a low false-alarm rate for a variety of different antenna element spacings and frequencies.

A radar apparatus includes a plurality of transmission antennae and a radar transmitter that transmits transmission signals by using the plurality of transmission antennae. In a virtual reception array including a plurality of virtual antennae formed of a plurality of reception antennae and the plurality of transmission antennae, disposition positions of at least two of the virtual antennae are the same as each other, and, transmission intervals of the transmission signals that are sequentially transmitted from transmission antennae corresponding to the at least two virtual antennae among the plurality of transmission antennae are an equal interval.

An electronic device includes a plurality of sensors installed in predetermined orientations at different positions. Each of the plurality of sensors includes a transmission antenna that transmits a transmission wave, a reception antenna that receives a reflected wave that is the transmission wave having been reflected, and a control unit that detects an object that reflects the transmission wave, based on a transmission signal transmitted as the transmission wave and a reception signal received as the reflected wave. The electronic device further includes a determination unit that determines a shift in orientation of at least any of the plurality of sensors, based on detection results of an object obtained by the plurality of sensors.

An axis-misalignment estimation device estimates an axis-misalignment angle of a radar device mounted to a moving object. The axis-misalignment estimation device estimates the axis-misalignment angle using a plurality of different axis-misalignment angle estimation methods based on detection results of the radar device. The axis-misalignment estimation device determines whether a predefined employment condition is met, based on a plurality of axis-misalignment angle estimates estimated using respective ones of the plurality of axis-misalignment angle estimation methods. In response to determining that the employment condition is met, the axis-misalignment estimation device employs at least one of the plurality of axis-misalignment angle estimates.