A vehicular sensing system includes at least one radar sensor disposed at a vehicle and having a field of sensing exterior of the vehicle. The radar sensor includes multiple transmitting antennas and multiple receiving antennas. The transmitting antennas transmit signals and the receiving antennas receive the signals reflected off objects. Multiple scans of radar data are received at an electronic control unit (ECU) and processed at a processor of the ECU. The ECU detects presence of a plurality of objects exterior the equipped vehicle and within the field of sensing of the at least one radar sensor. The ECU, responsive at least in part to processing at the processor of the received multiple scans of captured radar data and received vehicle motion estimation, tracks objects detected in the received multiple scans over two or more scans.

A multiple input multiple output imaging array for incident angle resolved images with respect to a device under test is provided. The multiple input multiple output imaging array comprises a redundant array of transmit and receive antennas and a controller. In this context, the controller is configured to implement a selection scheme, wherein the selection scheme selects the respective transmit and receive antenna pairs used to create the corresponding image.

A method for the phase calibration of a MIMO radar sensor having an array of transmitting and receiving antenna elements that are offset from each other in at least one direction, and high-frequency modules, which are each assigned to a part of the array. The array is subdivided into transmitting subarrays and receiving subarrays in such a manner, that each subarray is assigned to exactly one of the high-frequency modules and at least two receiving subarrays, which belong to different high-frequency modules, are offset from each other in the at least one direction and are aligned with each other in the direction perpendicular to it. The method includes a calibration which corrects a receiving control vector with the aid of a known relationship between first and second comparison variables for the respective receiving subarrays.

A device comprising circuitry configured to: obtain radar signal measurements simultaneously acquired by two or more radar sensors having overlapping fields of view, derive range information of one or more potential targets from samples of radar signal measurements of said two or more radar sensors acquired at the same time or during the same time interval, the range information of a single sample representing a ring segment of potential positions of a potential target at a particular range from the respective radar sensor in its field of view, determine intersection points of ring segments of the derived range information, determine a region of the scene having one of the highest densities of intersection points, select a ring segment per sensor that goes through the selected region, and determine the most likely target position of the potential target from the derived range information of the selected ring segments.

A sensing system for a vehicle includes at least one radar sensor disposed at the vehicle and having a field of sensing exterior of the vehicle. The radar sensor includes multiple transmitting antennas and multiple receiving antennas. The transmitting antennas transmit signals and the receiving antennas receive the signals reflected off objects. Multiple scans of radar data sensed by the radar sensor are received at a control, and a vehicle motion estimation is received at the control. The control, responsive to received scans of sensed radar data, detects the presence of objects within the field of sensing of the radar sensor. The control, responsive to the received scans of sensed radar data and the received vehicle motion estimation, synthesizes a virtual aperture and matches objects detected in the scans and determines a separation between detected objects by tracking the detected objects over two or more scans.

In an azimuth estimation device, a center generation unit configured to generate, for each peak bin extracted by the extraction unit, a center matrix which is a correlation matrix obtained using values of the same peak bin collected from all of transmitting/receiving channels. A surrounding generation unit is configured to generate, for each of one or more surrounding bins of each of the peak bins, a surrounding matrix which is a correlation matrix obtained using values of the same surrounding bin collected from all of the transmitting/receiving channels. An integration unit is configured to generate, for each peak bin, an integrated matrix which is a correlation matrix obtained by weighting and adding the center matrix and the one or more surrounding matrices. An estimation unit is configured to execute an azimuth estimation calculation using the integrated matrix generated by the integration unit.

A signal processing device, includes: an azimuth estimation unit configured to estimate an arrival azimuth of a radio wave based on a reception signal of plural antennas; an estimated reception signal calculation unit configured to calculate an estimated reception signal based on an estimation result of the arrival azimuth, for comparison with the reception signal; a residual signal calculation unit configured to calculate a residual signal which is a difference between the reception signal and the estimated reception signal; and a determination unit configured to determine whether the estimation result of the arrival azimuth is correct based on the residual signal.

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 vehicle radar system and calibration method that provide for system calibration so that target object parameters can be calculated with improved accuracy. Generally speaking, the calibration method uses a number of hypothesized calibration matrices, which represent educated guesses for possible system or array calibrations, to obtain a number of beamforming images. A blurring metric is then derived for each beamforming image, where the blurring metric is generally representative of the quality or resolution of the beamforming image. The method then selects hypothesized calibration matrices based on their blurring metrics, where the selected matrices are associated with the blurring metrics having the best beamforming image resolution (e.g., the least amount of image blurriness). The selected hypothesized calibration matrices are then used to generate new calibration matrices, which in turn can be used to calibrate the vehicle radar system so that more accurate target object parameters can be obtained.

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.