Gm Global Technolgoy Operations, Inc.

Ishai Eljarat

Igal Bilik

Gonen Barkan

Shahar Villeval

Alexander Pokrass

Gaston Solodky

Ilya Shapir Poltorak

A method includes obtaining an initial point cloud for each of two or more radar systems that share a common field of view. Each initial point cloud results from processing reflected energy at each of the two or more radar systems. Each point of the initial point cloud indicates one or more hypotheses for a range, a Doppler, and a direction of arrival (DOA) to an object that resulted in the reflected energy. A point cloud, obtained from the initial point cloud, has a same number of hypotheses for the range, the Doppler, and the DOA. Resolving ambiguity in the common field of view is based on the point clouds to obtain resolved and unresolved points in the common field of view. A radar image obtained from each of the two or more radar systems is used to control an aspect of operation of a vehicle.

Ambiguity mitigation based on common field of view of radar systems

A system and method to detect a target with a radar system of a vehicle involve transmitting two or more chirps, in turn, from two or more transmit elements. Each chirp is a continuous wave liner frequency modulated waveform. The method also includes receiving reflections generated by each of the two or more chirps from each of the two or more transmit elements at two or more receive elements, and processing the reflections based on a Doppler sampling frequency corresponding with a period of each of the two or more chirps to determine velocity of each detected target relative to the vehicle.

Resolving doppler ambiguity in multi-input multi-output radar using digital multiple pulse repetition frequencies

A vehicle, radar system and method of detecting an object is disclosed. The radar system includes plurality of sensors a plurality of sensors, wherein a field of view of one of the plurality of sensors overlaps a field of view of at least another of the plurality of sensors at a region, and a processor. The processor is receives from the plurality of sensors a detection from the region, the detection related to an object, determines an overlap value for the region, adjusts a density parameter of a clustering criterion based on the overlap value, and determines a valid clustering of the detection with a neighboring detection using the adjusted density parameter.

DBSCAN parameters as function of sensor suite configuration

A system and method to perform target tracking with a radar system that uses a Kalman filter include predicting a predicted target position of a target detected by the radar system in a frame. The frame is a period of time to transmit from every transmit element of the radar system in turn and receive the reflections from a range of the target. The method also includes determining an actual target position of the target detected by the radar system based on reflections received by the radar system for the frame, and computing a process noise covariance matrix Q for a next frame, immediately following the frame, based on the predicted target position and the actual target position. Predicting a position of the target in the next frame is based on the process noise covariance matrix Q.

Adaptive process noise description for improved kalman filter target tracking

Methods and systems are provided for controlling a radar system of a vehicle. One or more transmitters are configured to transmit radar signals and tracking information is translated and coupled between radar transmitters in order to reduce target acquisition times when commencing a new track.

Method and apparatus for continuous tracking in a multi-radar system

A vehicle, system and method of driving the vehicle. A radar system obtains a measured location of an object in an environment of the vehicle. A processor predicts a location of the object, determines a distance between the predicted location of the object and the measured location of the object, determines a confidence level for the predicted location the object based on the determined distance, selects a tracking state for the object based on the confidence level, and drives the vehicle to avoid the object according to the tracking state of the object. The processor and radar system may be onboard the vehicle.

Growing community of inventors

Raanana, Israel

Ishai Eljarat