Aptiv Technologies Ag

Baokun Liu

Benjamin Dilsaver

James F Searcy

Xiuzhang Cai

Yi Feng

This document describes techniques and systems related to a radar system using a machine-learned model to identify the number of objects within each range-Doppler bin. For example, the radar system includes a processor that obtains radar data associated with objects and processes the radar data to generate beam vectors. The processor then uses a machine-learned model to identify the number of objects within each range-Doppler bin using extracted features (e.g., magnitude variation, signal-to-noise ratio, subarray beam vector correlations) of the beam vectors. The processor selects a particular angle-finding technique based on whether a single object or multiple objects are identified. In this way, the described systems and techniques more accurately identify the number of object in each range-Doppler bin, thus improving the computational efficiency and robustness of subsequent angle finding.

Radar system using a machine-learned model for identifying a number of objects

This document describes techniques, apparatuses, and systems for radar interference mitigation using signal pattern matching. Radar signals (e.g., chirps) received by a radar system may include interference from other nearby radar systems. The interference can result in reduced sensitivity of the radar system. The techniques, apparatuses, and systems described herein mitigate the interference by selecting an uncorrupted segment of the radar signal that neighbors a corrupted segment, analyzing the radar signal to identify a match segment that has similar signal characteristics to the neighbor segment, and replacing the corrupted segment with a segment that is adjacent to the match segment. In this manner, a noise floor of the radar system may be lowered, leading to increased sensitivity.

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Kokomo, IN, United States of America

Baokun Liu