Automobile millimeter-wave radar plot condensation method and system

Abstract

The invention provides an automobile millimeter-wave radar plot condensation method and system. The method comprises the steps: preferentially carrying out track and plot correlation clustering according to a target position prediction value, a Doppler speed and target length and width information fed back by the track information of a previous frame when the current frame of plot data is condensed, and then carrying out the plot-level condensation of remaining plots. Therefore, a problem that large target splitting and adjacent target merging are difficult to merge and process is avoided, and the problem of large target splitting of the automobile millimeter-wave radar is effectively solved. In addition, in the track and plot correlation clustering process, the length and width information obtained after clustering is corrected through a target recognition result, and more reliable target size feature information is ensured to be output.

Description

technical field [0001] The invention relates to the field of radar signal processing, in particular to a method and a system for agglomerating automobile millimeter-wave radar spots. Background technique [0002] In the field of automotive active safety, the automotive millimeter-wave radar sensor is an essential key component of a smart car. It has strong anti-interference ability and can quickly perceive the distance, speed, azimuth and other information of the surrounding environment of the vehicle in all weathers. Adaptive cruise control (ACC), automatic braking (AEB), forward / rearward collision avoidance warning (F / RCW), blind spot detection (BSD), auxiliary lane change (LCA), and parking assistance can be realized by using millimeter-wave radar (PA) and other advanced driver assistance system (ADAS) functions. [0003] General trace aggregation algorithms, such as sliding window method, labeling method, neighborhood search, clustering algorithm, etc., its basic assump...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to observation angles, distances, target RCS fluctuations, detection algorithms, etc., the minimum distance between two clusters of traces from a large truck may exceed 10 meters, while the minimum distance between two clusters of traces from two vehicles traveling at the same speed The minimum distance between two clusters of traces on the trolley may be less than 5 meters

The same measurement error, observation angle, distance, detection algorithm and other reasons, because the length and width distribution of the point traces from the same target may be quite different from the real physical size, the target length and width size information obtained from the single frame point trace distribution statistics is completely dependent on Target measurement, poor reliability

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