Multi-target tracking method based on millimeter wave radar

Abstract

The invention belongs to the field of radar signal processing, and particularly provides a multi-target tracking method based on a millimeter-wave radar, which can be used for multi-target tracking of the millimeter-wave radar in a close-range scene. According to the method, Doppler dimension information of a millimeter wave radar is fully utilized, the correlation probability of a track and a radar clustering target is calculated through a Doppler D-CJPDA algorithm, cascade matching is taken as a framework, and data correlation is realized through a KM matching algorithm; meanwhile, in consideration of a target missing detection condition, secondary data association is performed on clutter trace points through an elliptical association gate algorithm, so that the probability of target tracking loss is reduced; moreover, after the successfully matched tracks are replaced by radar measurement plots belonging to the same clustering target, the tracks are updated by using a Doppler PDAF filter, so that the precision of multi-target tracking is improved. In conclusion, the method has the advantage of high tracking accuracy.

Description

technical field [0001] The invention belongs to the field of radar signal processing, and specifically provides a multi-target tracking method based on a millimeter-wave radar, which can be used for multi-target tracking of a millimeter-wave radar in a close-range scene. Background technique [0002] With the rapid development of radar technology and the increasing demand for multi-target tracking in modern military and civilian applications, the research on multi-target tracking technology is deepening. ), civil aspects (including mobile robots, intelligent transportation systems, intelligent security monitoring, etc.) are widely used. Today, multi-target tracking technology involves a number of discipline-related fields. According to the overall process of multi-target tracking, the start and end of the track, the setting of the tracking threshold, the maintenance of the tracking, the collection and processing of data, and the association of the collected data. Processing...

AI Technical Summary

Problems solved by technology

DeepSort, a classic multi-target tracking algorithm based on deep learning, converts the data association problem into a weighted bipartite graph matching problem, and implements the KM algorithm to solve the matching of track and target; however, the multi-target tracking algorithm based on millimeter wave radar , the traditional radar target state information only has target position information, compared with the rich appearance information of the image, the error of calculating the data association index is relatively large; at the same time, the algorithm using the Bayesian criterion correlation algorithm will lead to an exponential increase in computational complexity, Difficult to apply to actual scene requirements

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