Track Initiation Method Based on Deep Learning and Logic Method

Abstract

This paper introduces a method of vehicle millimeter wave radar track initiation based on deep learning. In the complex and transient road environment of automobile radar, fast and correct track initiation is the key to multi-target tracking. In this paper, two improvements have been made to the classical logic method. One is to use YOLOv5 instead of CFAR to detect the target in the range Doppler image to improve the target detection effect. The other is to improve the track start condition of the logic method and use the idea of probability event to improve the track start speed. The m/n logic in the improved method means that if the probability of no less than n dots in the sliding window is greater than 90% or the probability of at least n dots being true is not less than 80%, the track will start successfully. This paper proves the superiority of the track initiation method based on deep learning and logic method in several special cases.