Static Targets Recognition and Tracking Based on Millimeter Wave Radar

Due to the poor ability of millimeter wave radar in recognizing distant static objects, target loss and incomplete information will occur when it recognizes the static target in front, thus increasing the false alarm rate and missing alarm rate of the radar-dependent driving assistant system, which will reduce the driving safety and the acceptability of the assistant system. Aiming at the radar's poor ability to recognize static targets, this paper uses a model based on machine learning algorithm to recognize and track targets. The radar signals are collected and processed in different conditions, and the results show that the radar has a poor recognition effect when the distance is more than 100 meters and the speed is more than 19m/s. Taking the relative distance and relative velocity of the radar target as observation quantity, Gaussian Hidden Markov Model (GHMM) is used to learn the labeled data from radar, and the nonlinear relationship among the relative distance, relative velocity and target states are obtained. Moreover, through the clustering of Gaussian Model (GM), the target’s state can be predicted according to the radar signal, and the forward-backward algorithm can be used to track the target. The results show that the model can effectively improve the accuracy of target prediction and tracking effect when the distance is around 140 meters and the speed is around 30m/s.