Target Tracking based on Millimeter Wave Radar in Complex Scenes

摘要

Currently, the method of using millimeter wave radar to detect obstacles in front of vehicles has been widely used. When using millimeter wave radar to detect obstacles on the road, the radar has more noise interference due to the changeable road environment and complex background. Combined with the complexity and variety of road targets, the random changes of scattering intensity and relative phase of different parts cause the distortion of the echo phase wave, resulting in the flicker noise that affects the accuracy of measurement, and even lead to the loss of targets. In this case, there are some shortcomings in tracking the target using the ordinary Kalman filter algorithm. In this paper, a Sage-Husa adaptive Kalman filtering algorithm is designed for the road environment to track radar targets and improve the accuracy of target tracking. Then, the radar and machine vision information fusion method is used to intuitively judge the filtering effect and determine whether the radar loses the target. Finally, the true value of the target position is approximated by filtered value when the radar loses its target. The experimental results show that this method can improve the accuracy and reliability of the millimeter wave radar.