Target Tracking by Cameras and Millimeter-Wave Radars: A Confidence Information Fusion Method

This paper addresses a confidence fusion problem of the camera and the millimeter-wave (MMW) radar for target tracking in intelligent driving systems. The local camera and radar estimators are performed by analyzing the measurement characteristics of each sensor. The radar estimates are aligned to the camera sampling time and the Kuhn-Munkers method is used to obtain the matching relationship of local camera and radar estimates for fusion. Next, to utilize the advantage of the camera with low false detection and the radar with low miss detection performance, the mass functions are introduced to model the detection performance of the two sensors. Based on the mass functions and a D-S (Dempster-Shafer) evidence theory, the confidence fusion is performed sequentially to determine whether each target exists. Then a weighted maximum likelihood fusion estimator is designed for matched targets based on priori positing accuracy of the local camera and radar estimates. Finally, the experimental results on road vehicle tracking show that the detection range is expanded and false targets are significantly reduced by the proposed confidence fusion method.