Sensor Fusion and Target Tracking Using Evidential Data Association

The problem of multitarget tracking in clutter has been shown to be very challenging for both the measurement of track association and the estimation of the targets' state. Several approaches have been suggested to solve this problem. In this paper, we present a new method for data association in multitarget tracking in the framework of evidence theory. The representation and the fusion of the information in our method are based on the use of belief function in the sense of Dempster-Shafer theory of evidence. The proposal introduces a method of computing mass assignment using a normalized Mahalanobis distance. While the decision making process is based on the extension of the frame of hypotheses, the method has been tested for a nearly constant velocity target and compared with both the nearest neighbor filter and the joint probabilistic data associations filter in highly ambiguous cases using Monte Carlo simulations. The results demonstrate the feasibility of the proposal, and show improved performance compared to the aforementioned alternative commonly used methods.