Directional Fuzzy Data Association Filter

In this paper, a new multi-target tracking algorithm based on fuzzy logic for tracking in clutter is developed, it is called directional fuzzy data association (DFDA) filter. The new algorithm incorporates the directional information of the targets for data association with the Mahalanobis distance. Firstly, the directional information, called pseudo-direction, is defined; the method of how to calculate the pseudo-direction has been introduced. Then the state incorporating with the pseudo-direction is updated using the cubature Kalman filter (CKF). At last the fuzzy logic inference method is used for data association. Simulation results are used to evaluate the performance of this new algorithm comparing with the nearest neighbor standard filter (NNSF) and joint probability data association filter (JPDAF), the final results show that the proposed DFDA filter an efficient and effective approach for real application.