The Investigation Of Multitarget Data Association Algorithms

This paper presents a comprehensible mathematics description of the joint probabilistic data association (JPDA) algorithm. In order to reduce the time梥pace complexity of the JPDA algorithm, the method of dividing validation matrix based on the .back track ?pruning technique is proposed in this paper. i抙en we utilize the fuzzy set theory to model the tracking filter. The fuzzy probabilistic data association of multiple targets tracking is presented in this paper, Which define a target梖uzzy set on the measurement set at time k and then use fuzzy least mean square error method to estimate target states. Simulation results show that our algorithm is of high tracking precision and is robust to the uncertainty of the clutter environment and multiple targets.