Coalescence-avoiding joint probabilistic data association based on bias removal

Abstract

Abstract In order to deal with the track coalescence problem of the joint probabilistic data association (JPDA) algorithm, a novel approach from a state bias removal point of view is developed in this paper. The factors that JPDA causes the state bias are analyzed, and the direct computation equation of the bias in the ideal case is given. Then based on the definitions of target detection hypothesis and target-to-target association hypothesis, the bias estimation is extended to the general and practical case. Finally, the estimated bias is removed from the state updated by JPDA to generate the unbiased state. The results of Monte Carlo simulations show that the proposed method can handle track coalescence and presents better performance when compared with the traditional methods.