Generalized Probability Data Association Algorithm

Q. Pan, X. Ye, F. Yang, and H. Zhang (PRC)


Multi-target tracking, data association, generalized joint event, generalized probability data association


With the development of modern multi-target tracking system, it is very difficult to deal with data association problems by simply using the feasible rule based on the hypothesis in which the association of measurements with targets is one-to-one correlated to each other, as is commonly used in JPDA. A new feasible rule is firstly put forward which is more suitable for practical environment of multi-target tracking system. Based on the new feasible rule, generalized joint event is defined. The generalized joint event set is divided into two generalized event sets and then a combination method with the two sub-sets is put forwarded. Further a Generalized Probability Data Association (GPDA) algorithm is deduced by using Bayesian rule. Additionally, the performance of GPDA algorithm is analyzed in various given tracking environments by using Monte Carlo simulation. All simulation results show that the performance of GPDA is superior to that of JPDA, and the algorithm has much smaller computational burden than JPDA.

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