Generalized probability data association algorithm

With the change and development of modern multi-target tracking system, it is very difficult to deal with data association problems 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. We have noticed that T. Kirubarajan and Bar-Shalom et al. gave some new results trying to solve the problem. But the performance, especially the computing burden of the algorithm can not be satisfied by most real time systems. In this paper, we put forward a new feasible rule which is more suitable for practical environment of multi-target tracking system. Based on the new feasible rule, we define a new concept of generalized joint event. We present a method to segment the generalized joint event set into two generalized event sub-sets and then a combination method with the two sub-sets is put forward. A Generalized Probability Data Association (GPDA) algorithm is deduced by using Bayesian rule. Additionally, we analyze the performance of GPDA algorithm in various given tracking environments by using Monte Carlo simulation. We compare the computation burden and computing memory with JPDA algorithm. All simulation results show that the performance of GPDA is superior to that of JPDA, and the algorithm has much smaller computation burden than JPDA.