Generalized Probability Data Association Algorithm
Hongcai Zhang · Dianzi xuebao · 2005
With the change and development of modern multi-target tracking system,it is ve ry difficult to deal with data association problems simply using the feasible r ule based on the hypothesis in which the association of measurements with target s is o ne-to-one correlated to each other,as is commonly used in JPDA.We have noti c ed 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 algorith m 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-tar get tracking system.Based on the new feasible rule,we define a new concept of ge neralized joint event.We present a method to segment the generalized joint event set into two generalized event sets and then a combination method with the two sub-sets is put forward.A Generalized Probability Data Association (GPDA) al gorithm is deduced by using Bayesian rule.Additionally,we analyze the performanc e of GPDA algorithm in various given tracking environments by using Monte Carlo simulation.We compare the computation burden and computing memory with JPDA algo rithm.All simulation results show that the performance of GPDA is superior to th at of JPDA,and the algorithm has much smaller computation burden than JPDA.