Multiple target tracking using particle filter based multi-scan joint probabilistic data association filter

In this paper, a particle filter based multi-scan JPDA filter is proposed to deal with the data association problem in multiple target tracking. In the proposed multi-scan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multi-scan JPDA algorithm examines the joint association events in a multi-scan sliding window and calculates the marginal posterior probability based on the multi-scan joint association events. The proposed algorithm is illustrated via an example involving tracking of two slowly maneuvering targets based on resolved measurements.