Nearest-neighbour Joint Probabilistic Data Association Filter Based on Random Finite Set

The joint probabilistic data association (JPDA) filter is effective for multitarget, but it suffers from the track coalescence problem. To solve this problem, an improved nearest-neighbour JPDA filter based on random finite set is proposed. First, the standard JPDA filter is utilized to compute the target posterior density. Then, the posterior density is optimized by reordering the target index in each global association event using a novel nearest-neighbour method. Finally, the marginalized posterior densities of targets are obtained as independent Gaussian densities. Compared to conventional data association methods, the proposed approach needs less computing time and achieves satisfactory tracking accuracy.