Joint Probabilistic Data Association Algorithm Based on Adaptive Cluster Probability Matrix

A novel JPDA method for data association on multi-target tracking system was presented for reducing the class of JPDA algorithm computational complexity and solving the problem of coalesce neighboring tracks. To improve the computational complexity, the joint association event probabilities were calculated with Cheap JPDA algorithm, then the cluster probability matrix was reconstructed by thresholding method to further optimize the computational complexity. Finally, the measurement prone to make wrong association were eliminated by measurement adaptive cancellation method to avoid the track coalescence problem for neighboring tracks. Theoretical analysis and simulation results showed that the proposed algorithm was able to reduce the complexity of the algorithm and improve the timeliness on the basis of preserving the tracking accuracy, and it was also capable of avoiding track coalescence with less errors when tracking the neighboring tracks and cross tracks, comparing with the standard JPDA and Scaled JPDA algorithm.