Data association combined with the probability hypothesis density filter for multi-target tracking

The particle probability hypothesis density (P-PHD) filter gives estimate of target state for multi-target tracking; however, it keeps no record of target identities and is not able to generate target tracks. This paper addresses the problem of data association (track continuity) using the particle probability hypothesis Density filter based on the particle cloud aliasing method, that is, the corresponding particle clouds originated from the same target at two successive time steps overlap each other largely. Thus, suitable associated state pairs selected from estimated state sets at successive time steps can be found to generate tracks step by step. Estimated tracks obtained by the proposed approach are basically more consistent with the true tracks compared with that of particle labeling association algorithm according to the simulation results.