Measurements Optimal Assigned GM-PHD Multi-target Tracking Algorithm

As describing target state space and sensor observation space by Random Finite Set (RFS) method, Probability Hypothesis Density (PHD) filter is a promising approach for multi-target tracking without consideration of measurement-to-track association. Due to the fact that classic GM-PHD filer has not distinguish the measurements of survival targets and birth targets, a Measurements Optimal Assigned GM-PHD filter (MOA-GM-PHD) is proposed in this paper, which distinguishes the measurements set by using a max likelihood adaptive gate, and survival targets and birth targets update the PHD estimation using the optimal assigned measurements respectively. Simulation results show that the proposed algorithm obtained an improved performance.