Particle labeling PHD filter for multi-target track-valued estimates

Multi-target tracking is a difficult problem due to the measurement origin uncertainty. Recently, the probability hypothesis density (PHD) filter provides a promising tool for joint estimation of target number and multi-target states, without using data association technique. In particle implementations of the PHD filter, clustering is used to extract the target state from the particle population. This technique yields poor performance when the estimated number of targets differs from the number of clusters in the particle population. A particle labeling PHD filter for multi-target track-valued estimates is developed in this paper. By implementing the efficient sampling and particle labeling technique, the proposed method can yield not only better state estimate, but also track-valued estimate. The multi-scan measurement information is also employed to reduce the uncertainty of the estimates. Simulation results demonstrate the efficiency of the proposed method.