Multitarget PHD Particle Filter Tracker Based on Single-Target PHD

摘要

Probability hypothesis density (PHD) filter is a new practical method for tracking multiple targets. However, to obtain the output of the PHD filter, peaks-extraction and association between frames is needed to estimate the target states and the individual target trajectories. A new PHD filter tracker based on single-target PHD is proposed. The method estimates target states by decomposing PHD into single-target PHDs, and associates target location estimates between time frames based on the additional labels of particles. Simulation results demonstrate that the new algorithm provides more accurate trajectory estimations and is more efficient than the PHD tracker with k-means algorithm and the usual particle-labeling association.