Track probability hypothesis density filter for multi-target tracking

The probability hypothesis density (PHD) filter is a practical alternative to the theoretically optimal multi-target Bayesian filter based on random finite sets (RFS) for multi-target tracking. In this paper, we propose Track PHD (TPHD) filter based on a track state space consisted of target position history and it propagates the multi-target intensity function of track RFS. The new filter provides the estimates of target track states and makes it easy to confirm identities. Simulation results demonstrate TPHD filter is effective in estimating multi-target states and providing target identities even when targets are in close proximity.