Track probability hypothesis density filter for multi-target tracking

@article{Wang2011TrackPH,
  title={Track probability hypothesis density filter for multi-target tracking},
  author={Yan Wang and Huadong Meng and Hao Zhang and Xiqin Wang},
  journal={2011 IEEE RadarCon (RADAR)},
  year={2011},
  pages={612-615}
}
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… CONTINUE READING