TY - GEN
T1 - Track probability hypothesis density filter for multi-target tracking
AU - Wang, Yan
AU - Meng, Huadong
AU - Zhang, Hao
AU - Wang, Xiqin
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/80052436113
U2 - 10.1109/RADAR.2011.5960610
DO - 10.1109/RADAR.2011.5960610
M3 - Conference contribution
AN - SCOPUS:80052436113
SN - 9781424489022
T3 - IEEE National Radar Conference - Proceedings
SP - 612
EP - 615
BT - RadarCon'11 - In the Eye of the Storm
T2 - 2011 IEEE Radar Conference: In the Eye of the Storm, RadarCon'11
Y2 - 23 May 2011 through 27 May 2011
ER -