The optimal Bayesian multi-target tracking is computationally demanding. The probability hypothesis density (PHD) filter, which is a first moment approximation of the optimal one, is a computationally tractable alternative. By evaluating the PHD, one can extract the number of targets as well as their individual states. Recent sequential Monte Carlo (SMC) implementation of the PHD filter paves the way to apply the PHD filter to nonlinear non-Gaussian problems. It seems that the particle implementation of PHD filter is more dependent on current measurements, especially in the case of low observable target problems (i.e., estimates are sensitive to missed detections and false alarms). In this paper, a PHD smoothing algorithm is proposed to improve the capability of the PHD based tracking system. By performing smoothing, which gives delayed estimates, we will get not only better estimates for target states but also better estimate for number of targets. Simulations are performed on proposed method with a multi-target scenario. Simulation results confirm the improved performance of the proposed algorithm.

Date Published: 25 September 2007
PDF: 8 pages
Proc. SPIE 6699, Signal and Data Processing of Small Targets 2007, 66990M (25 September 2007); doi: 10.1117/12.734656

Published in SPIE Proceedings Vol. 6699:
Signal and Data Processing of Small Targets 2007
Oliver E. Drummond; Richard D. Teichgraeber, Editor(s)