Hybrid probabilistic data association and variational filtering for multi-target tracking in wireless sensor networks

A hybrid signal processing scheme is proposed for distributed multi-target tracking (MTT). For the sake of resource efficiency in a wireless sensor network (WSN), we reduce the problem to parallel cluster-based single target tracking when the targets are far apart, and switch to MTT only when data association becomes ambiguous. A sequential monte carlo method is employed to assign the ambiguous observations to specific targets or clutter, based on association probabilities. Whereas the rest observations are incorporated by the variational filter, which approximates the distribution of involved particles by a simple Gaussian distribution for each target. The natural and adaptive message compression dramatically reduces the resource consumption of the WSN. The low computation complexity also guarantees the one-line execution of the hybrid MTT scheme. In addition, experimental results prove that the proposed scheme succeeds in distinguishing and tracking multiple targets even during the occlusions.

[1]  P. Pérez,et al.  Tracking multiple objects with particle filtering , 2002 .

[2]  Hichem Snoussi,et al.  Binary Variational Filtering for Target Tracking in Sensor Networks , 2007, 2007 IEEE/SP 14th Workshop on Statistical Signal Processing.

[3]  Patrick Pérez,et al.  Sequential Monte Carlo methods for multiple target tracking and data fusion , 2002, IEEE Trans. Signal Process..

[4]  Yaakov Bar-Shalom,et al.  Multi-target tracking using joint probabilistic data association , 1980, 1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes.

[5]  J. Liu,et al.  Multitarget Tracking in Distributed Sensor Networks , 2007, IEEE Signal Processing Magazine.

[6]  Donald Reid.  An algorithm for tracking multiple targets , 1978 .

[7]  Lui Sha,et al.  Dynamic clustering for acoustic target tracking in wireless sensor networks , 2003, IEEE Transactions on Mobile Computing.

[8]  Songhwai Oh,et al.  Markov chain Monte Carlo data association for general multiple-target tracking problems , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[9]  A. Doucet,et al.  Particle filtering for multi-target tracking and sensor management , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).

[10]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[11]  H. Snoussi.  Ensemble Learning Online Filtering in Wireless Sensor Networks , 2006, 2006 10th IEEE Singapore International Conference on Communication Systems.