Binary Variational Filtering for Target Tracking in Sensor Networks

Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limited energy supply and bandwidth of WSN have put stringent constraints on the complexity and inter-node information exchange of the tracking algorithm. In this paper, we propose a binary variational algorithm outperforming existing target tracking algorithms such as Kalman and Particle filtering. The variational formulation allows an implicit compression of the exchanged statistics between leader nodes, enabling thus a distributed decision-making. Its binary extension further reduces the resource consumption by locally exchanging only few bits.

[1]  Parameswaran Ramanathan,et al.  Distributed particle filter with GMM approximation for multiple targets localization and tracking in wireless sensor network , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[2]  Patrick Pérez,et al.  Variational inference for visual tracking , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[3]  A. Willsky,et al.  Particle filtering under communications constraints , 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

[4]  Feng Zhao,et al.  Information-driven dynamic sensor collaboration , 2002, IEEE Signal Process. Mag..

[5]  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).

[6]  Nikos A. Vlassis,et al.  Accelerated EM-based clustering of large data sets , 2006, Data Mining and Knowledge Discovery.

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

[8]  Feng Zhao,et al.  Information-Driven Dynamic Sensor Collaboration for Tracking Applications , 2002 .

[9]  Mónica F. Bugallo,et al.  Tracking with particle filtering in tertiary wireless sensor networks , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[10]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[11]  Xiaodong Wang,et al.  Joint multiple target tracking and classification in collaborative sensor networks , 2005, IEEE Journal on Selected Areas in Communications.

[12]  Stergios I. Roumeliotis,et al.  SOI-KF: Distributed Kalman Filtering With Low-Cost Communications Using The Sign Of Innovations , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.