Time-space-sequential algorithms for distributed Bayesian state estimation in serial sensor networks

We consider distributed estimation of a time-dependent, random state vector based on a generally nonlinear/non-Gaussian state-space model. The current state is sensed by a serial sensor network without a fusion center. We present an optimal distributed Bayesian estimation algorithm that is sequential both in time and in space (i.e., across sensors) and requires only local communication between neighboring sensors. For the linear/Gaussian case, the algorithm reduces to a time-space-sequential, distributed form of the Kalman filter. We also demonstrate the application of our state estimator to a target tracking problem, using a dynamically defined “local sensor chain” around the current target position.

[1]  Leonidas J. Guibas,et al.  Wireless sensor networks - an information processing approach , 2004, The Morgan Kaufmann series in networking.

[2]  Richard G. Baraniuk,et al.  Robust Distributed Estimation Using the Embedded Subgraphs Algorithm , 2006, IEEE Transactions on Signal Processing.

[3]  Feng Zhao,et al.  Collaborative In-Network Processing for Target Tracking , 2003, EURASIP J. Adv. Signal Process..

[4]  Branko Ristic,et al.  Beyond the Kalman Filter: Particle Filters for Tracking Applications , 2004 .

[5]  Xiaodong Wang,et al.  Decentralized sigma-point information filters for target tracking in collaborative sensor networks , 2005, IEEE Transactions on Signal Processing.

[6]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[7]  Petros G. Voulgaris,et al.  On optimal ℓ∞ to ℓ∞ filtering , 1995, Autom..

[8]  Feng Zhao,et al.  Scalable Information-Driven Sensor Querying and Routing for Ad Hoc Heterogeneous Sensor Networks , 2002, Int. J. High Perform. Comput. Appl..

[9]  Tong Zhao,et al.  Distributed Sequential Bayesian Estimation of a Diffusive Source in Wireless Sensor Networks , 2007, IEEE Transactions on Signal Processing.

[10]  Tong Zhao,et al.  Information-Driven Distributed Maximum Likelihood Estimation Based on Gauss-Newton Method in Wireless Sensor Networks , 2007, IEEE Transactions on Signal Processing.

[11]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[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.