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Sensors 2014, 14(11), 21281-21315;

Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks

School of Information Science & Technology, Southwest Jiaotong University, Chengdu 610031, China
Department of Electrical & Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA
The Institute for Communication Systems, University of Surrey, Guildford, Surrey GU2 7XH, UK
Author to whom correspondence should be addressed.
Received: 31 July 2014 / Revised: 28 October 2014 / Accepted: 4 November 2014 / Published: 11 November 2014
(This article belongs to the Section Sensor Networks)
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The received signal strength (RSS)-based online tracking for a mobile node in wireless sensor networks (WSNs) is investigated in this paper. Firstly, a multi-layer dynamic Bayesian network (MDBN) is introduced to characterize the target mobility with either directional or undirected movement. In particular, it is proposed to employ the Wishart distribution to approximate the time-varying RSS measurement precision’s randomness due to the target movement. It is shown that the proposed MDBN offers a more general analysis model via incorporating the underlying statistical information of both the target movement and observations, which can be utilized to improve the online tracking capability by exploiting the Bayesian statistics. Secondly, based on the MDBN model, a mean-field variational Bayesian filtering (VBF) algorithm is developed to realize the online tracking of a mobile target in the presence of nonlinear observations and time-varying RSS precision, wherein the traditional Bayesian filtering scheme cannot be directly employed. Thirdly, a joint optimization between the real-time velocity and its prior expectation is proposed to enable online velocity tracking in the proposed online tacking scheme. Finally, the associated Bayesian Cramer–Rao Lower Bound (BCRLB) analysis and numerical simulations are conducted. Our analysis unveils that, by exploiting the potential state information via the general MDBN model, the proposed VBF algorithm provides a promising solution to the online tracking of a mobile node in WSNs. In addition, it is shown that the final tracking accuracy linearly scales with its expectation when the RSS measurement precision is time-varying. View Full-Text
Keywords: online tracking; Bayesian network; variational Bayesian filtering; WSN online tracking; Bayesian network; variational Bayesian filtering; WSN
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Zhou, B.; Chen, Q.; Li, T.J.; Xiao, P. Online Variational Bayesian Filtering-Based Mobile Target Tracking in Wireless Sensor Networks. Sensors 2014, 14, 21281-21315.

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