Belief Consensus Algorithms for Distributed Target Tracking in Wireless Sensor Networks

Details Belief Consensus Algorithms for Distributed Target Tracking in Wireless Sensor Networks Belief Consensus Algorithms for Distributed Target Tracking in Wireless Sensor Networks

2012-02-23

arxiv.org/abs/1202.5261v1

Computer Science

Distributed, Parallel, and Cluster Computing

Scientific paper

In distributed target tracking in wireless sensor networks (WSN), agreement on the target state is usually achieved by the construction and maintenance of a communication path. Such an approach lack robustness to failures, and is not applicable to asynchronous networks. Recently, methods have been proposed that can solve these problems using consensus algorithms. However, these methods suffer from at least one of the following problems: i) they do not use fastest consensus methods, and ii) they cannot handle all parametric and nonparametric likelihood functions. In this paper, we propose a general framework for target tracking using distributed particle filtering (DPF) based on three asynchronous belief consensus (BC) algorithms: standard belief consensus (SBC), broadcast gossip (BG), and belief propagation (BP). Since DPF can be also solved (without consensus) by exchanging the observed data, we determine under which conditions BC-based methods are preferred. Finally, we perform extensive simulations to analyze the performance of these methods. Our main result is that DPF-BG and DPF-SBC provide the best performance in terms of root-mean square error, and that DPF-BP provides the best performance in terms of disagreement in the network.

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