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In distributed target tracking for wireless sensor networks, agreement on thetarget state can be achieved by the construction and maintenance of acommunication path, in order to exchange information regarding local likelihoodfunctions. Such an approach lacks robustness to failures and is not easilyapplicable to ad-hoc networks. To address this, several methods have beenproposed that allow agreement on the global likelihood through fullydistributed belief consensus (BC) algorithms, operating on local likelihoods indistributed particle filtering (DPF). However, a unified comparison of theconvergence speed and communication cost has not been performed. In this paper,we provide such a comparison and propose a novel BC algorithm based on beliefpropagation (BP). According to our study, DPF based on metropolis beliefconsensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensusis the fastest in tree graphs. Moreover, we found that BC-based DPF methodshave lower communication overhead than data flooding when the network issufficiently sparse.

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