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Belief consensus algorithms for fast distributed target tracking in wireless sensor networks
Linköping University, Department of Electrical Engineering, Communication Systems. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-7158-1525
Chalmers University of Technology, Gothenburg, Sweden.
Technical University of Madrid, Spain.
2014 (English)In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 95, 149-160 p.Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
Elsevier, 2014. Vol. 95, 149-160 p.
Keyword [en]
Belief consensus, belief propagation, distributed target tracking, particle filtering, wireless sensor networks
National Category
Signal Processing Communication Systems Control Engineering
URN: urn:nbn:se:liu:diva-97419DOI: 10.1016/j.sigpro.2013.09.005ISI: 000326912000015OAI: diva2:647685
eLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Foundation for Strategic Research
Available from: 2013-09-12 Created: 2013-09-12 Last updated: 2013-12-09

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