Belief consensus algorithms for fast distributed target tracking in wireless sensor networks
Artikel i vetenskaplig tidskrift, 2014

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. (C) 2013 Elsevier B.V. All rights reserved.

Belief consensus

Wireless sensor networks

Belief propagation

Particle filtering

Distributed target tracking


Vladimir Savic

Universidad Politecnica de Madrid

Linköpings universitet

Henk Wymeersch

Chalmers, Signaler och system, Kommunikation, Antenner och Optiska Nätverk

Santiago Zazo

Universidad Politecnica de Madrid

Signal Processing

0165-1684 (ISSN)

Vol. 95 149-160


Europeiska kommissionen (EU) (EC/FP7/258418), 2011-05-01 -- 2016-04-30.


Elektroteknik och elektronik



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