Belief consensus algorithms for fast distributed target tracking in wireless sensor networks
Journal article, 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

Author

Vladimir Savic

Technical University of Madrid

Linköping University

Henk Wymeersch

Chalmers, Signals and Systems, Communication and Antenna Systems, Communication Systems

Santiago Zazo

Technical University of Madrid

Signal Processing

0165-1684 (ISSN)

Vol. 95 149-160

Cooperative Situational Awareness for Wireless Networks (COOPNET)

European Commission (FP7), 2011-05-01 -- 2016-04-30.

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1016/j.sigpro.2013.09.005

More information

Latest update

3/29/2018