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Fourier Density Approximation for Belief Propagation in Wireless Sensor Networks

Na, Chongning; Wang, Hui; Obradovic, Dragan; Hanebeck, Uwe D.

Many distributed inference problems in wireless sensor networks can be represented by probabilistic graphical models, where belief propagation, an iterative message passing algorithm provides a promising solution. In order to make the algorithm efficient and accurate, messages which carry the belief information from one node to the others should be formulated in an appropriate format. This paper presents two belief propagation algorithms where non-linear and non-Gaussian beliefs are approximated by Fourier density approximations, which significantly reduces power consumptions in the belief computation and transmission. We use self-localization in wireless sensor networks as an example to illustrate the performance of this method.

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Volltext §
DOI: 10.5445/IR/1000034851
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2008
Sprache Englisch
Identifikator ISBN: 978-1-4244-2144-2
KITopen-ID: 1000034851
Erschienen in Proceedings of IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems Seoul, Korea, August 20 - 22, 2008
Verlag IEEE, Piscataway
Seiten 290-295
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