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Sensor localization using nonparametric generalized belief propagation in network with loop
Technical University of Madrid, Spain. (Signal Processing Applications Group)
Technical University of Madrid, Spain. (Signal Processing Applications Group)
2009 (English)In: IEEE Proc. of Intl. Conf. on Information Fusion (FUSION), 2009Conference paper, Oral presentation only (Refereed)
Abstract [en]

Belief propagation (BP) is one of the best-known graphical model for inference in statistical physics, artificial intelligence, computer vision, etc. Furthermore, a recent research in distributed sensor network localization showed us that BP is an efficient way to obtain sensor location as well as appropriate uncertainty. However, BP convergence is not guaranteed in a network with loops. In this paper, we propose localization using generalized belief propagation based on junction tree method (GBP-JT) and nonparametric (particle-based) approximation of this algorithm (NGBP-JT). We illustrate it in a network with loop where BP shows poor performance. In fact, we compared estimated locations with nonparametric belief propagation (NBP) algorithm. According to our simulation results, GBP-JT resolved the problems with loops, but the price for this is unacceptable large computational cost. Therefore, our approximated version of this algorithm, NGBP-JT, reduced significantly this cost, with little effect on accuracy.

Place, publisher, year, edition, pages
2009.
Keyword [en]
Localization, generalized belief propagation, junction tree, loops, particle filters
National Category
Engineering and Technology Signal Processing Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-81430ISBN: 978-0-9824-4380-4 (print)OAI: oai:DiVA.org:liu-81430DiVA: diva2:552428
Conference
Intl. Conf. on Information Fusion (FUSION), Seattle, US
Available from: 2012-09-14 Created: 2012-09-14 Last updated: 2012-09-21

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Sensor localization using nonparametric generalized belief propagation in network with loops

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Savic, Vladimir

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf