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Distributed Gaussian particle filtering using likelihood consensus

  • TU Wien

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

45 Scopus citations

Abstract

We propose a distributed implementation of the Gaussian particle filter (GPF) for use in a wireless sensor network. Each sensor runs a local GPF that computes a global state estimate. The updating of the particle weights at each sensor uses the joint likelihood function, which is calculated in a distributed way, using only local communications, via the recently proposed likelihood consensus scheme. A significant reduction of the number of particles can be achieved by means of another consensus algorithm. The performance of the proposed distributed GPF is demonstrated for a target tracking problem.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages3756-3759
Number of pages4
DOIs
StatePublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: May 22 2011May 27 2011

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period05/22/1105/27/11

Keywords

  • distributed particle filter
  • Gaussian particle filter
  • likelihood consensus
  • target tracking
  • wireless sensor network

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