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Sequential likelihood consensus and its application to distributed particle filtering with reduced communications and latency

  • TU Wien

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

10 Scopus citations

Abstract

We propose a sequential likelihood consensus (SLC) for a distributed, sequential computation of the joint (all-sensors) likelihood function (JLF) in a wireless sensor network. The SLC is based on a novel dynamic consensus algorithm, of which only a single iteration is performed per time step. We demonstrate the application of the SLC in a distributed particle filter with low communication requirements and low latency. Because the JLF is available at each sensor, the local particle filters at the individual sensors take into account the measurements of all sensors. The performance of the proposed distributed particle filter is assessed for a target tracking problem.

Original languageEnglish
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages1766-1770
Number of pages5
DOIs
StatePublished - 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Conference

Conference45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/6/1111/9/11

Keywords

  • distributed estimation
  • distributed particle filter
  • Likelihood consensus
  • target tracking
  • wireless sensor network

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