Distributed Estimation Using Particles Intersection

Or Tslil, Ori Aharon, Avishy Carmi

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

7 Scopus citations

Abstract

A technique is presented for combining arbitrary empirical probability density estimates whose interdependencies are unspecified. The underlying estimates may be, for example, the particle approximations of a pair of particle filters. In this respect, our approach, named hereafter particles intersection, provides a way to obtain a new particle approximation, which is better in a precise information-theoretic sense than that of any of the particle filters alone. Particles intersection is applicable in networks with potentially many particle filters. We demonstrate both theoretically and through numerical simulations that depending on the communication topology this technique leads to consensus in the underlying network where all particle filters agree on their estimates. The viability of the proposed approach is demonstrated through examples in which it is applied for multiple object tracking and distributed estimation in networks.

Original languageEnglish
Title of host publication2018 21st International Conference on Information Fusion, FUSION 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1653-1660
Number of pages8
ISBN (Print)9780996452762
DOIs
StatePublished - 5 Sep 2018
Event21st International Conference on Information Fusion, FUSION 2018 - Cambridge, United Kingdom
Duration: 10 Jul 201813 Jul 2018

Publication series

Name2018 21st International Conference on Information Fusion, FUSION 2018

Conference

Conference21st International Conference on Information Fusion, FUSION 2018
Country/TerritoryUnited Kingdom
CityCambridge
Period10/07/1813/07/18

Keywords

  • Chernoff-information
  • Distributed estimation
  • Fault-tolerant network
  • Information fusion
  • Kullback-Leibler divergence
  • Particle filter

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Statistics, Probability and Uncertainty
  • Instrumentation

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