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Adaptive systems of particle filters

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

5 Scopus citations

Abstract

We study systems of particle filters that track targets based on data acquired from a network of sensors. We build on our previous concept of symbiotic particle filtering and propose a system of particle filters, where each one of them explores a state space of minimal dimension. The number of particle filters in the system varies in that more particle filters may be added to the system, some may be removed, and some may be merged or split with time. The decision for changing the number of filters in the system depends on the estimated states of the targets that are being tracked and the locations of the sensors that sense them. We demonstrate the performance of the system by computer simulations and compare it with that of a standard particle filter.

Original languageEnglish
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages59-63
Number of pages5
DOIs
StatePublished - 2010
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

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

Conference

Conference44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Country/TerritoryUnited States
CityPacific Grove, CA
Period11/7/1011/10/10

Keywords

  • dynamic systems
  • filtering
  • particle filters
  • recursive estimation

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