Particle algorithms for filtering in high dimensional state spaces: A case study in group object tracking

Lyudmila Mihaylova, Avishy Carmi

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

6 Scopus citations

Abstract

We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex structured scenarios. The big dimensionality attributed to the group tracking problem poses a major difficulty to particle filters (PFs). This in turn has motivated researchers to devise many alternatives and variants over the past decade. In this work, we corroborate and extend a single promising direction for alleviating the dimensionality problem. Our derived scheme endows a recently introduced Markov chain Monte Carlo (MCMC) PF algorithm with an improved proposal distribution. We demonstrate the performance of our approach using a nonlinear system with up to 40 states.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages5932-5935
Number of pages4
DOIs
StatePublished - 18 Aug 2011
Externally publishedYes
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 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
Period22/05/1127/05/11

Keywords

  • Markov chain Monte Carlo methods (MCMC)
  • group object tracking
  • high dimensional systems
  • nonlinear estimation
  • sequential Monte Carlo methods

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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