A detailed analysis of the ant colony optimization enhanced particle filters

Junpei Zhong, Yu Fai Fung

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Particle filters, as a kind of non-linear/non-Gaussian estimation method, are suffered from two problems when applied to cases with large states dimensions, namely particle impoverishment and sample size dependency. Previous papers from the authors have proposed a novel particle filtering algorithm that incorporates Ant Colony Optimization (PF ACO), to alleviate effect induced by these problems. In this paper, we will provide a theoretical foundation of this new algorithm. A theorem that validates the PF ACO introduces a smaller Kullback-Leibler Divergence between the proposal distribution and the optimal one when comparing to those produced by the generic PF is discussed.

Original languageEnglish
Title of host publicationElectrical Engineering and Control - Selected Papers from the 2011 International Conference on Electric and Electronics, EEIC 2011
Pages641-648
Number of pages8
Volume98 LNEE
EditionVOL. 2
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event2011 International Conference on Electric and Electronics, EEIC 2011 - Nanchang, China
Duration: 2011 Jun 202011 Jun 22

Publication series

NameLecture Notes in Electrical Engineering
NumberVOL. 2
Volume98 LNEE
ISSN (Print)18761100
ISSN (Electronic)18761119

Other

Other2011 International Conference on Electric and Electronics, EEIC 2011
CountryChina
CityNanchang
Period11/6/2011/6/22

Keywords

  • Ant Colony Optimization
  • Combinatorial Optimization
  • Metaheuristic Methods
  • Nonlinear Estimation
  • Particle Filters

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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