Case study and proofs of ant colony optimisation improved particle filter algorithm

J. Zhong, Y. F. Fung

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Particle filters (PF), as a kind of non-linear/non-Gaussian estimation method, are suffering from two problems in large-dimensional cases, namely particle impoverishment and sample size dependency. Previous studies from the authors have proposed a novel PF algorithm that incorporates ant colony optimisation (PF ACO), to alleviate these problems. In this paper the authors will provide a theoretical foundation of this new algorithm; two theorems are introduced to validate that the PF ACO introduces smaller Kullback-Leibler divergence (K-L divergence) between the proposal distribution and the optimal one compared to those produced by the generic PF. In addition, with the same threshold level, the PF ACO has a higher probability than the generic PF to achieve a certain K-L divergence. A mobile robot localisation experiment is applied to examine the performance between various PF schemes.

Original languageEnglish
Pages (from-to)689-697
Number of pages9
JournalIET Control Theory and Applications
Volume6
Issue number5
DOIs
Publication statusPublished - 2012 Mar 15
Externally publishedYes

Fingerprint

Ant colony optimization
Particle Filter
Mobile robots
Kullback-Leibler Divergence
Experiments
Particle Size
Mobile Robot
Sample Size
Theorem

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Control and Optimization

Cite this

Case study and proofs of ant colony optimisation improved particle filter algorithm. / Zhong, J.; Fung, Y. F.

In: IET Control Theory and Applications, Vol. 6, No. 5, 15.03.2012, p. 689-697.

Research output: Contribution to journalArticle

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