A biologically inspired improvement strategy for particle filter: Ant colony optimization assisted particle filter

Junpei Zhong*, Yu Fai Fung, Mingjun Dai

*この研究の対応する著者

研究成果: Article査読

19 被引用数 (Scopus)

抄録

Particle Filter (PF) is a sophisticated model estimation technique based on simulation. Due to the natural limitations of PF, two problems, namely particle impoverishment and sample size dependency, frequently occur during the particles updating stage and these problems will limit the accuracy of the estimation results. In order to alleviate these problems, Ant Colony Optimization is incorporated into the generic PF before the updating stage. After executing the Ant Colony optimization, impoverished particle samples will be re-positioned and closer to their locally highest likelihood distribution function. Our experimental results show that the proposed algorithm can realize better tracking performance when comparing to the generic PF, the Extended Kalman Filter and other enhanced versions of PF.

本文言語English
ページ(範囲)519-526
ページ数8
ジャーナルInternational Journal of Control, Automation and Systems
8
3
DOI
出版ステータスPublished - 2010 6
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用

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