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

Junpei Zhong, Yu Fai Fung, Mingjun Dai

研究成果: Article

17 引用 (Scopus)

抜粋

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.

元の言語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
発行部数3
DOI
出版物ステータスPublished - 2010 6
外部発表Yes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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