中国邮电高校学报(英文) ›› 2012, Vol. 19 ›› Issue (2): 100-106.doi: 10.1016/S1005-8885(11)60253-7

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Improved particle filter based on fine resampling algorithm

曹蓓1,马彩文1,刘镇弢2   

  1. 1. 中国科学院西安光学精密机械研究所
    2. 西安邮电学院
  • 收稿日期:2011-08-16 修回日期:2011-12-23 出版日期:2012-04-30 发布日期:2012-04-17
  • 通讯作者: 曹蓓 E-mail:bbflyer@126.com
  • 基金资助:

    This work was supported by the High-Tech Research and Development Program of China (2008AA7080304).

Improved particle filter based on fine resampling algorithm

  • Received:2011-08-16 Revised:2011-12-23 Online:2012-04-30 Published:2012-04-17
  • Supported by:

    This work was supported by the High-Tech Research and Development Program of China (2008AA7080304).

摘要:

In order to solve particle degeneracy phenomenon and simultaneously avoid sample impoverishment, this paper proposed an improved particle filter based on fine resampling algorithm for general case, called as particle filter with fine resampling (PF-FR). By introducing distance-comparing process and generating new particle based on optimized combination scheme, PF-FR filter performs better than generic sampling importance resampling particle filter (PF-SIR) both in terms of effectiveness and diversity of the particle system, hence, evidently improving estimation accuracy of the state in the nonlinear/non-Gaussian models. Simulations indicate that the proposed PF-FR algorithm can maintain the diversity of particles and thus achieve the same estimation accuracy with less number of particles. Consequently, PF-FR filter is a competitive choice in the applications of nonlinear state estimation.

关键词:

particle filter, fine resampling, particle degeneracy, sample impoverishment, optimized combination

Abstract:

In order to solve particle degeneracy phenomenon and simultaneously avoid sample impoverishment, this paper proposed an improved particle filter based on fine resampling algorithm for general case, called as particle filter with fine resampling (PF-FR). By introducing distance-comparing process and generating new particle based on optimized combination scheme, PF-FR filter performs better than generic sampling importance resampling particle filter (PF-SIR) both in terms of effectiveness and diversity of the particle system, hence, evidently improving estimation accuracy of the state in the nonlinear/non-Gaussian models. Simulations indicate that the proposed PF-FR algorithm can maintain the diversity of particles and thus achieve the same estimation accuracy with less number of particles. Consequently, PF-FR filter is a competitive choice in the applications of nonlinear state estimation.

Key words:

particle filter, fine resampling, particle degeneracy, sample impoverishment, optimized combination

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