Journal of South China University of Technology (Natural Science Edition) ›› 2006, Vol. 34 ›› Issue (1): 57-61.

• Electronics, Communication & Automation Technology • Previous Articles     Next Articles

Adaptive Particle Filtration for State Estimation

Deng Xiao-long  Xie Jian-ying  Cuo Wei-zhong   

  1. 1.Dept.of Automation,Shanghai Jiaotong Univ.,Shanghai 200030,China;2.School of Mechanical Engineering,Shanghai Jiaotong Univ.,Shanghai 200030.China
  • Received:2005-01-05 Online:2006-01-25 Published:2006-01-25
  • Contact: 邓小龙(1972-)，男，博士生，主要从事最优估计、非线性滤波和粒子滤波方面的研究 E-mail:xl-deng@sjtu.edu.cn
  • About author:邓小龙(1972-)，男，博士生，主要从事最优估计、非线性滤波和粒子滤波方面的研究
  • Supported by:

    国家自然科学基金资助项目(50405017)

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Abstract:

This paper analyzes the keys for the performance of particle filter(PF)and presents a new adaptive PF algorithm.The algorithm adopts a new proposal distribution combining the unscented Kalman filter(UKF)with the adaptive strong tracking filter(STF).The new proposal distribution adopts UKF to produce the particles,in which each sigma point of every particle is updated by STF.Moreover,the added scaling factor can be adjusted on line to
make the algorithm adaptive.Simulated experiments of nonlinear state estimation are finally ca~ied out to confirm the validity of the improved PF algorithm.

Key words: particle filter, state estimation, unscented Kalman filter, adaptive filtering, strong tracking filter