The auxiliary iterated extended Kalman particle filter

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• 作者：Yanhui Xi ; Hui Peng ; Genshiro Kitagawa ; Xiaohong Chen
• 关键词：Particle filter ; Iterated extended Kalman filter ; Auxiliary particle filter ; Importance density function
• 刊名：Optimization and Engineering
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：16
• 期：2
• 页码：387-407
• 全文大小：2,218 KB
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• 作者单位：Yanhui Xi (1) (2)
  Hui Peng (1)
  Genshiro Kitagawa (3)
  Xiaohong Chen (4) (5)
  
  1. School of Information Science & Engineering, Central South University, Changsha, 410083, People鈥檚 Republic of China
  2. Hunan Province Higher Education Key Laboratory of Power System Safety Operation and Control, Changsha University of Science and Technology, Changsha, 410004, Hunan, People鈥檚 Republic of China
  3. Transdisciplinary Research Integration Center, Research Organization of Information and Systems, Hulic Kamiyacho Bldg. 2F, 4-3-13 Toranomon, Minato-ku, Tokyo, 105-0001, Japan
  4. School of Business, Central South University, Changsha, 410083, Hunan, China
  5. Collaborative Innovation Center of Resource-conserving & Environment-friendly Society and Ecological Civilization, Changsha, 410083, Hunan, China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Optimization
  Engineering, general
  Systems Theory and Control
  Environmental Management
  Agriculture
  Operation Research and Decision Theory
  
• 出版者：Springer Netherlands
• ISSN：1573-2924

文摘

This paper proposes a novel particle filter, namely, the auxiliary iterated extended Kalman particle filter (AIEKPF). To generate the importance density, based on the auxiliary particle filtering (APF) technique the proposed filter uses the iterated extended Kalman filter (IEKF) to integrate the latest measurements into state transition density. This new filter can match the posterior density well, because of the robustness of the APF and the importance density generated by the IEKF. The performance of the presented particle filter is evaluated by two different estimation problems with the noise of Gaussian distribution and Gamma distribution, respectively. The experimental results illustrate that the AIEKPF is superior to the extended Kalman filter and some existing particle filters, such as the standard particle filter (PF), the extended Kalman particle filter, the unscented Kalman particle filter (UKPF) and the auxiliary extended Kalman particle filter, where the number of particles is relatively small, such as 200 and 1,000. However, with an increase of particles, the superiority of the proposed method may decline compared with the PF and APF as showed in the experiments. Also, the AIEKPF has less running time than the UKPF under the same conditions, and from the viewpoint of the average effective sample sizes, it is clear that the AIEKPF has the slightest degeneracy in all filters presented in the experiments.