An iterated fuzzy extended Kalman filter for nonlinear systems

  • Authors:

  • Xiaojun Yang;Hongxing Zou;Zhijie Zhou;Jianjiang Ding;Daizhi Liu

  • Affiliations:

  • Research Institute of Hi-Tech, Xi'an, China,Department of Automation, Tsinghua University, Beijing 100084, China;Department of Automation, Tsinghua University, Beijing 100084, China;Research Institute of Hi-Tech, Xi'an, China,Department of Automation, Tsinghua University, Beijing 100084, China;Key Laboratory of Radar Academy, Wuhan, China;Research Institute of Hi-Tech, Xi'an, China

  • Venue:
  • International Journal of Systems Science
  • Year:
  • 2010

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Abstract

The fuzzy extended Kalman filter (FEKF) for state estimation can be used to deal with fuzzy uncertainty effectively. However, the linearisation processing of the FEKF introduces truncation error, which degrades the estimation precision. In order to reduce the error, a new iterated fuzzy extended Kalman filter (IFEKF), based on the FEKF and the maximum a posteriori estimation, is proposed in this article. Compared with the FEKF, the proposed algorithm can be used not only to deal with the fuzzy uncertainty, but also to reduce the truncation error and to estimate the states more accurately. With an algebraic example and a passive location simulation, it is shown that the IFEKF has better estimation precision than that of the FEKF.