An iterated fuzzy extended Kalman filter for nonlinear systems

作者：Yang Xiaojun^*; Zou Hongxing; Zhou Zhijie; Ding Jianjiang; Liu Daizhi

来源：International Journal of Systems Science, 2010, 41(6): 717-726.

DOI：10.1080/00207720903199606

• fuzzy uncertainty
• iterated extended Kalman filter
• nonlinear systems
• maximum a posteriori estimation
• Kalman filtering
• fuzzy logic

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.

• 出版日期2010
• 单位清华大学; 中国人民解放军空军预警学院