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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.
Acknowledgement
The authors would like to thank the associate editor and two reviewers for their constructive comments and recommendations. This work was supported in part by the National Natural Science Foundation of China under Grants 60372020 and 60672108, and in part by A Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant 200445.