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The Stability Analysis of the Adaptive Fading Extended Kalman Filter

Cited 35 time in Web of Science Cited 42 time in Scopus
Authors
Kim, Kwang Hoon; Lee, Jang Gyu; Park, Chan Gook; Jee, Gyu In
Issue Date
2007-10
Publisher
The Institute of Control, Robotics and Systems Engineers
Citation
16th IEEE International Conference on Control Applications, Part of IEEE multi-conference on systems and control Singapore, 1-3 October 2007
Abstract
The well-known conventional Kalman filter gives
the optimal solution but requires an accurate system model and
exact stochastic information. Thus, the Kalman filter with
incomplete information may be degraded or even diverged. In a
number of practical situations, the system model and the
stochastic information are incomplete. To solve this problem, a
new adaptive fading Kalman filter (AFKF) using the forgetting
factor has recently been proposed. This paper extends the
AFKF to nonlinear system models to obtain an adaptive fading
extended Kalman filter (AFEKF). The forgetting factor is
generated from the ratio between the calculated innovation
covariance and the estimated innovation covariance. Based on
the analysis result of Reif for the EKF, the stability of the
AFEKF is also analyzed.
Language
English
URI
http://hdl.handle.net/10371/26242
DOI
https://doi.org/10.1007/s12555-009-0107-x
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Mechanical Aerospace Engineering (기계항공공학부)Others_기계항공공학부
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