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The Stability Analysis of the Adaptive Fading Extended Kalman Filter Using the Innovation Covariance

Cited 45 time in Web of Science Cited 51 time in Scopus
Issue Date
2009-02
Publisher
Springer Verlag
Citation
International Journal of Control, Automation, and Systems (2009) 7(1):49-56
Keywords
Adaptive Kalman filterforgetting factornonlinear filterstability analysis
Abstract
The well-known conventional Kalman filter gives the optimal solution but to do so, it
requires an accurate system model and exact stochastic information. However, in a number of practical
situations, the system model and the stochastic information are incomplete. The Kalman filter with
incomplete information may be degraded or even diverged. To solve this problem, a new adaptive
fading filter using a forgetting factor has recently been proposed by Kim and co-authors. This paper
analyzes the stability of the adaptive fading extended Kalman filter (AFEKF), which is a nonlinear
filter form of the adaptive fading filter. The stability analysis of the AFEKF is based on the analysis
result of Reif and co-authors for the EKF. From the analysis results, this paper shows the upper
bounded condition of the error covariance for the filter stability and the bounded value of the
estimation error.
ISSN
1598-6446
Language
English
URI
https://hdl.handle.net/10371/69241
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)Journal Papers (저널논문_기계항공공학부)
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