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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
- Authors
- Issue Date
- 2009-02
- Publisher
- Springer Verlag
- Citation
- International Journal of Control, Automation, and Systems (2009) 7(1):49-56
- 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
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