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The Stability Analysis of the Adaptive Fading Extended Kalman Filter
Cited 45 time in
Web of Science
Cited 51 time in Scopus
- Authors
- Issue Date
- 2007-10
- 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
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