Fuzzy Kalman filtering

Authors:
Guanrong Chen;Qingxian Xie;Leang S. Shieh
Affiliations:
Department of Electrical and Computer Engineering University of Houston, Houston, TX 77204-4793, USA;Department of Electrical and Computer Engineering University of Houston, Houston, TX 77204-4793, USA;Department of Electrical and Computer Engineering University of Houston, Houston, TX 77204-4793, USA
Venue:
Information Sciences: an International Journal
Year:
1998

Citing 2
Cited 1

Kalman filtering with real-time applications

Kalman filtering with real-time applications
Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)

Methods and Applications of Interval Analysis (SIAM Studies in Applied and Numerical Mathematics) (Siam Studies in Applied Mathematics, 2.)

An improved fuzzy Kalman filter for state estimation of non-linear systems

International Journal of Systems Science

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Abstract

The classical Kalman filtering (KF) algorithm has recently been extended to interval linear systems with interval parameters under the same statistical assumptions on noise, where the new algorithm is called Interval Kalman Filtering (IKF) scheme. The IKF algorithm has the same structure, and preserves the same optimality, as the classical KF scheme but provides interval-valued estimates. If the interval system has confidence description about the distribution of its interval values, we can further incorporate the IKF scheme with fuzzy logic inference, so as to develop a new filtering algorithm, called Fuzzy Kalman Filtering (FKF) algorithm. This algorithm preserves the same recursive mechanism of the KF and IKF, but produces a scalar-valued (rather than an interval-valued) estimate at each iteration of the filtering process. To compare the FKF to the IKF, computer simulation is included, which shows that the FKF is also robust against system parameter variations.