Fuzzy Adaptive Extended Kalman Filter
نویسندگان
چکیده
Kalman filtering is a method for estimating state variables of a dynamic systems recursively from noise-contaminated measurements. For systems with nonlinear dynamics, a natural extension of the Linear Kalman Filter (LKF), called Extended Kalman filter (EKF) is used. The Kalman filter represents one of the most popular estimation techniques for integrating signals from navigation systems, like Inertial Navigation System (INS) and Global Positioning System (GPS). However, a significant difficulty in designing a Kalman Filter (refers to both LKF and EKF) can often be traced to incomplete a priori information about R and Q matrices. It has been shown that incorrect a priori information can lead to practical divergence of the filter. The use of fuzzy-rule based adaptation scheme to cope with divergence problem is explored. The Fuzzy Logic Adaptive Controller (FLAC) was implemented in Integrated INS/GPS Navigation Systems to detect the uncertainties, adapt the Kalman Filter on-line and prevent divergence.
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