Adaptive Kalman Filter Based on Evolutionary Algorithm and Fuzzy Interference System
نویسندگان
چکیده
This is a survey paper.The performance of the Kalman filter (KF), which is Algoritstandard as an outstanding implementation for dynamic system state estimation, greatly depends on its parameter R, called the measurement noise covariance matrix. . However, it’s difficult to obtain the accurate value of R before the filter starts, and the value of R is possible to change with the measurement environment once the filter is working. To solve this difficulty, a new parameter adaptive Kalman filter is proposed in this paper. In this new Kalman filter, the initial value of R is offline determined by Evolutionary hm (EA), and the value of R determined by EA is online updated by Fuzzy Inference System (FIS). The new adaptive Kalman filter proposed in this paper (HYdGeFuzKF) has a stronger adaptableness to time-varying measurement noises than regular Kalman filter (RegularKF). Key word; -MSE, EA, FIS, KF. __________________________________________________*****_________________________________________________
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