Fuzzy Adaptive Variational Bayesian Unscented Kalman Filter
نویسندگان
چکیده
We consider the problem of nonlinear filtering under the circumstance of unknown covariance statistic of the measurement noise. A novel adaptive unscented Kalman filter (UKF) integrating variational Bayesian methods and fuzzy logic techniques is proposed in this paper. It is called fuzzy adaptive variational Bayesian UKF (FAVBUKF). Firstly, the sufficient statistics of the measurement noise variances are estimated with a fixed-point iteration of the UKF in real time. Secondly, a fuzzy inference system (FIS) is introduced to adaptively adjust the measurement noise covariance based on a covariance matching technique. And last, the standard UKF with modified measurement noise covariance is carried out to obtain a state estimation. Simulation examples are used to evaluate the performance of this new algorithm comparing with UKF, and the results show that the proposed method is efficient and effective for potential practical applications
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