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Extended Kalman Filter (EKF) has been a popular approach to localization amobile robot. However, the performance of the EKF and the quality of theestimation depends on the correct a priori knowledge of process and measurementnoise covariance matrices (Qk and Rk, respectively). Imprecise knowledge ofthese statistics can cause significant degradation in performance. This paperproposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering(ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts toestimate the elements of Qk and Rk matrices of the EKF algorithm, at eachsampling instant when measurement update step is carried out. The ANFISsupervises the performance of the EKF with the aim of reducing the mismatchbetween the theoretical and actual covariance of the innovation sequences. Thefree parameters of ANFIS are trained using the steepest gradient descent (SD)to minimize the differences of the actual value of the covariance of theresidual with its theoretical value as much possible. The simulation resultsshow the effectiveness of the proposed algorithm.

Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization