  This paper proposes a novel approach to improve the performance of the
extended Kalman filter (EKF) for the problem of mobile robot localization. A
fuzzy logic system is employed to continuous-ly adjust the noise covariance
matrices of the filter. A neural network is implemented to regulate the
membership functions of the antecedent and consequent parts of the fuzzy rules.
The aim is to gain the accuracy and avoid the divergence of the EKF when the
noise covariance matrices are fixed or wrongly determined. Simulations and
experiments have been conducted. The results show that the proposed filter is
better than the EKF in localizing the mobile robot.
