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.

中文翻译：

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基于模糊神经网络扩展卡尔曼滤波的移动机器人定位。

针对移动机器人的定位问题，本文提出了一种新颖的方法来提高扩展卡尔曼滤波器（EKF）的性能。采用模糊逻辑系统连续调整滤波器的噪声协方差矩阵。实施神经网络来调节模糊规则的前序部分和后继部分的隶属函数。目的是在固定或错误确定噪声协方差矩阵时获得精度并避免EKF的发散。进行了仿真和实验。结果表明，所提出的滤波器在定位移动机器人方面比EKF更好。