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.

翻译：本文建议采取新颖办法,改进扩大的卡尔曼过滤器(Kalman过滤器)在移动机器人定位问题上的性能。采用了模糊逻辑系统,以持续调整过滤器的噪音共变矩阵。实施了神经网络,以规范先前的和随后的模糊规则部分的会籍功能。目的是在噪音共变矩阵固定或错误确定时,提高 EKF的准确性,避免其差异。进行了模拟和实验。结果显示,拟议的过滤器在移动机器人本地化方面比EKF好。