基于模糊自适应卡尔曼滤波的移动机器人定位方法*

针对移动机器人定位过程中噪声统计特性不确定的问题，提出一种模糊自适应扩展卡尔曼滤波定位方法。利用模糊理论和协方差匹配技术对扩展卡尔曼滤波算法中的观测噪声协方差R进行自适应调整，实现定位算法性能的在线改进；同时采用传感器故障诊断与修复算法来监测传感器的工作状态，提高定位算法的鲁棒性。将该方法用于观测噪声统计特性未知情况下的移动机器人定位。实验结果表明，该方法可以有效地降低观测噪声先验信息不确定的影响，提高机器人定位的精度。

Localization of mobile robot based on fuzzy-adapted Kalman filtering

In order to resolve the problem of mobile robot localization with unknown noise characteristics,this paper proposed a mobile robot localization method based on fuzzy-adapted extended Kalman filtering.Combined fuzzy logic and covariance-matching technique together to adjust the measurement noise covariance R and on-line improve the performance of the localization algorithm.Moreover,it used a sensor fault diagnostic and recovery algorithm to monitor the sensors' states and improved the algorithm's robustness....