Abstract: To meet the requirement of accurate localization of mobile robot，a self-localization algorithm based on Fuzzy Kalman Filtering（FKF） is proposed.This paper employs an Extended Kalman Filtering（EKF） algorithm to fuse odometer readings and sonar data.To address the problem of performance degradation and even divergence of EKF caused by poor estimates of the observation noise，a fuzzy logic based adaption tuning algorithm，which can on-line adjust the observation noise covariance by monitoring the coincidence of the actual covariance with the theoretical covariance of residuals，is presented.Simulation results demonstrate that the proposed scheme achieves better localization precision and robustness than traditional EKF.

Key words: extended Kalman filtering, fuzzy logic, sonar, localization, residuals

摘要： 为了满足移动机器人准确定位的要求，提出了一种基于模糊卡尔曼滤波（FKF）的自定位算法。利用扩展卡尔曼滤波（EKF）算法融合里程计和声纳的观测数据，并针对EKF中观测噪声方差估计不准确导致滤波器性能下降甚至发散的问题，提出了基于模糊逻辑的自适应调节算法。该算法通过监测新息实际方差和理论方差的一致程度，在线调整观测噪声的方差值。仿真结果表明，此方法较EKF提高了系统的定位精度和鲁棒性。

关键词: 扩展卡尔曼滤波, 模糊逻辑, 声纳, 定位, 新息