基于模糊自适应卡尔曼滤波的INS/GPS组合导航系统算法研究

Research on GPS/INS Integrated navigation System Based on Fuzzy Adaptive Kalman Filtering

针对车载组合导航系统量测噪声统计特性随实际工作条件的不同而变化的特点,提出了一种基于模糊自适应卡尔曼滤波的车载INS/GPS组合导航算法。该方法通过监视理论残差与实际残差的比值是否在一附近,应用模糊推理系统不断的调整量测噪声协方差阵的加权,对卡尔曼滤波的量测噪声协方差阵进行递推在线修正,使其逐渐逼近真实噪声水平,从而使滤波器执行最优估计,提高导航系统的精度。对车载组合导航系统的仿真结果表明,这种算法对时变的量测噪声具有较强的自适应性,进而精度比常规卡尔曼滤波也大为提高,是一种可行的车载组合导航算法。

This paper presents a novel vehicle GPS/INS integrated navigation algorithm based on Fuzzy Adaptive Kalman Filtering. This method is mainly used in vehicle GPS/INS integrated navigation system to deal with time varied statistic of measurement noise in different working conditions. By monitoring if the ratio between filter residual and actual residual is near 1, this algorithm modifies recursively the measurement noise covariance of Kalman Filtering online using the Fuzzy Inference System （FIS） to make the covariance close to real measurement covariance gradually. Accordingly the kalman filter performs optimally and the accuracy of the navigation system is improved. Simulations in INS/GPS integrated navigation system demonstrate that the Fuzzy Adaptive Kalman Filtering is adaptive to time varied measurement noise and gives the better results than the regular Kalman Filtering.