When noise statistical characteristics of the system are unknown and there are outliers in the measurement information, the filtering accuracy of the strap-down inertial navigation system/geomagnetic navigation system (SINS/GNS) tightly integrated navigation system would decrease, and the filtering may diverge in severe cases. To solve this problem, a robust residual-based adaptive estimation Kalman filter (RRAEKF) method is proposed. In the RRAEKF method, the covariance matching technique is employed to detect whether the system is abnormal or not. When the system is judged to be abnormal, a weighted factor is constructed to identify and weight the wild value in the measurement information, eliminating the influence of the outliers on the filtering accuracy. To further improve the filtering accuracy of the integrated navigation system, a contraction factor is introduced to adaptively adjust the gain matrix of the filter algorithm, obtaining the optimal estimate of the state vector and covariance matrix. Simulation results demonstrate that compared with the standard extended Kalman filter method and residual-based adaptive estimation method, the space position errors of the SINS/GNS tightly integrated navigation system based on the proposed method are improved by 63.37% and 56.93%, respectively, in the case of time-varying noise and the presence of outliers.

中文翻译：

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捷联惯性地磁紧密结合导航系统的鲁棒残差自适应估计卡尔曼滤波方法

当系统噪声统计特性未知且测量信息存在异常值时，捷联惯导系统/地磁导航系统（SINS/GNS）紧密集成导航系统的滤波精度会降低，滤波可能发散在严重的情况下。为了解决这个问题，提出了一种基于鲁棒的基于残差的自适应估计卡尔曼滤波器（RRAEKF）方法。在 RRAEKF 方法中，采用协方差匹配技术来检测系统是否异常。当系统判断为异常时，构造加权因子对测量信息中的野值进行识别和加权，消除异常值对过滤精度的影响。为进一步提高组合导航系统的滤波精度，引入收缩因子自适应调整滤波器算法的增益矩阵，得到状态向量和协方差矩阵的最优估计。仿真结果表明，与标准扩展卡尔曼滤波方法和基于残差的自适应估计方法相比，基于该方法的SINS/GNS紧密结合导航系统的空间位置误差分别提高了63.37%和56.93%。时变噪声的情况和异常值的存在。