The robust residual-based adaptive estimation Kalman filter method for strap-down inertial and geomagnetic tightly integrated navigation system.

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.