Robust Alignment Base on IKF for SINS/DVL Integrated Navigation System

SINS is the main navigation means for AUV, which is mainly composed of SINS, DVL and acoustic transponder. Due to the advantages of strong autonomy, high accuracy and strong stabilization of velocity measurement with time, DVL can realize all-time and all-weather autonomous navigation when being integrated with SINS. In SINS/ DVL integrated autonomous navigation underwater, the measured data of DVL are easily polluted by non-Gaussian noise such as outliers. The priori information of state quantity is usually unknown and the value is also hard to estimate accuracy enough, which will lead performance of alignment to decline or even divergent. To solve the above problems, a RIKF alignment method based on Mahalanobis distance (MD) with an expansion factor weighting is proposed in this paper. The effectiveness of the proposed RIKF is verified by the data measured from the SINS/DVL on board. Initial alignment experiments are carried out by traditional KF, IKF and RIKF algorithm respectively under the conditions of DVL being polluted by outliers noise. Compared with KF and IKF, the results show that the proposed RIKF algorithm have a better performance with the measured data polluted by outliers, and RIKF algorithm is feasible and effective to be applied to the dynamic initial alignment of SINS/DVL integrated system.