Integrated navigation and location algorithm based on iterated split CIF

For the integrated navigation system GNSS/SINS in the process of data fusion, the traditional filtering algorithm does not consider the correlation between the two system and the poor robustness when measurement outliers occur, this paper proposes an iterated split covariance intersection filter algorithm to fuse the data of the two systems. It combines the Split CIF and the Gauss-Newton iteration and separate state covariance matrix into independent parts and dependent parts, and adjusts the posterior state estimation by calculating the Kalman filter gain iteratively during the measurement update process to reduce the error caused by outliers. The simulation show that the Iterated Split CIF based integrated navigation system has higher accuracy and better robustness. Compared with Split CIF and Kalman filter, the east velocity error is reduced by 30% and 35% respectively, and the latitude error is reduced by 22% and 30% respectively. In addition, the position accuracy still remains at a high level when outliers occur, so the algorithm has good robustness.