Improved Adaptive Traceless Kalman Filtering Algorithm Based on SINS/GPS Combined Navigation

In the process of combined navigation between jetlink Strapdown Inertial Navigation System (SINS) and Global Positioning System (GPS), the measurement noise statistical characteristics are unknown and the measurement information is abnormal, which may cause filter instability or divergence. Therefore, in this paper, a Modified Adaptive Unscented Kalman Filter (MAUKF) method for improving the adaptive fading factor is proposed for measuring noise estimation. In this paper, we establish the loose combination model of SINS/GPS combined navigation system, improve it based on the UKF filter, and use the variable gradual elimination factor to enhance the adaptability of the new measurement noise change, so as to timely adjust the measurement matrix, so as to suppress the error and solve the problems such as filter instability and divergence. Finally, we evaluate the effectiveness of the proposed method by comparing the traditional KF algorithm, UKF algorithm, and MAUKF algorithms. The results show that the proposed method outperforms the conventional KF filters and UKF filters in both measurement noise estimation and navigation accuracy.