A SINS/CNS/GPS Online Calibration Method Based on Improved Sage-Husa Adaptive Filtering Algorithm

To solve the problem that the filtering accuracy of the online calibration decreases or even diverges due to time-varying noise and outlier value interference, a SINS/CNS/GPS high-precision integrated navigation online calibration method based on the improved Sage-Husa adaptive filtering algorithm is designed. In the proposed method, a 21-dimensional state space model and 9-dimensional measurement model are established. Furthermore, on the basis of the simplified Sage-Husa adaptive filtering algorithm, a smoothing estimator and an adaptive robust factor are introduced to suppress the influence on the filtering accuracy due to the abnormal disturbances in the measurement information, which improving the online calibration accuracy of integrated navigation. The simulation results show that the online calibration method based on the improved Sage-Husa adaptive filtering algorithm can better calibrate the error parameters, especially the calibration of the lever arm error for the east and up directions.