MIMU/BDS Integrated Navigation Technology Based on Smooth Variable Structure-Adaptive Kalman Filter

In order to improve the accuracy of MIMU/BDS integrated navigation with uncertain models and large disturbances, a smoothing variable structure-Kalman combined filter information fusion method is proposed. The coordinate transformation method is introduced, and the state space equation and the observation equation of the integrated navigation system are established. In order to prevent the algorithm divergence caused by the old observation data, the adaptive fading factor which varies with the residual error is incorporated into the Kalman filter algorithm, and the adaptive Kalman filter algorithm is constructed. Combining the accuracy advantage of Kalman filter with the robustness advantage of smooth variable structure filter, a smooth variable structure-Kalman combined filter algorithm is constructed. It is verified by simulation that location and speed fusion error of combined algorithm are smaller than Kalman filter and adaptive Kalman filter under the condition of uncertain model and large disturbances. The experiment shows that location fusion accuracy and speed fusion accuracy are also high under the condition of satellite occlusion, which means the accurate navigation with insufficient number of satellites is achieved by utilizing the combined filter algorithm.

Abstract licence: CC BY

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