摘要：In the navigation of an autonomous underwater vehicle (AUV), the positioning accuracy and stability of the navigation system will decrease due to uncertainties such as mobility, inaccuracy of a priori process noise characteristic, and simplification of a dynamic model. In order to solve the above problems, a new, adaptive factor-based H∞ cubature Kalman filter based on a fading factor (AF-H∞CKF) is proposed in this paper. On the one hand, the H∞ game theory provides AF-H∞CKF good robustness in the worst case; on the other hand, the fading factor makes the innovation orthogonal and inflates the predicted error covariance and the Kalman gain, which avoids a decrease in estimation precision in the case of model uncertainty. The simulation and experiment results show that the AF-H∞CKF filter can deal with AUV navigation better than other existing algorithms in the presence of outliers and model uncertainty, which confirms its effectiveness and superiority.
