Abstract
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.
Highlights
autonomous underwater vehicle (AUV) integrates a variety of advanced technologies, such as underwater communication, multi-sensor fusion, data processing, etc., and has been widely used in the fields of mine clearance, oceanographic survey, and ocean sounding data acquisition [1]
Yang proposed an adaptive H-Infinity cubature Kalman filter based on the Sage–Husa estimator (AH∞CKF), which combines H∞CKF and the
In order to evaluate the effectiveness of the novel AF-H∞CKF in terms of an inaccurate model, the process noise suddenly becomes unknown during the time period (300 s, 400 s)
Summary
AUV integrates a variety of advanced technologies, such as underwater communication, multi-sensor fusion, data processing, etc., and has been widely used in the fields of mine clearance, oceanographic survey, and ocean sounding data acquisition [1]. Kalman filter (KF), which is widely used in navigation systems, is a practical real-time optimal estimation method [7]. Yang proposed an adaptive H-Infinity cubature Kalman filter based on the Sage–Husa estimator (AH∞CKF), which combines H∞CKF and the. A fading factor adaptive algorithm based on innovation orthogonality [20] can suppress the effects of model uncertainty without estimating the process and measurement errors. H∞ theory can ensure the robustness in the case of extreme error, and the adaptive factor can keep high accuracy when the model is uncertain The rest of this manuscript is as follows. P0 , mentioned above, represents the initial predicted error covariance that is presented on the basis of the actual conditions, and it denotes the closeness between the initial estimate â0 and actual initial state vector a0. A fading factor adaptive filter is introduced to be combined with H∞CKF
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