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- W2969583147 abstract "Underwater high-precision positioning is the basis of safe sailing and smooth operation for underwater autonomous vehicle (AUV). With the continuous expansion of marine surveys, the requirements for navigation accuracy of AUVs are also increasing. As the unscented Kalman filter (UKF)-based terrain matching method can meet the high-precision positioning demand of underwater navigation, it becomes one of the hot research content in recent years. However, due to the insufficient robustness of UKF algorithm, under the condition of non-additive state noise, the initial distribution of Sigma points is prone to be unreasonable, which will reduce filter accuracy and even cause the recursive result divergence. In order to improve the accuracy and robustness of the underwater terrain matching algorithm, this paper introduces the method of nonlinear filtering state expansion. The system noise and measurement noise information are added into the system state matrix and the variance matrix, and obtains the state expansion UKF-based terrain matching algorithm. Since the noise and the estimation parameters are both participating in the filtering operation, the inaccurate prediction influence of initial noise can be reduced, and finally improves the accuracy and applicability of the underwater terrain matching algorithm." @default.
- W2969583147 created "2019-08-29" @default.
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- W2969583147 date "2019-06-01" @default.
- W2969583147 modified "2023-09-27" @default.
- W2969583147 title "Research on Matching Accuracy of Underwater Terrain Matching Algorithm Based on State Augmented Unscented Kalman Filter" @default.
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- W2969583147 doi "https://doi.org/10.1109/icras.2019.8809003" @default.
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