A Method Based on Deep Learning for Assisting SINS/DVL Integrated Navigation
The navigation and positioning accuracy of an Autonomous Underwater Vehicle(AUV)affects the efficiency of the AUV to a certain extent,and since GNSS cannot be used underwater,the integrated navigation system of Strapdown Inertial Navigation System/Doppler Velocity Log(SINS/DVL)has been widely favored.However,DVL will fail in some cases,and if DVL is isolated directly,the system will become a pure inertial navigation system,which seriously affects the accuracy of navigation and positioning.In order to cope with the situation that DVL is missing in some beams,a DLinear-Informer assisted integrated navigation algorithm is proposed.Through DLinear's unique decomposition of the original input data,the algorithm enhances the extraction and learning of the nonlinear information of AUV and improves the velocity prediction accuracy.The experimental results show that the proposed algorithm can accurately predict the velocity of the lost beam during the DVL failure,reduce the position error of the integrated navigation,and improve the robustness and positioning accuracy of the system.
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