Abstract
The article aims to prove the feasibility of implementation of a neural network based approach for a vessel’s dead reckoning positioning. For this purpose, four dead reckoning algorithms have been developed on the basis of neural networks. Each of these algorithms is characterized by a certain set of navigational equipment used. Training samples are prepared with the use of differential and/or kinematic equations, depending on navigational equipment being used. Testing of the algorithms has been conducted with a vessel’s motion modelling, based on solving corresponding differential equations. The parameters of five vessels of significantly different types were used. A vessel’s sailing during four hours under wind and wave influence is named a navigational situation. It has been considered 300 such navigational situations. Each situation belongs to one of three classes, characterized by certain time behaviour of control actions and external influences. The results of the testing have shown that neural network based dead reckoning algorithms may be used for calculation of a vessel’s trajectory.
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Deryabin, V.V., Sazonov, A.E. (2019). A Vessel’s Dead Reckoning Position Estimation by Using of Neural Networks. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Sukhanov, A. (eds) Proceedings of the Third International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’18). IITI'18 2018. Advances in Intelligent Systems and Computing, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-01818-4_49
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DOI: https://doi.org/10.1007/978-3-030-01818-4_49
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