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A Vessel’s Dead Reckoning Position Estimation by Using of Neural Networks

  • Victor V. Deryabin
  • Anatoly E. Sazonov
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 874)

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.

Keywords

Vessel’s trajectory Dead reckoning Neural network 

References

  1. 1.
    Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Sig. Syst. 2, 303–314 (1989)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Funahashi, K.: On the approximate realization of continuous mappings by neural networks. Neural Netw. 2(3), 183–192 (1989)CrossRefGoogle Scholar
  3. 3.
    Hornik, K., Stinchcombe, M., White, K.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)CrossRefGoogle Scholar
  4. 4.
    Ebada, A.: Intelligent techniques-based approach for ship maneuvering simulations and analysis (Artificial Neural Networks Application). Dr.-Ing. genehmigte Dissertation, Institute of Ship Technology und Transport Systems, University Duisburg-Essen, Germany (2007)Google Scholar
  5. 5.
    Waclawek, P.: A neural network to identify ship hydrodynamics coefficients. In: Chislett, M.S. (eds.) Marine Simulation and Ship Maneuverability: Proceedings of the International Conference, MARSIM 1996, Balkema, Rotterdam, pp. 509–514 (1996)Google Scholar
  6. 6.
    Xu, T., Liu, X., Yang, X.: A novel approach for ship trajectory online prediction using bp neural network algorithm. Adv. Inf. Sci. Serv. Sci. (AISS) 4(11), 271–277 (2012)Google Scholar
  7. 7.
    Deryabin, V.: Adaptive filtering algorithms in vessel’s position prediction problem (in Russian). Vestnik Gosudarstvennogo universiteta morskogo i rechnogo flota imeni admirala S.O. Makarova, vol. 1, pp. 12–19 (2014)Google Scholar
  8. 8.
    Haykin, S.: Neural Networks and Learning Machines, 3rd edn. Prentice Hall, New York (2009)Google Scholar
  9. 9.
    Faltinsen, O.: Sea Loads on Ships and Offshore Structures. Cambridge University Press, Cambridge (1999)Google Scholar
  10. 10.
    Deryabin, V.: Model ship traffic above the horizontal plane. Transp. Bus. Russia 6, 60–67 (2013). (in Russian)Google Scholar
  11. 11.
    Callan, R.: The Essence of Neural Networks. Prentice Hall Europe, London (1999)Google Scholar
  12. 12.
    Levenberg, K.: A method for the solution of certain problems in least squares. Q. Appl. Math. 2, 164–168 (1944)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. J. Soc. Ind. Appl. Math. 11(2), 431–441 (1963)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Foresee, F., Hagan, M.: Gauss-Newton approximation to Bayesian learning. In: Proceedings of the 1997 IEEE International Conference on Neural Networks, vol. 3, pp. 1930–1935. IEEE, New Jersey (1997)Google Scholar
  15. 15.
    Yu, H.: Advanced Learning Algorithms of Neural Networks. Ph.D. dissertation, Auburn, USA (2011)Google Scholar
  16. 16.
    Newman, J.: Marine Hydrodynamics. The MIT Press, Cambridge (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Admiral Makarov State University of Maritime and Inland ShippingSaint-PetersburgRussian Federation

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