Prediction of Ship Movement Using a Kalman Filter Algorithm

Abstract:

The Automatic identification system is a system used globally by ships that allows the easy identification of other surface contacts (also equipped with an automatic identification system equipment). The main objective of the present work is to validate experimentally the prediction of a ship's movement. A Kalman Filter algorithm, a recursive technique widely used in several scientific areas of study including navigation, is designed to estimate and predict the movement. In this work, a non-linear kinematic model is proposed to analyse and predict the movement of an automatic identification system contact, based on the data transmitted by it. The results evidence that the automatic identification system data can be used as input of a Kalman filter to get consistent and reliable information, an advantage to decision makers so they can act with adequate knowledge and in time.

• Periodical: Advances in Science and Technology (Volume 144)
• Pages: 93-100
• DOI: https://doi.org/10.4028/p-iPm9W5
• Online since: March 2024
• Authors: M. Filomena Teodoro*, Cardoso Pereira, Paulino Henriques, António Canas
• Keywords: AIS Data, Forecast, Kalman Filter, Ships Movement

References

[1] R. E. Kalman, A new approach to linear filtering and prediction problems, Trans. ASME Ser. D J. Basic Eng. 82(1) (1960) 35-45.

[2] R. E. Kalman, New methods in wiener filter theory, in: J. L. Bogdanof and F. Kozin, Frank (Eds.), Proc. of the First Symposium on Engineering Application of Random Function Theory and Probability, John Wiley & Sons, New York, 1963, p.270–388.

[3] A. C. Antoulas, Mathematical System Theory, The Influence of R. E. Kalman, Springer, Heidelberg, 1991.

[4] S. Julier, J. Uhlmann, A New Extension of the Kalman Filter to Nonlinear Systems. Multi Sensor Fusion, Tracking and Resource Management, in: II SPIE, Orlando, 1997, Vol. 3068, pp.182-193.

DOI: 10.1117/12.280797

[5] H. W. Sorenson, Kalman Filtering: Theory and Application, IEEE Press, New Jersey, 1985.

[6] R. G. Brown, Y. C. Hwang, Introduction to random signals and applied Kalman filtering: with MATLAB exercises, 4th ed., John Wiley & Sons, New Jersey, 2012.

[7] P. S. Maybeck, Stochastic Models, Estimation, and Control, vol. 1, Academic Press, New York, 1979, pp.1-16.

[8] G. Welch, G. Bishop, An introduction to Kalman filter, UNC-Chapel Hill, Chapel Hill, 2006.

[9] J. Bezručka, The use of a Kalman Filter in geosedy and navigation, Slovak Journal of Civil Engineering, 19(2) (2011) 8 – 15.

DOI: 10.2478/v10189-011-0007-y

[10] J. Duque, Desenho de Filtros de Kalman aplicados a sistemas de navegação de baixo custo, Portuguese Naval Academy- Universitary Military Institute, Almada, 2018.

[11] J. Duque, V. da Conceição, M. F. Teodoro, Filtering Applied to Low-Cost Navigation Systems: A Preliminary Approach, in: O. Gervasi et. al, Computational Science and Its Applications – ICCSA 2018, Springer, Cham, 2018.

DOI: 10.1007/978-3-319-95165-2_36

[12] T. I. Fossen, Handbook of Marine Craft Hydrodynamics and Motion Control, 2nd ed., John & Sons, Chichester, 2021.

[13] M. F. Teodoro et al., Kalman Filtering Applied to Some Navigation Systems: Advances in Science and Technology (In press)

[14] A. Gelb, Applied Optimal Estimation, Cambridge, Mass: M.I.T. Press, 1974.

[15] M. F. Teodoro, P. Carvalho, A. Trindade, Modelling the Fuel Consumption of a NRP Ship Using a Kalman Filter Approach, in: O. Gervasi et. al, Computational Science and Its Applications – ICCSA 2023, Springer, Cham, 2023.

DOI: 10.1007/978-3-031-37108-0_22

[16] NMEA, Available at https://www.nmea.org/content/STANDARDS/NMEA _0183_Standard. [Accessed at 22 July 2023].

[17] M. Vilares, A. Lopes, F. Melro, M. F. Teodoro, Análise de Conjuntura,Estudo Metodológico, Studies and Management Bureau, Portuguese Ministery of Economy, Lisboa, 1996.

[18] M. F. Teodoro, Filtragem de Kalman: uma Aplicação à Previsão da Produção da Industria Transformadora, Nova IMS, Universidade Nova de Lisboa, Lisboa, 2005.

DOI: 10.24140/issn.1645-7250.rle40.15

[19] Spire Sense Maritime, Available at https://faq.spire.com/available-ais-message-types. [Accessed at 22 July 2023].

[20] L. Mervart, G. Beutler, M. Rothacher, U. Wild, Ambiguity Resolution Strategies using the Results of the International GPS Service for Geodyanamics (IGS), Bulletin Géodesique 68 ( 1994) 29–38.

DOI: 10.1007/bf00806750

[21] L. Mervart, Ambiguity Resolution Techniques in Geodetic and Geodynamic Applications of the Global Positioning System, University of Bern, Bern, 1995.

[22] S. J. Julier and J. K. Uhlmann, Unscented Filtering and Nonlinear Estimation, in: proceedings of the IEEE, 92(3) (2004) 401-422.

DOI: 10.1109/jproc.2003.823141

[23] L. A. McGee, S. F. Scmidt, Discovery of the Kalman Filter as a Practical Tool for Aerospace and Industry. Technical Report NASA TM-86847, Washington DC: National Aeronautics and Space Administration, 1985.