Improvement in accuracy of determining a vessel’s position with the use of neural networks ana robust M-estimation

• Autorzy: Czaplewski K. , Wąż M.
• Języki publikacji: EN

Abstrakty

EN

In the 21st century marine navigation has become dominated by satellite positioning systems and automated navigational processes. Today, global navigation satellite systems (GNSS) play a central role in the process of carrying out basic navigational tasks, e.g. determining the coordinates of a vessel’s position at sea. Since satellite systems are being used increasingly more often in everyday life, the signals they send are becoming more and more prone to jamming. Therefore there is a need to search for other positioning systems and methods that would be as accurate and fast as the existing satellite systems. On the other hand, the automation process makes it possible to conduct navigational tasks more quickly. Due to the development of this technology, all kinds of navigation equipment can be used in the process of automating navigation. This also applies to marine radars, which are characterised by a relatively high accuracy that allows them to replace satellite systems in performing classic navigational tasks. By employing M-estimation methods that are used in geodesy as well as simple neural networks, a software package can be created that will aid in automating navigation and will provide highly accurate information about a given object’s position at sea by making use of radar in comparative navigation. This paper presents proposals for automating the process of determining a vessel’s position at sea by using comparative navigation methods that are based on simple neural networks and geodetic M-estimation methods.

Słowa kluczowe

EN

navigation; neural network; radar; M-estimation

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2017
• Tom: nr 1
• Strony: 22--31
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Czaplewski K.

• Akademia Morska w Gdyni, Morska 81-87, 81-225 Gdynia Polska

autor

Wąż M.

• Akademia Marynarki Wojennej, Śmidowicza 69, 81-103 Gdynia Polska

Bibliografia

• 1. Alippi C. 1995. Real-time analysis of ships in radar images with neural networks, Pattern Recognition, Volume 28 (12), pp. 1899-1913. New York, NY, USA.
• 2. Caspary W., Haen W. 1990. Simultaneous Estimation of Location and Scale Parameters in the Context of Robust M-estimation. Manuscripta Geodaetica no. 15, pp 273–282, Italy.
• 3. Czaplewski, K., 2004. Positioning with interactive navigational structures implementation. Annual of Navigation, no. 7/2004. Gdynia, Poland.
• 4. Czaplewski, K., 2014. Development of the IANS chain using a satellite system. TransNav, pp. 485–493, Gdynia, Poland.
• 5. Czaplewski, K., Wąż, M., 2009. Automation of radar navigation. European Journal of Navigation, no 7 (2), pp. 33–39. the Netherland.
• 6. Granger E., Rubin M., Grossberg S., Lavoie P. 2001. A What-and-Where fusion neural network for recognition and tracking of multiple radar emitters’, Neural Networks 14/2001, pp. 325-344, Bandera, TX, USA.
• 7. Hampel F.R., Ronchetti E.M., Rousseeuw P.J, Stahel W.A. 1986. Robust Statistics. The Approach Based on Influence Functions. John Wiley & Sons, New York, USA.
• 8. Jianjun Z. 1996. Robustness and the Robust Estimate. Journal of Geodesy no. 70, pp 586-590, Heidelberg, Germany.
• 9. Masters T., 1996. Sieci neuronowe w praktyce. Programowanie w języku C++. Wydawnictwo Naukowo Techniczne WNT, Warszawa, Poland.
• 10. Osowski S., 1996. Sieci neuronowe w ujęciu algorytmicznym. Wydawnictwo Naukowo Techniczne WNT, pp. 161–186, Warszawa, Poland.
• 11. Praczyk T., 2006A. Application of Neural Networks and Radar Navigational Aids of Shore Area to Positioning. Computational Methods in Science and Technology 12(2), DOI: 10.12921/cmst.2006.12.02. pp.149-155. Poznań, Poland.
• 12. Praczyk T., 2006B. Better Kohonen Neural Network in Radar Images Compression, Computational Methods In Science And Technology 12(2), DOI:10.12921/ cmst.2006.12.02, pp.157-164, Poznań, Poland.
• 13. Tadeusiewicz R., 1993. Sieci neuronowe. Akademicka Oficyna Wydawnicza RM, pp.121–125, Warszawa, Poland.
• 14. Vicen-Bueno R., Carrasco-Álvarez R., Rosa-Zurera M, Nieto-Borge J. 2009. Sea Clutter Reduction and Target Enhancement by Neural Networks in a Marine Radar System, Sensors 9(3), pp. 1913-1936; DOI:10.3390/ s90301913. Basel, Switzerland.
• 15. Wąż M., 2009. Precise matching of radar display with the nautical chart. International Conference ENC-GNSS 2009, pp. 329–334. Naples, Italy.
• 16. Wąż M., Meller R., 2003. Determination of ship’s position by way of matching a radar image with a nautical chart using a multilayer perceptron. International Radar Symposium IRS, pp. 701–706, Dresden, Germany.
• 17. Wiśniewski Z., 2002. Koncepcje opracowania wyników pomiarów nawigacyjnych. Naval University of Gdynia, pp. 47–50. Gdynia, Poland.
• 18. Wiśniewski Z., 2003. Metody opracowania wyników pomiarów w nawigacji i hydrografii. Naval University of Gdynia, pp. 224 –235, 249–263. Gdynia, Poland.
• 19. Wiśniewski Z., 2014. Zaawansowane metody opracowania obserwacji geodezyjnych z przykładami. Wydawnictwo UWM, Olsztyn, Poland.
• 20. Yang Y., Song L., Xu T. 2002. Robust Estimator for Correlated Observations Based on Bifactor Equivalent Weights. Journal of Geodesy nr 76, pp 353 – 358, Heidelberg, Germany.
• 21. Zhong D. 1997. Robust Estimation and Optimal Selection of Polynomial Parameters for the Interpolation of GPS Geoid Heights. Journal of Geodesy no. 71, pp 552 – 561, Heidelberg, Germany.
• 22. Żurada J., Barski M., Jędruch W., 1996. Sztuczne sieci neuronowe. Wydawnictwo Naukowe PWN. Warszawa, Poland

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)