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Two-dimensional coordinate estimation for missing automatic identification system (AIS) signals based on the discrete Kalman filter algorithm and universal transverse mercator (UTM) projection

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Języki publikacji
EN
Abstrakty
EN
Due to safety reasons, the movement of a ship in coastal areas should be monitored, tracked, recorded, and stored. The Automatic Identification System (AIS) is a suitable tool to use in performing these functions. The probability limit for the AIS dynamic data availability can be limited by the lack of a Global Position System (GPS) signal, heading (HDG), and rate of turn (ROT) data in the position report. The unavailability of a data link is an additional limitation. To fill this gap, it is possible to attach the discrete Kalman filter (KF) for the position and course estimation. Coordinate estimation in the absence of a transmission link can improve the quality of the AIS service at Vessel Traffic Service (VTS) stations. This paper has presented the Kalman filtering algorithm to improve the possibilities for ship motion tracking and monitoring in the TSS (Traffic Separation Scheme) and fairways area. More than 570 iterations were calculated and the results have been presented in figures to familiarize the reader with the operating principle of the Kalman filter algorithm.
Rocznik
Strony
82--89
Opis fizyczny
Bibliogr. 16 poz., rys., tab.
Twórcy
  • Polish Naval Academy, Institute of Navigation and Maritime Hydrography 69 Śmidowicza St. 81-103 Gdynia, Poland
Bibliografia
  • 1. Banachowicz, A. & Urbański, J. (1988) Obliczenia nawigacyjne. Gdynia: AMW.
  • 2. Banyś, P., Noack,T. & Gewies, S. (2012) Assessment of AIS vessel position report under the aspect of data reliability. Annual of Navigation 19, 1, pp. 5–16.
  • 3. Bezrucka, J. (2012) The use of a Kalman Filter in Geodesy and Navigation. Slovak Journal of Civil Engineering XIX, 2, pp. 8–15.
  • 4. Czapiewska, A. & Sadowski, J. (2015) Algorithms for Ship Movement Prediction for Location Data Compression. TRANSNAV, The International Journal on Marine Navigation and Safety of Sea Transportation 9, 1, pp. 75–81.
  • 5. Felski, A., Jaskólski, K. & Banyś, P. (2015) Comprehensive Assessment of Automatic Identification System (AIS) Data Application to Anti-collision Maneuvering. The Journal of Navigation 68, pp. 697–717,
  • 6. Hori, A., Arai, Y., Okuda, S. & Fujie, S. (2009) Reliability and Availability on Onboard AIS Information. Proceedings IAIN 2009, pp. 1–10, Stockholm.
  • 7. ITU-R M.1371 (2014) Technical characteristics for an automatic identification system using TDMA in the VHF maritime mobile frequency band.
  • 8. Jaskólski, K. (2017) AIS dynamic data estimation based on Kalman Filter. AIS Seminar, HELCOM’17, Helsinki.
  • 9. Kaniewski, P. (2010) Funkcje, struktury i algorytmy w zintegrowanych systemach pozycjonujących i nawigacyjnych. Rozprawa habilitacyjna. Warszawa: WAT.
  • 10. Kantak, T., Stateczny, A. & Urbański, J. (1988) Podstawy automatyzacji nawigacji, cz. A, Zautomatyzowane systemy nawigacyjne. Gdynia: AMW.
  • 11. Konatowski, S. & Sipa, T. (2004) Position Estimation Using Unscented Kalman Filter. Annual of Navigation 8, pp. 97–110.
  • 12. MSC.115(73) (2000) Adoption of the revised performance standards for shipborne combined GPS/GLONASS receiver equipment. London: IMO.
  • 13. MSC.96(72) (2000) Adoption of amendments to performance standards for devices to measure and indicate speed and distance (Resolution A.824(19)). London: IMO.
  • 14. Naus, K. & Nowak, A. (2016) The Positioning Accuracy of BAUV Using Fusion of Data from USBL System and Movement Parameters Measurements. Sensors 16, 1279, pp. 1–23.
  • 15. Richert, D. (2017) Propozycja modernizacji systemu AIS w oparciu o filtr Kalmana. Praca magisterska. Gdynia: AMW.
  • 16. Welch, G. & Bishop, G. (2006) An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, pp. 1–16.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83f52e28-cd23-4cb0-97c6-0bbc5543ac8f
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