Algorithms for Ship Movement Prediction for Location Data Compression

• Autorzy: Czapiewska A. , Sadowski J.

Identyfikatory

DOI

10.12716/1001.09.01.09

• Języki publikacji: EN

Abstrakty

EN

Due to safety reasons, the movement of ships on the sea, especially near the coast should be tracked, recorded and stored. However, the amount of vessels which trajectories should be tracked by authorized institutions, often in real time, is usually huge. What is more, many sources of vessels position data (radars, AIS) produces thousands of records describing route of each tracked object, but lots of that records are correlated due to limited dynamic of motion of ships which cannot change their speed and direction very quickly. In this situation it must be considered how many points of recorded trajectories really have to be remembered to recall the path of particular object. In this paper, authors propose three different methods for ship movement prediction, which explicitly decrease the amount of stored data. They also propose procedures which enable to reduce the number of transmitted data from observatory points to database, what may significantly reduce required bandwidth of radio communication in case of mobile observatory points, for example onboard radars.

Słowa kluczowe

EN

Methods and Algorithms; Ship Movement, Ship Movement Prediction; location; Location Data Compression; Autoregressive Model (AR); Autoregressive Moving Average Model (ARMA); AIS Data

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2015
• Tom: Vol. 9, no. 1
• Strony: 75--81
• Opis fizyczny: Bibliogr. 6 poz., rys., tab.

Twórcy

autor

Czapiewska A.

• Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Poland

autor

Sadowski J.

• Faculty of Electronics, Telecommunications and Informatics, Gdansk University of Technology, Poland

Bibliografia

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• 2 Grenier Y. 1983. Estimation of non‐stationary movingaverage models, Proceedings of IEEE International Conference on ICASSP’83, volume 8, pages 268‐271
• 3 Grewal M. S., Andrews A. P. 2008. Kalman Filtering Theory and Practice using MATLAB
• 4 IMO Resolution MSC.74(69). 1998.
• 5 Kashyap R. L. 1982. Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models, IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 99‐104
• 6 Welch G., Bishop G. 2006. An Introduction to the Kalman Filter