Vessel Motion Statistical Learning based on Stored AIS Data and Its Application to Trajectory Prediction

DOI

10.2991/icmmct-17.2017.232How to use a DOI?

Keywords

Maritime Surveillance; Vessel Motion; Statistical Learning; AIS; Trajectory Prediction

Abstract

A vessel motion statistical learning based on stored AIS data is proposed in this paper. This paper divide the region of interest into a uniformly sized grid, and analyze the stored AIS data messages according the vessel's position and index the motion information into the unique grid. The sailing state variation between messages are highlighted. Several predictors are designed to predict the vessel's position and the prediction error is get comparing the true position achieved from AIS messages. Experimental results show that the proposed model is credible and the prediction accuracy is higher.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Lu Sun
AU  - Wei Zhou
PY  - 2017/04
DA  - 2017/04
TI  - Vessel Motion Statistical Learning based on Stored AIS Data and Its Application to Trajectory Prediction
BT  - Proceedings of the 2017 5th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2017)
PB  - Atlantis Press
SP  - 1183
EP  - 1189
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-17.2017.232
DO  - 10.2991/icmmct-17.2017.232
ID  - Sun2017/04
ER  -