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Detection of Abnormal Vessel Behaviour Based on Probabilistic Directed Graph Model

Published online by Cambridge University Press:  31 March 2020

Huang Tang ,
Liqiao Wei ,
Yong Yin ,
Helong Shen  and
Yinghong Qi
Huang Tang
Affiliation:
(Laboratory of Marine Simulation and Control, Dalian Maritime University, Dalian, China)
Liqiao Wei
Affiliation:
(Laboratory of Marine Simulation and Control, Dalian Maritime University, Dalian, China)
Yong Yin
Affiliation:
(Laboratory of Marine Simulation and Control, Dalian Maritime University, Dalian, China)
Helong Shen*
Affiliation:
(Laboratory of Marine Simulation and Control, Dalian Maritime University, Dalian, China)
Yinghong Qi
Affiliation:
(Chongqing Three Gorges University, Chongqing, China)
*
(E-mail: shenhelong@126.com)
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Abstract

To detect the abnormal behaviour of ships in the waters of any jurisdiction and to improve the safety of maritime navigation, the meshing-based method is adopted to obtain discrete trajectory data and a probabilistic directed graph model is established to obtain historical data from ships' AIS (automatic identification systems). The state statistical characteristics of each node in the ship probability map are obtained to detect the navigation state of the ship in real time. By predicting the normal navigation trajectory of the ship, it can be judged whether the ship has the potential to behave abnormally at some moment in the future. Simulation experiments were designed based on a maritime simulator platform. The experimental results indicate that the model can correctly predict abnormal behaviour by ships, including excessive speed and deviation from the channel or normal sailing mode.

Keywords

Maritime Abnormal DetectionProbabilistic Directed Graph ModelShip Trajectory PredictionMarine Surveillance

Information

Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 5 , September 2020 , pp. 1014 - 1035
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Copyright
Copyright © The Royal Institute of Navigation 2020

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