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Research on Ship Classification Based on Trajectory Features

Published online by Cambridge University Press:  23 August 2017

Kai Sheng ,
Zhong Liu ,
Dechao Zhou ,
Ailin He  and
Chengxu Feng
Kai Sheng*
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Zhong Liu
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Dechao Zhou
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Ailin He
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
Chengxu Feng
Affiliation:
(College of Electronic Engineering, Naval University of Engineering, Wuhan, Hubei, China)
*
(E-mail: [email protected])
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Abstract

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

Keywords

Ship classificationTrajectory feature extractionAIS dataLogistic regression
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 1 , January 2018 , pp. 100 - 116
Copyright
Copyright © The Royal Institute of Navigation 2017 

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