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Inference of Single Vessel Behaviour with Incomplete Satellite-based AIS Data

Published online by Cambridge University Press:  27 June 2013

Changqing Liu  and
Xiaoqian Chen
Changqing Liu
Affiliation:
(College of Aerospace Science and Engineering, National University of Defense Technology, P.R.China)
Xiaoqian Chen*
Affiliation:
(College of Aerospace Science and Engineering, National University of Defense Technology, P.R.China)
*
(E-mail: chenxiaoqian@nudt.edu.cn)
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Abstract

The problem of analysing a single vessel's behaviour from real but incomplete Automatic Identification System (AIS) data received by satellite has been investigated. The main objective was to infer the route of any single vessel of interest, utilising the dynamic information decoded from AIS messages. A complete process of route inference using position, speed, course over ground and time stamp information is proposed in this paper. Due to the incompleteness of satellite AIS messages, an algorithm incorporating random deviations is also presented to account for the missing sections of obtained vessel routes. Analysis results from a set of real AIS data have demonstrated the applicability of the proposed algorithms in various scenarios.

Keywords

AISVessel BehaviourAnomaly Detection
Type
Research Article
Information
The Journal of Navigation , Volume 66 , Issue 6 , November 2013 , pp. 813 - 823
Copyright
Copyright © The Royal Institute of Navigation 2013 

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References

REFERENCES

Carson-Jackson, J. (2012). Satellite AIS – Developing Technology or Existing Capability?. The Journal of Navigation, 65, 303–321.CrossRefGoogle Scholar
Harati-Mokhtari, A., Brooks, P., Wall, A. and Wang, J. (2007). AIS Contribution in Navigation Operation – Using AIS User Satisfaction Model. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, V.1, N.3, 243–249.Google Scholar
Hu, Q., Yong, J., Shi, C. and Chen, G. (2010). Evaluation of Main Traffic Congestion Degree for Restricted Waters with AIS Reports. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, V.4, N.1, 55–58.Google Scholar
Johansson, F. and Falkman, G. (2007). Detection of Vessel Anomalies – a Bayesian Network Approach, 3rd International Conference on Intelligent Sensors, Sensor Networks and Information Processing, 395–400.CrossRefGoogle Scholar
Laxhammar, R. (2008). Anomaly Detection for Sea Surveillance, The 11th International Conference on Information Fusion, Cologne, Germany.Google Scholar
Laxhammar, R., Falkman, G. and Sviestins, E. (2009). Anomaly Detection in Sea Traffic – a Comparison of the Gaussian Mixture Model and the Kernel Density Estimator, 12th International Conference on Information Fusion, Seattle, WA, USA.Google Scholar
Mascaro, S., Korb, K. B. and Nicholson, A. E. (2010). Learning Abnormal Vessel Behaviour From AIS Data With Bayesian Networks at Two Time Scales. Technical Report.Google Scholar
Peters, D. J. and Hammond, T. R. (2011). Interpolation Between AIS Reports: Probabilistic Inferences Over Vessel Path Space. The Journal of Navigation, 64, 595–607.CrossRefGoogle Scholar
Redoutey, M., Scotti, E., Jensen, C., Ray, C. and Claramunt, C. (2008). Efficient Vessel Tracking with Accuracy Guarantees, LNCS, 5373, 140–151.Google Scholar
Ristic, B., Scala, B. L., Morelande, M. and Gordon, N. (2008). Statistical Analysis of Motion Patterns in AIS Data: Anomaly Detection and Motion Prediction. The 11th International Conference on Information Fusion, Cologne, Germany.Google Scholar
Szlapczynski, R. (2011). Evolutionary Sets of Safe Ship Trajectories: A New Approach to Collision Avoidance. The Journal of Navigation, 64, 169–181.CrossRefGoogle Scholar