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Interpolation Between AIS Reports: Probabilistic Inferences Over Vessel Path Space

Published online by Cambridge University Press:  12 September 2011

D. J. Peters  and
T. R. Hammond
D. J. Peters*
Affiliation:
(Defence R&D Canada – Atlantic)
T. R. Hammond
Affiliation:
(Defence R&D Canada – Atlantic)
*
(Email: [email protected])
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Abstract

We present a method for addressing probabilistic queries about the location of a vessel in the time interval between two position reports, such as from the Automatic Identification System (AIS). The heart of the method is the random generation of physically feasible paths connecting the two reports. The method empowers operators to answer probabilistic questions about any characteristic of the unknown true path. For illustrative purposes, we demonstrate the use of the method to identify which of several vessels is the most likely perpetrator, in a fictitious scenario in which illegal dumping of waste matter has taken place.

Keywords

Bayesian inferenceSituational awarenessAIS
Type
Research Article
Information
The Journal of Navigation , Volume 64 , Issue 4 , October 2011 , pp. 595 - 607
Copyright
Copyright © The Royal Institute of Navigation 2011

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References

REFERENCES

Devijver, P. A. and Kittler, J. (1982). Pattern Recognition: A Statistical Approach. Prentice-Hall.Google Scholar
Dijkstra, E. (1959). A note on two problems in connection with graphs. Numerische Mathematik, 1, 269–271.CrossRefGoogle Scholar
Gelman, A., Carlin, J., Stern, H. and Rubin, D. (1995). Bayesian Data Analysis. London: Chapman and Hall.CrossRefGoogle Scholar
Hammond, T., McIntyre, M., Chapman, D. and Lapinski, L. (2006). The implications of self-reporting systems for maritime domain awareness. 11th ICCRTS Symposium: Command and Control in the Networked Era, Cambridge, UK.Google Scholar
Hammond, T. and Peters, D. J. (2009). Probabilistic interpolation between position reports. NATO Workshop on Data Fusion and Anomaly Detection for Maritime Situational Awareness, La Spezia, Italy.Google Scholar
Lane, R.O., Nevell, D. A., Hayward, S. D. and Beaney, T. W. (2010). Maritime anomaly detection and threat assessment. 13th International Conference on Information Fusion, Edinburgh, UK.Google Scholar
Laxhammar, R., Falkman, G. and Sviestins, E. (2009). Anomaly detection in sea traffic – a comparison of the Gaussian mixture model and kernel density estimators. 12th International Conference on Information Fusion, Seattle, WA.Google Scholar
Nilsson, N. J. (1980). Principles of Artificial Intelligence. Palo Alto, CA: Tioga Publishing Company.Google Scholar
Ristic, B., La Scala, B., Morelande, M. and Gordon, N. (2008). Statistical analysis of motion patterns in AIS data: anomaly detection and motion prediction. 11th International Conference on Information Fusion, Cologne, Germany.Google Scholar
Somers, C. and Chaulk, N. (2004). Extending Automatic Identification System (AIS) coverage to the middle zone. Dartmouth, NS: Defence R&D Canada – Atlantic (CR 2004-067).Google Scholar
Sumner, M. D., Wotherspoon, S. J. and Hindell, M. A. (2009). Bayesian estimation of animal movement from archival and satellite tags. PLoS ONE, 4, e7324.CrossRefGoogle ScholarPubMed