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Ship Surveillance by Integration of Space-borne SAR and AIS – Further Research

Published online by Cambridge University Press:  20 November 2013

Zhi Zhao ,
Kefeng Ji ,
Xiangwei Xing ,
Huanxin Zou  and
Shilin Zhou
Zhi Zhao*
Affiliation:
(College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P. R. China)
Kefeng Ji
Affiliation:
(College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P. R. China)
Xiangwei Xing
Affiliation:
(College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P. R. China)
Huanxin Zou
Affiliation:
(College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P. R. China)
Shilin Zhou
Affiliation:
(College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan, P. R. China)
*
(E-mail: [email protected])
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Abstract

Many countries are making increased efforts to improve marine security and safety and develop ship surveillance techniques to satisfy the increasing demands. Space-borne Synthetic Aperture Radar (SAR) delivers high performance day/night all weather capabilities and a space-based Automatic Identification System (AIS) can give near real time and global coverage. Limited by the development of sensors and data processing techniques, the integration of space-borne SAR and AIS has much to offer ship surveillance. State-of-the-art data fusion methods have generally provided satisfactory performance. However, in high-density shipping or high sea-states, performance quality is less assured. This paper firstly investigates improved data association methods. The association methods based on the position feature are improved, and multi-feature-based association methods are proposed. Then, ship identification and tracking by the integration of space-borne SAR and AIS are researched further. Multi-source data fusion strategy is also investigated. Finally, the discussion is presented and the future works are emphasized in the conclusion.

Keywords

Ship SurveillanceSynthetic Aperture Radar (SAR)Automatic Identification System (AIS)
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 2 , March 2014 , pp. 295 - 309
Copyright
Copyright © The Royal Institute of Navigation 2013 

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References

REFERENCES

Brusch, S., Lehner, M., Fritz, T., Schwarz, E., Lehner, S. and Hamidi, D. (2011). DLR-German Aerospace Center. Near Real Time Ship Detection Experiments. http://earth.eo.esa.int/workshops/seasar2010/participants/471/pres_471_brusch.pdf. Accessed 13 January 2013.Google Scholar
Dempster, A.P. (2008). The Dempster-Shafer calculus for statisticians. International Journal of Approximate Reasoning, 48, 365–377.CrossRefGoogle Scholar
Guerriero, M., Willett, P., Coraluppi, S. and Carthel, C. (2008). Radar/AIS Data Fusion and SAR tasking for Maritime Surveillance. Proceedings of 11th conference on information fusion, Cologue, Germany.Google Scholar
Jouni, S., Timo, K. and Harri, H. (2002). A review on Networking and Multiplayer Computer Games. Game Turku Centre for Computer Science TUCS Technical Report, 454, 1–20.Google Scholar
Kerbaol, V. (2006). Theme 4: Existing Remote Sensing Means-Satellite Radar and Passive sensors-The Synthetic Aperture Radar satellite imagery. http://www.cedre.fr/fr/publication/colloque/obs/4_boost.pdf. Accessed 13 January 2013.Google Scholar
Knapskog, A.O. (2010). Classification of ships in TerraSAR-X images based on 3D models and silhouette matching. Proceedings of 8th European Conference on Synthetic Aperture Radar (EUSAR), Aachen, Germany.Google Scholar
Lu, Y. and Yamaoka, F. (1997). Fuzzy integration of classification results. Elsevier: Pattern Recognition, 30, 1877–1891.Google Scholar
Murphy, C. (2000). Combining belief function when evidence conflicts. Decision Support System, 29, 1–9.CrossRefGoogle Scholar
Myronenko, A. and Song, X.B. (2010). Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 2262–2275.Google Scholar
Osman, H., Pany, L., Blosteiny, S.D. and Gagnonz, L. (1997). Classification of ships in airborne SAR imagery using backpropagation Neural Networks. SPIE Proceedings of Radar Processing, Technology and Applications II, 3161, 126–136.Google Scholar
Premaratne, P. and Safaei, F. (2009). Ship classification by superstructure moment invariants. Emerging Intelligent Computing Technology and Applications Lecture Notes in Computer Science, 5754, 327–335.Google Scholar
Qu, Z.Y., Gao, H. and Zhu, Y. (2008). Research on High accuracy Position Prediction Algorithm in Online Game. Proceedings of IEEE International Symposium on Electronic Commerce and Security (ISECS), Guangzhou, China.CrossRefGoogle Scholar
Shafer, G. (1976). A Mathematical Theory of Evidence. Princeton University Press.CrossRefGoogle Scholar
Touzi, R.; Raney, R.K. and Charbonneau, F. (2004). On the use of permanent symmetric scatters for ship characterization. IEEE Transaction on Geoscience and Remote Sensing, 42, 2039–2045.Google Scholar
Vachon, P.W., English, R.A. and Wolfe, J. (2007). Ship Signatures in RADARSAT-1 ScanSAR Narrow B Imagery Analysis with AISLive Data. TECHNICAL MEMORANDUM DRDC Ottawa, 52, 1–44.Google Scholar
Wanas, N. (2003). Feature-based architectures for decision fusion. http://pami.uwaterloo.ca/pub/nwanas/theis.pdf. Accessed 16 January 2013.Google Scholar
Zhao, J., Zhou, S.L., Sun, J.X. and Li, Z.Y. (2010). Point Pattern Matching Using Relative Shape Context and Relaxation Labeling. Proceeding of 2nd International Conference on Advanced Computer Control (ICACC), Shenyang, China.Google Scholar
Zimmermann, H.-J. (2010). Fuzzy set theory. WIREs Computational Statistics, 2, 317–332.CrossRefGoogle Scholar
Zou, R., Mou, X. and Yi, W. (2012). The Non-Equidistant Grey GRM (1, 1) Model and Its Application. International Journal of Modern Nonlinear Theory and Application, 1, 51–54.Google Scholar