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Detection and Tracking of Ships in the Canadian Arctic

  • Steven Horn
Chapter
Part of the WMU Studies in Maritime Affairs book series (WMUSTUD, volume 7)

Abstract

The Canadian Arctic is becoming increasingly important as climate change and economic pressures stimulate increasing activity in the region. The number of transits, cruise ships, and adventurer expeditions in this area is on the rise. Ensuring environmental, economic, archeological, defence, safety and security responsibilities in this challenging area has resulted in many recent investments including the Arctic Offshore Patrol Vessels and the RADARSAT Constellation Mission. This chapter will explore the challenges in detection and tracking of ships in the Arctic from perspectives including: ship-ice discrimination in remote sensing, sparse data tracking, effects of constrained navigation, and operational decision aids.

Keywords

Arctic Surveillance Situational awareness Detection Sparse data 

Notes

Acknowledgements

Gratefully acknowledged is Paris W. Vachon of DRDC Ottawa Research Centre, who provided helpful comments, discussion, and reference material related to space based SAR and S-AIS. The author also acknowledges the Arctic dataset provided by the Royal Canadian Navy’s Global Position Warehouse developed and maintained by the team of Scott Syms and Andrew DeBaie. The operational ship detection capabilities of RADARSAT-2 and the RADARSAT Constellation Mission are implemented via the Department of National Defence Polar Epsilon and Polar Epsilon 2 projects, MacDonald, Dettwiler and Associates, and DRDC Ottawa Research Centre.

References

  1. Amante, C., & Eakins, B. W. (2009). ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center. NOAA.  https://doi.org/10.7289/V5C8276M
  2. Canada Space Agency. (2015). RADARSAT Constellation Mission description, online. Retrieved July 21, 2015, from http://www.asc-csa.gc.ca/eng/satellites/radarsat/description.asp
  3. Canada’s Northern Strategy. (2013) Our north, our heritage, our future, online. Retrieved July 21, 2015, from http://www.northernstrategy.gc.ca
  4. Canadian Coast Guard. (2013). Vessel traffic reporting Arctic Canada traffic Zone (NORDREG), online. Retrieved July 21, 2015, from http://www.ccg-gcc.gc.ca/eng/MCTS/Vtr_Arctic_Canada
  5. Cervera, M., & Alberto, G. (2008). On the performance analysis of a satellite-based AIS system. In IEEE 10th International Workshop on Signal Processing for Space Communications.Google Scholar
  6. Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (pp. 226–231). AAI Press.Google Scholar
  7. Hammond, T (2014) Applications of probabilistic interpolation to ship tracking. In Proceedings of Joint Statistical Meetings (pp. 1–19).Google Scholar
  8. Howell, C., Power, D., Lynch, M., Dodge, K., Bobby, P., Randell, C., et al. (2008). Dual polarization detection of ships and icebergs – recent results with ENVISAT ASAR and data simulations of RADARSAT-2. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 206–209).Google Scholar
  9. Howell, C., Youden, J., Kane, K., Power, D., Randell, C., & Flett, D. (2004). Iceberg and ship discrimination with ENVISAT multipolarization ASAR. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), (pp. 113–116).Google Scholar
  10. Mazzarella, F., Vespe, M., & Santamaria, C. (2015). SAR ship detection and self-reporting data fusion based on traffic knowledge. IEEE Geoscience and Remote Sensing Letters, 12(8), 1685–1689.CrossRefGoogle Scholar
  11. Pallotta, G., Horn, S., Braca, P., & Bryan, K. (2014). Context-enhanced vessel prediction based on Ornstein-Uhlenbeck processes using historical AIS traffic patterns: Real-world experimental results. In Information Fusion (FUSION), 2014 17th International Conference on (pp. 1–7).Google Scholar
  12. Pallotta, G., Vespe, M., & Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15(6), 2218–2245.CrossRefGoogle Scholar
  13. Papa, G., Horn, S., Braca, P., Bryan, K., & Romano, G. (2012) Estimating sensor performance and target population size with multiple sensors. In Information Fusion (FUSION), 2012 15th International Conference on (pp. 2102–2109).Google Scholar
  14. Reynolds, R. W., Rayner, N. A., Smith, T. M., Stokes, D. C., & Wang, W. (2002). An improved in situ and satellite SST analysis for climate. Journal of Climate, 15, 1609–1625.CrossRefGoogle Scholar
  15. Tunaley, J. K. E. (2011). Space-based AIS performance (Technical Report 2011-05-23-001). London Research and Development Corporation.Google Scholar
  16. Vachon, P. W., Kabatoff, C., & Quinn, R. (2014). Operational ship detection in Canada using RADARSAT. In Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (pp. 998–1001).Google Scholar

Copyright information

© Her Majesty the Queen in Right of Canada 2018 as represented by the Department of National Defence 2018

Authors and Affiliations

  1. 1.Defence Research and Development CanadaCentre for Operational Research and AnalysisOttawaCanada

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