A Surveillance Application of Satellite AIS - Utilizing a Parametric Model for Probability of Detection

Cheryl Eisler, Peter Dobias, Kenzie MacNeil

Abstract

The question of having sufficient surveillance capability to detect illicit behaviour in order to inform decision makers in a timely fashion is of the ultimate importance to defence, security, law enforcement, and regulatory agencies. Quantifying such capability provides a means of informing asset allocation, as well as establishing the link to risk of mission failure. Individual sensor models can be built and integrated into a larger model that layers sensor performance using a set of metrics that can take into account area coverage, coverage times, revisit rates, detection probabilities, and error rates. This paper describes an implementation of a parametric model for Satellite Automated Identification System (S-AIS) sensor performance. Utilizing data from a real data feed, the model was able to determine the percentage of uncorrupted S-AIS messages and the probability of detection of at least one correct S-AIS message received during an observation interval. It is important to note that the model implementation was not actively calculating the effect of message overlap based on satellite altitude and footprint width, or reductions in collisions due to signal decollision algorithms.

References

  1. Bošnjak, R., Šimunovica, L., and Kavran, Z., 2012. Automatic Identification System in Maritime Traffic and Error Analysis, Transactions on Maritime Science, 02, 77-84.
  2. Busler, J., Wehn, H., and Woodhouse, L., 2015. Tracking Vessels to Illegal Pollutant Discharges Using Multisource Vessel Information, In 36th International Symposium on Remote Sensing of Environment, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-7/W3, 927-932.
  3. Canadian Coast Guard, 2016. Marine Communications and Traffic Services MCTS, Government of Canada. Retrieved from: http://www.ccg-gcc.gc.ca/MarineCommunications/Home (Access Date; 13 September 2016).
  4. Cherrak, O., Ghenniou, H., Thirion-Moreau, N., and Abarkan, E., 2014. Successive Interference Cancellation technique for decollision of AIS signals in maritime surveillance context by a LEO satellite, In La première édition du Workshop International sur les nouvelles Technologies sans fil et Systèmes répartis, WITS.
  5. Cowles, P.R., D'Souza, I.A., and Peach, R.C., 2014. Satellite detection of automatic identification system signals, Patent CA 2691120 C, viewed 10 Dec 2016, http://www.google.com/patents/CA2691120C?cl=en.
  6. exactEarth, 2012. Satellite AIS and First Pass Detection: An exactEarth White Paper, viewed 10 Dec 2016, http://cdn2.hubspot.net/hub/183611/file-30951507- pdf/Collateral_for_Download/First_Pass_Detection_ White_Paper.pdf.
  7. Guerriero, M., Willett, P., Coraluppi, S., and Carthel, C., 2008. Radar/AIS Data Fusion and SAR tasking for Maritime Surveillance, In 11th International Conference on Information Fusion, IEEE, 1650-1654.
  8. Horn, S., Collins, Lt(N) J., Eisler, C., and Dobias, P., 2016. Data requirements for anomaly detection, In 2016 Workshop on Maritime Knowledge Discovery and Anomaly Detection.
  9. Høye, G., 2004. Observation Modelling and Detection Probability for Space-Based AIS Reception - Extended Observation Area, FFI Report, FFI/RAPPORT-2004/04390.
  10. International Telecommunications Union, 2014. Technical characteristics for an automatic identification system using time division multiple access in the VHF maritime mobile band (Recommendation ITU-R M.1371-4), viewed 8 Dec 2016, https://www.itu.int/dms_pubrec/itu-r/rec/m/RREC-M.1371-5-201402-I!!PDF-E.pdf.
  11. Macikunas, A. and Randhawa, B., 2012. Space-based Automated Identification System (AIS) Detection Performance and Application to World-wide Maritime Safety", In 30th AIAA International Communications Satellite Systems Conference, ICSSC.
  12. MacNeil, K., 2015. DRDC CORA Task #194: Coastal Surveillance Model Development, Defence Research and Development Canada - Centre for Operational Research and Analysis, DRDC-RDDC-2015-C283.
  13. MarineTraffic.com, 2016. Ships List - Vessel Search | AIS Marine Traffic, viewed 10 Dec 2016, http://www.marinetraffic.com/en/ais/index/ships/all/st atus:all.
  14. Meger, E., 2013. Limitations of Satellite AIS: Time Machine Wanted!, viewed 10 Dec 2016, http://d284f45nftegze.cloudfront.net/emeger/White%2 0Paper%20- %20Limitations%20of%20Satellite%20AIS%20- %20Time%20Machine%20Wanted.pdf.
  15. myShipTracking, 2016. My Ship Tracking Free Realtime AIS Vessel Tracking Finder Map, viewed 10 Dec 2016, http://www.myshiptracking.com/search/vessels.
  16. Parsons, G., Youden, J., Yue, B., and Fowler, C, 2013. Satellite Automatic Identification System (SAIS) Performance Modelling and Simulation: Final Findings Report, Defence Research and Development Canada - Ottawa, DRDC Ottawa CR 2013-096.
  17. Picard, M., Ourlarbi, M.R., Flandin, G., and Houcke, S., 2012. An Adaptive Multi-User Multi-Antenna Receiver for Satellite-Based AIS Detection, 6th Advanced Satellite Multimedia Systems Conference and 12th Signal Processing for Space Communications Workshop, IEEE, 273-280.
  18. International Maritime Organization, 2015. SOLAS 1974, Chapter V, Regulation 19. Retrieved from http://www.imo.org/en/About/Conventions/ListOfCon ventions/Pages/International-Convention-for-theSafety-of-Life-at-Sea-(SOLAS),-1974.aspx (Access Date: 13 September 2016).
  19. Tunaley, J.K.E., 2011a. Space-Based AIS Performance, London Research and Development Corporation Technical Report, LRDC 2011-05-23-001.
  20. Tunaley, J.K.E., 2011b. The Performance of Space-Based AIS System, London Research and Development Corporation Technical Report, LRDC 2011-06-20- 001.
  21. Tunaley, J.K.E, 2013. Utility of Various AIS Messages for Maritime Awareness, London Research and Development Corporation Technical Report, LRDC 2013-10-001.
  22. Vesecky J.F., Laws, K., and Paduan, J.D., 2009. Using HF surface wave radar and the ship Automatic Identification System (AIS) to monitor coastal vessels, In Geoscience and Remote Sensing Symposium, IEEE, Volume 3.
  23. Yang, J, Cheng, Y., and Chen, L., 2014. The Detection Probability Modeling and Application Study of Satellite-Based AIS System, In 7th Joint International Information Technology and Artificial Intelligence Conference, IEEE.
  24. Yang, M., Zou, Y, and Fang, L., 2012. Collision and Detection Performance with Three Overlap Signal Collisions in Spaced-Based AIS Reception, In 11th International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, 1641-1648.
Download


Paper Citation


in Harvard Style

Eisler C., Dobias P. and MacNeil K. (2017). A Surveillance Application of Satellite AIS - Utilizing a Parametric Model for Probability of Detection . In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-218-9, pages 211-218. DOI: 10.5220/0006108302110218


in Bibtex Style

@conference{icores17,
author={Cheryl Eisler and Peter Dobias and Kenzie MacNeil},
title={A Surveillance Application of Satellite AIS - Utilizing a Parametric Model for Probability of Detection},
booktitle={Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2017},
pages={211-218},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006108302110218},
isbn={978-989-758-218-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - A Surveillance Application of Satellite AIS - Utilizing a Parametric Model for Probability of Detection
SN - 978-989-758-218-9
AU - Eisler C.
AU - Dobias P.
AU - MacNeil K.
PY - 2017
SP - 211
EP - 218
DO - 10.5220/0006108302110218