Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T08:44:34.311Z Has data issue: false hasContentIssue false
  • English
  • Français

Vessel Track Recovery With Incomplete AIS Data Using Tensor CANDECOM/PARAFAC Decomposition

Published online by Cambridge University Press:  03 July 2013

Changqing Liu  and
Xiaoqian Chen
Changqing Liu
Affiliation:
(College of Aerospace Science and Engineering, National University of Defense Technology, China)
Xiaoqian Chen*
Affiliation:
(College of Aerospace Science and Engineering, National University of Defense Technology, China)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Global analysis of vessel motion patterns has become possible using satellite-based Automatic Identification System (AIS). The concept of space-based AIS needs several satellites to provide complete coverage and high detection probability. However, in early development stages, often only one satellite is launched and due to its limitation of orbit and footprint, received AIS messages are discontinuous. In this paper, we have analysed real AIS data obtained by satellite to form a global maritime surveillance picture. Furthermore, we propose to take advantage of the tensor CANDECOMP/PARAFAC (CP) decomposition to analyse three mode characteristics of the data, which are location, vessel and time. For incomplete data, we exploit the link prediction technique based on tensor factorisation to recover vessel tracks in a specified area. A variant of temporal link prediction based on CP is presented. We illustrate the usefulness of exploiting the three-mode structure of AIS data by simulation, and demonstrate that the track recovery result has acceptable precision.

Keywords

Automatic Identification SystemTensor DecompositionLink Prediction
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 1 , January 2014 , pp. 83 - 99
Copyright
Copyright © The Royal Institute of Navigation 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Adamic, L.A. and Adar, E. (2003). Friends and Neighbors on the Web. Social Networks, 25(3): 211–230.CrossRefGoogle Scholar
Bader, B. W. and Kolda, T. G. (2007). MATLAB Tensor Toolbox, Version 2.2. http://csmr.ca.sandia.gov/tgkolda/TensorToolbox/.Google Scholar
Becky, C., Foley, K.C. and Chandler, S. (2012). The Ability of a Small Satellite Constellation to Tip and Cue Other Commercial Assets. Proceedings of 26th Annual AIAA/USC Conference on Small Satellites, Logan, UT.Google Scholar
Bomberger, N.A., Rhodes, B.J., Seibert, M. and Waxman, A.M. (2006). Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness. Proc. Int. Conf. Information Fusion, Florence, Italy.Google Scholar
Brax, C. (2011). Anomaly Detection in the Surveillance Domain. University of Skovde.Google Scholar
Carroll, J.D. and Chang, J.J. (1970). Analysis of Individual Differences in Multidimensional Scaling via an N-way Generalisation of ‘Eckart-Young’ Decomposition. Psychometrika, 35, 283–319.CrossRefGoogle Scholar
Carson-Jackson, J. (2012). Satellite AIS - Developing Technology or Existing Capability? Journal of Navigation, 65(2): 303–321.CrossRefGoogle Scholar
Cervera, M.A., Ginesi, A. and Eckstein, K. (2011). Satellite-based Vessel Automatic Identification System: a Feasibility and Performance Analysis. Int. J. Satell. Commun. Network., 29, 117–142.CrossRefGoogle Scholar
Dahl, O.F.H. (2006). Space-Based AIS Receiver for Maritime Traffic Monitoring Using Interference Cancellation, Norwegian University of Science and Technology.Google Scholar
Dunlavy, D.M., Kolda, T.G. and Acar, E. (2011). Temporal Link Prediction Using Matrix and Tensor Factorisations. ACM Trans. Knowl. Discov. Data, 5(2).CrossRefGoogle Scholar
Eriksen, T., Hoye, G., Narheim, B. and Meland, B.J. (2006). Maritime Traffic Monitoring Using a Space-Based AIS Receiver. Acta Astronautica, 58, 537–549.CrossRefGoogle Scholar
Harshman, R.A. (1970). Foundations of the PARAFAC Procedure: Models and Conditions for an “Explanatory” Multi-modal Factor Analysis. UCLA working papers in phonetics, 16, 1–84.Google Scholar
Høye, G. (2004). Observation Modelling and Detection Probability for Space Based AIS Reception-Extended Observation Area. FFI/RAPPORT-2004/04390, Forsvarets Forsknlnginstitutt.Google Scholar
Høye, G.K., Eriksen, T., Meland, B.J. and Narheim, B.T. (2008). Space - Based AIS for Global Maritime Traffic Monitoring. Acta Astronautica, 62, 240–245.CrossRefGoogle Scholar
Jemin, G., John, C.L., Tarunraj, S. and Adam, F.M. (2011). Anomaly Detection Using Context-Aided Target Tracking. Journal of Advances in Information Fusion, 6(1): 39–56.Google Scholar
Katz, L. (1953). A New Status Index Derived From Sociometric Analysis. Psychometrika, 18(1): 39–43.CrossRefGoogle Scholar
Kolda, T.G. and Bader, B.W. (2008). Tensor Decompositions and Applications. SIAM Review.Google Scholar
Kruskal, J.B. (1989). Rank, Decomposition, and Uniqueness for 3-way and N-way Arrays. Multiway Data Analysis, Amsterdam, 7–18.Google Scholar
Lane, R.O., Nevell, D.A., Hayward, S.D. and Beaney, T.W. (2010). Maritime Anomaly Detection and Threat Assessment, 2010 13th Conference on Information Fusion, 1–8.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. Proceedings of 12th International Conference on Information Fusion, Seattle, WA, 756–763.Google Scholar
Laxhammar, R. and Falkman, G. (2010). Conformal Prediction for Distribution - Independent Anomaly Detection in Streaming Vessel Data. Proceedings of Stream KDD'10, ACM, Washington, DC, 47–55.Google Scholar
Rhodes, B.J., Bomberger, N.A. and Zandipour, M. (2007). Probabilistic Associative Learning of Vessel Motion Patterns at Multiple Spatial Scales for Maritime Situation Awareness. Proc. Int. Conf. Information Fusion, Quebec, Canada.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. Proceedings of the 11th International Conference on Information Fusion, 1–7.Google Scholar
Riveiro, M. and Falkman, G. (2010). Supporting the Analytical Reasoning Process in Maritime Anomaly Detection : Evaluation and Experimental Design. Proceedings of the 14th IEEE International Conference on Information Visualisation (IV'10), 170–178.Google Scholar
Tun, M.H., Chambers, G., Tan, T. and Ly, T. (2007). Maritime Port Intelligence Ssing AIS Data. Proc. RNSA Security Technology Conference, Melbourne, Australia.Google Scholar
Wahl, T. and Høye, G.K. (2003). New Possible Roles of Small Satellites in Maritime Surveillance. Proceedings of Fourth IAA Symposium on Small Satellites for Earth Observation, Berlin, Germany.Google Scholar