Unsupervised extraction of maritime patterns of life from Automatic Identification System data

This paper presents an unsupervised approach to extract maritime Patterns of Life (PoL) from historical Automatic Identification System (AIS) data based on a low-dimensional synthetic representation of ship routes. Recent advances in long-term vessel motion modeling through Ornstein-Uhlenbeck mean-reverting stochastic processes allow to encode knowledge about maritime traffic via a compact graph-based model where waypoints are graph vertices and the connections between them, i.e., the navigational legs, are graph edges. The resulting directed graph ultimately leads to the detection and statistical characterization of recurrent maritime traffic patterns. To demonstrate its effectiveness and applicability to real-world case studies, the proposed methodology has been tested on two extensive AIS datasets, collected in the areas of two operational trials of EU-H2020’s MARISA (Maritime Integrated Surveillance Awareness) project.

[1]  Peter Willett,et al.  Anomaly Detection and Tracking Based on Mean–Reverting Processes with Unknown Parameters , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Paolo Braca,et al.  Scalable distributed change detection and its application to maritime traffic , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[3]  Paolo Braca,et al.  Modeling vessel kinematics using a stochastic mean-reverting process for long-term prediction , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[4]  Nicola Forti,et al.  Hybrid Bernoulli Filtering for Detection and Tracking of Anomalous Path Deviations , 2018, 2018 21st International Conference on Information Fusion (FUSION).

[5]  E. S. Page.  CONTINUOUS INSPECTION SCHEMES , 1954 .

[6]  Mark R. Morelande,et al.  Statistical analysis of motion patterns in AIS Data: Anomaly detection and motion prediction , 2008, 2008 11th International Conference on Information Fusion.

[7]  Maarten van Someren,et al.  Machine learning for vessel trajectories using compression, alignments and domain knowledge , 2012, Expert Syst. Appl..

[8]  Michele Vespe,et al.  Vessel Pattern Knowledge Discovery from AIS Data: A Framework for Anomaly Detection and Route Prediction , 2013, Entropy.

[9]  Allen M. Waxman,et al.  Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness , 2006, 2006 9th International Conference on Information Fusion.

[10]  Giuliana Pallotta,et al.  Maritime Traffic Networks: From Historical Positioning Data to Unsupervised Maritime Traffic Monitoring , 2018, IEEE Transactions on Intelligent Transportation Systems.

[11]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[12]  Paolo Braca,et al.  Consistent Estimation of Randomly Sampled Ornstein–Uhlenbeck Process Long-Run Mean for Long-Term Target State Prediction , 2016, IEEE Signal Processing Letters.

[13]  Paolo Braca,et al.  Detecting Anomalous Deviations From Standard Maritime Routes Using the Ornstein–Uhlenbeck Process , 2018, IEEE Transactions on Signal Processing.

[14]  Paolo Braca,et al.  Multiple Ornstein–Uhlenbeck Processes for Maritime Traffic Graph Representation , 2018, IEEE Transactions on Aerospace and Electronic Systems.