TitleMaritime Threat Detection Using Probabilistic Graphical Models
Publication TypeConference Proceedings
Year of Conference2012
AuthorsAuslander, B, Gupta, K, Aha, DW
Conference NameProceedings of the Twenty-Fifth Florida Artificial Intelligence Research Society Conference
Date Published12/2012
PublisherAAAI Press
Conference LocationMarco Island, FL

Maritime threat detection is a challenging problem because maritime environments can involve a complex combination of concurrent vessel activities, and only a small fraction of these may be irregular, suspicious, or threatening. Previous work on this task has been limited to analyses of single vessels using simple rule-based models that alert watchstanders when a proximity threshold is breached. We claim that Probabilistic Graphical Models (PGMs) can be used to more effectively model complex maritime situations. In this paper, we study the performance of PGMs for detecting (small boat) maritime attacks. We describe three types of PGMs that vary in their representational expressiveness and evaluate them on a threat recognition task using track data obtained from force protection naval exercises involving unmanned sea surface vehicles. We found that the best-performing PGMs can outperform the deployed rule-based approach on these tasks, though some PGMs require substantial engineering and are computationally expensive.

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machine learning
probabilistic graphical models
maritime threat assessment