MARITIME DOMAIN AWARENESS BY ANOMALY DETECTION LEVERAGING TRACK INFORMATION
Kragh, Frank E.
Scrofani, James W.
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Techniques for anomaly detection in the maritime domain are developed in this thesis using an area metric that measures the degree of similarity, or distinction, between ships’ tracks using the area between ships’ tracks. A modified k-means algorithm is applied to the areas calculated between ships tracks to extract sea lanes and classify these ships’ tracks into sea lanes. Bayes’ theorem of conditional probabilities is used to calculate the probability of ship tracks being in different sea lanes. Two types of anomaly detection are examined in this thesis. Ships not sailing in standard sea lanes are detected using Bayes’ theorem, and ships following one another are detected using user-defined thresholds on the minimum area between sea tracks. The development of the metric that compares ships’ track data using area is the most significant result of this thesis.
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