Unsupervised Learning of Maritime Traffic Patterns for Anomaly Detection
نویسندگان
چکیده
Maritime anomaly detection requires an efficient representation and consistent knowledge of vessel behaviour. Automatic Identification System (AIS) data provides ships state vector and identity information that is here used to automatically derive knowledge of maritime traffic in an unsupervised way. The proposed approach only utilises AIS data, historical or real-time, and is aimed at incrementally learning motion patterns without any specific a priori contextual description. This can be applied to a single AIS terrestrial receiver, to regional networks or to global scale tracking. The maritime traffic representation underpins lowlikelihood behaviour detection and supports enhanced Maritime Situational Awareness by providing a characterisation of vessels traffic.
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