default Traffic route extraction and anomaly detection (TREAD): vessel pattern knowledge discovery and exploitation for maritime situational awareness

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Traffic route extraction and anomaly detection (TREAD): vessel pattern knowledge discovery and exploitation for maritime situational awareness. Pallotta, Giuliana ; Vespe, Michele ; Bryan, Karna. CMRE-FR-2013-001. February 2013.

The knowledge of maritime traffic patterns is key to Maritime Situational Awareness (MSA) applications, in particular to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information can overwhelm human operators. Thus, automatic processing to synthesise the behaviours of interest in a clear and effective way is now required. Although AIS data reliably depicts the traffic related to large vessels only, it can be effectively used to infer different levels of contextual information, spanning from the characterisation of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime motion patterns is presented in this work to help convert MSA data to information enabling decisions support. Automatic processing techniques form the basis for detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection), deals with different levels of intermittency and persistence (i.e., time lag between subsequent observations), varying coverage performance, as well as ground and space based receivers.