Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness

Neurobiologically inspired algorithms have been developed to continuously learn behavioral patterns at a variety of conceptual, spatial, and temporal levels. In this paper, we outline our use of these algorithms for situation awareness in the maritime domain. Our algorithms take real-time tracking information and learn motion pattern models on-the-fly, enabling the models to adapt well to evolving situations while maintaining high levels of performance. The constantly refined models, resulting from concurrent incremental learning, are used to evaluate the behavior patterns of vessels based on their present motion states. At the event level, learning provides the capability to detect (and alert) upon anomalous behavior. At a higher (inter-event) level, learning enables predictions, over pre-defined time horizons, to be made about future vessel location. Predictions can also be used to alert on anomalous behavior. Learning is context-specific and occurs at multiple levels: for example, for individual vessels as well as classes of vessels. Features and performance of our learning system using recorded data are described