Probabilistic associative learning of vessel motion patterns at multiple spatial scales for maritime situation awareness

Abstract

An improved neurobiologically inspired algorithm for situation awareness in the maritime domain is presented, which takes real-time tracking information and learns 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. Improvement to the associative learning law for learning temporal associations between vessel events enables conditional probabilities between events to be learned incrementally and locally. This allows weights in the learned model to be interpreted more readily, enabling better location prediction performance. Improvement in prediction performance is achieved by using multiple spatial scales to represent position, enabling the most relevant spatial scale to be used for local vessel behavior. Features and performance of these updates to the learning system using recorded data are described.