Threat assessment using context-based tracking in a maritime environment

The main objective of this work is to model and exploit available maritime contextual information to provide a hypothesis on suspicious vessel maneuvers. This concept involves utilizing the L1 tracking to perform L2/L3 data fusion, i.e. refinement and assessment for situations and threats. A new context-based tracker known as the ConTracker is developed. The purpose of the ConTracker is to incorporate the contextual information into a traditional alpha - beta tracker in such a way so that it provides a repeller or an attractor characteristics to a specific region of interest. Any behavior of the vessel that is inconsistent with the repeller or the attractor characteristics of the current vessel location would be classified as suspicious. Such an inconsistent vessel behavior would be directly indicated by a high measurement residual which may be used to estimate an accurate process noise covariance using a multiple-model adaptive estimator. Based on the rate of change of the estimated process noise covariance values, an L2/L3 hypothesis generator red-flags the target vessel. Simulation results indicate that the context based tracking enhances the reliability of erratic maneuver detection.