Cognitively-Inspired Motion Pattern Learning & Analysis Algorithms for Higher-Level Fusion and Automated Scene Understanding

We have developed a suite of neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior. These capabilities contribute to higher-level fusion situational awareness and assessment objectives. They also provide essential elements for automated scene understanding to shift operator focus from sensor monitoring and activity detection to assessment and response. Our learning algorithms learn behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of track data to a rich set of information regarding their field of regard that supports decision-making and timely response initiation. Continuous incremental learning enables the models of normal behavior to adapt well to evolving situations while maintaining high levels of performance. Deviations from normalcy result in reports being published directly to operator displays or to other reasoning components within a larger system. Deviation tolerance levels are user settable during system operation to tune alerting sensitivity. Operator (or other system component) responses to anomaly alerts can be fed back into the algorithms to further enhance and refine learned models. These algorithms have been successfully demonstrated to learn vessel behaviors across the maritime domain and to learn vehicle and dismount behavior in land-based settings.