Intelligent Situation Awareness and Assessment Using Constrained Hierarchical Clustering and Temporal Fusion

Current methods for automated situation awareness and assessment are inadequate to provide a timely view of the situation. This deficiency is due to the flood of intelligence data, the incompleteness and contradictions in the data, and the difficulty in drawing conclusions about the situation. In this SBIR effort, ORINCON is proposing innovative techniques to exploit intelligence data to provide more timely situation awareness and assessment. ORINCON proposes to develop and apply the following innovative techniques: 1) hierarchical clustering of the major military groupings on the battlefield and 2) time-series analysis of changes in enemy equipment and position. Taken together, these techniques would provide a substantial advance in understanding the significance of changes in enemy position and equipment over time. The above techniques will undergo testing against representative scenarios to indicate their feasibility and performance. A final technical report will discuss the development and test results in terms of expected improvements over current situation awareness and assessment and provide recommendations for future development. In Phase II, a more extensive system will be implemented that will include a graphical user interface and interact with other software agents in a larger C4I workstation environment. Expanded scenarios will demonstrate the scalability and effectiveness of the approach. The techniques described in this proposal apply to domains where events occur over time and are reevaluated for their significance, and where plans to counteract threats change often. Examples include terrorist activities, drug interdiction, law enforcement, illegal immigration, and intelligent highway systems. The techniques can extend the characterization of groups and their movements to any level of complexity, thus promoting scalability.