A strong need exists to protect the US, its deployed forces and allies against all ranges of enemy ballistic missiles in all phases of flight. Current methodologies tend to use prior fixed measurements and features for identifying missiles and cannot adapt/change decision logic based on new information collected during an engagement which is critical as tactical operational environments are often different from those used to collect or generate sample data. Our proposed solution entails a novel adaptable cognitive algorithm approach that synthesizes current signature information from the radar and auxiliary information, such as DTED/GIS, target operating characteristics, prior flight profiles, etc., to provide feedback, through a sensor resource manager, to empower the adaptive modification of the next radar collection mode ensuring that the future collected signature will aid in maintaining or increasing the confidence level of the current target identification. The target recognition paradigm uses a blend of template-, feature- and model-based paradigms blended through a hierarchical information tree approach. As most research today focuses on prior collection data and known system parameters, our innovative adaptive cognitive approach will enable high-confidence target identification reporting through the provision of a robust architecture that excels under multiple normal and extenuating operating conditions. Approved for Public Release | 16-MDA-8951 (15 December 16)
