Sensors and Surveillance: Fusion-Generated Target Discrimination for BMDO Sensor and Surveillance Systems

ORINCON's Fusion-Generated Target Discrimination effort will develop innovative algorithms for Project Hercules and the Missile Defense Agency (MDA) that address complex NMD technological hurdles in the areas of discrimination, tracking, and decisionsupport. Proven multiple-hypothesis fusion and extended Kalman filter algorithms provide super-resolution of target cloud objects by removing multiple-sensor pointing error biases. Elimination of these systematic pointing errors enables subsequentdevelopment of feature phenomenology algorithms to identify individual objects within the target cloud. Feature phenomenology algorithms, operating in real time, measure and extract key features such as object length change, object ballistic extension,and object spin rates. In this way, it is possible to discriminate among warhead(s), decoys, and penetration aids in short detect-to-kill times.To accumulate target cloud evidence, the current Project Hercules decision support architecture delays engagement decisions as long as possible. ORINCON proposes to investigate relaxation methodologies in a temporal Bayes net to develop an architecture tomake faster, better engagement decisions.As future elements of the NMD