Midcourse Target Identification (MCTID) Algorithm for Midcourse Threats Using Bayesian-Topological Feature Fusion and Classification

The Midcourse Target Identification (MCTID) Algorithm for Midcourse Threats Using Bayesian-Topological Feature Fusion and Classification program will extend the previously developed Enhanced Target Identification (ETID) algorithm developed under a previous MDA program to the midcourse threat regime. ETID was built to identify boost phase threats by performing both topological threat classification and Bayesian probabilistic threat classification, and then fusing the two classification results. The ETID achieved a predicted 95% classification accuracy for a broad range of possible strategic and theater threat boosters. The MCTID will employ the same dual mode classification and target ID approach, but apply it to midcourse threat and decoy types. The goal of the MCTID is a 99% accuracy of correct classification. The proposed program will develop the algorithm, test it against real and simulated data, and report the results. The algorithm development will utilize dedicated software coding, as well as generalized COTS software packages for building the topological and Bayesian classifiers.