Classification of Intermediate Range Missiles During Launch

This paper describes a Deep Learning Neural Network (DNN) approach to intermediate range missile system rapid classification during engagement. This paper is an update on a strategy to develop live fire analysis capability starting with the idea that if a known set of missile classes is to be fired, the measured telemetry of a missile from the given class can be used to rapidly determine which of the classes of missiles is flying. The ultimate goal of this work is to rapidly identify characteristics of unknown missiles during flight. These initial steps rely on characterizations derived from large numbers of flight histories or simulated trajectories identified by class. Deep learning neural networks were applied, as well as, other statistical learning procedures, which yielded extremely accurate class predictions on both non-noisy and noisy fly-outs.