A main objective of BMDO is to reduce the dependency of the optical mid-course discrimination algorithms on a priori information. Existing algorithms use average intensity, modulation, signature trends and frequency content as features, which are very sensitive to both geometry and training assumptions and impose large storage requirements on an operational system. Also features like average intensity may be easily masked using simple countermeasures. PRA is proposing to use a physics model in conjunction with an estimation procedure to extract, in real-time from optical signatures, dynamics-based features such as coning angle, angular momentum vector and precession rate. These features reduce the dependency of discrimination on a priori information, make discrimination less susceptible to countermeasures and also simplify the training process. The estimator will use a physics model to iterate on a set of dynamics-based parameters until the sensor intensity measurements are best matched. In Phase I PRA will develop the models to be used by the real-time estimation algorithm to predict intensity measurements, incorporate the model into the estimator, demonstrate the feasibility of extracting dynamics-based features from infrared sensor measurements and show the performance benefits obtained by using these features in an discrimination example. Anticipated Benefits/Commercial Applications: The immediate benefit will be to make mid-course discrimination algorithms more robust by reducing their dependency on a priori information and their susceptibility to countermeasures. This has direct utility for systems such as SBIRS Low as well as GBI. The model-based estimator developed under this SBIR offers the commercial potential of developing a programmable logic array that would be a key product in a low cost interceptor system.
Keywords: Optical Discrimination, Model-Based Features, Kalman Filtering, Midcourse Discrimination, Estimation Theory, Physics Modeling
