Analog Image Processing Algorithms for Target Discrimination

During Phase II, Computational Sensors Corporation (CSC) proposes to enhance the discrimination algorithms developed under Phase I and implement them in the CSC analog spatio-temporal filtering system. The hardware will be used to accelerate searches and to perform HWIL parametric studies of target discrimination problems. During Phase I, CSC demonstrated successful discrimination using a wavelet best basis search algorithm where wavelet features were calculated via simulation and objects grouped by features using linear discrimination theory. Discrimination relies on computing multiple useful target features based upon known target characteristics. Discrimination improvements gained by calculating ever more specific features that rely heavily on a priori knowledge leads to over-training and an over- reliance on intelligence. Under a Phase II effort, CSC will evaluate a more general, complete set of features derived directly from a large wavelet filter bank using best basis search methods to reduce the reliance on a priori information to effect target discrimination. At the conclusion of Phase II, a method for designing optimal wavelet filter banks for target discrimination will have been developed, tested, and implemented in real-time hardware. System development suitable for interceptor integration is planned in Phase III.