Sparse recovery method for order-of-magnitude reduction in RCS measurments

There are two national facilities in the US that can meet the stringent RCS test requirements of the government for classification, size and fidelity of the data. Characterization of each object takes a long time and slows down research and development. Therefore, innovative methods are needed for reducing the time spent in the chamber without compromising the fidelity of the data. Presented here are direct and statistical methods to arrive at a solution. Direct methods such as spare recovery making use of the range-aspect profiles of the objects demonstrates potential to be useful but Machine Learning (ML) algorithms were also employed to provide relief. Optical measurements were used to augment the methods and the presented solution has high potential of success in reducing the chamber time by 90%.