Enhanced resolution and modeling of exponential signals

A signal enhancement algorithm is described which achieves the objective of modification of the given data set that then serves as a cleansing process whereby corrupting noise, measurement distortion, or theoretical mismatch present in the given data set is removed. Particular attention is directed toward properties that are describable using a singular value decomposition of a data generated matrix. An example is given demonstrating a significant improvement in the performance of subspace-based frequency estimation techniques. The algorithm is shown to provide a useful means for solving a variety of important signal processing problems. It has been successfully applied to the missing data problem, deconvolution, high-dimensional filter synthesis, and image synthesis