Resolution analysis in signal recovery

Resolution analysis for the problem of signal recovery from finitely many linear samples is the subject of the paper. The classical Rayleigh limit serves only as a lower bound on resolution since it does not assume any recovery strategy and is based only on observed data. The authors show that details finer than the Rayleigh limit can be recovered by simple linear processing that incorporates prior information. They first define a measure of resolution based on allowable levels of error that is more appropriate for current signal recovery strategies than the Rayleigh definition. In the practical situation in which only finitely many noisy observations are available, one must restrict the class of signals in order to make the resolution measure meaningful. They consider the set of bandlimited and essentially time-limited signals since it describes most signals encountered in practice. For this set they show how to precompute resolution limits from knowledge of measurement functionals, signal-to-noise ratio, passband, energy concentration regions, energy concentration factor, and a prescribed level of error tolerance. In the process they also derive an algorithm for high resolution signal recovery. They illustrate the results with an example