Achieving high resolution for super-resolution via reweighted atomic norm minimization

The super-resolution theory developed recently by Candès and Fernandes-Granda aims to recover fine details in a sparse frequency spectrum from coarse scale information. The theory was then extended to the cases of compressive samples and/or multiple measurement vectors. However, the existing atomic norm (or total variation norm) techniques succeed only if the frequencies are sufficiently separated, prohibiting commonly known high resolution. In this paper, a reweighted atomic-norm minimization (RAM) approach is proposed which iteratively carries out atomic norm minimization (ANM) with a sound reweighting strategy that enhances sparsity and resolution. It is demonstrated analytically and via numerical simulations that the proposed method achieves high resolution with application to DOA estimation.