Abstract

The performance of existing approaches to the recovery of frequency-sparse signals from compressed measurements is limited by the coherence of required sparsity dictionaries and the discretization of frequency parameter space. In this pa-per, we adopt a parametric joint recovery-estimation method based on model selection in spectral compressive sensing. Numerical experiments show that our approach outperforms most state-of-the-art spectral CS recovery approaches in fi-delity, tolerance to noise and computation efficiency. Index Terms — Compressive sensing, frequency-sparse signal, model selection, parametric estimation, maximum likelihood estimator 1.