Covariation-based subspace-augmented MUSIC for joint sparse support recovery in impulsive environments

In this paper, we introduce a subspace-augmented MUSIC technique for recovering the joint sparse support of a signal ensemble corrupted by additive impulsive noise. Our approach uses multiple vectors of random compressed measurements and employs fractional lower-order moments stemming from modeling the underlying signal statistics with symmetric alpha-stable distributions. We show through simulations that the recovery performance of the proposed method is particularly robust for a wide range of highly impulsive environments. Our subspace-augmented MUSIC achieves higher recovery rates than a recently introduced sparse Bayesian learning algorithm, which was shown to outperform many state-of-the-art techniques for joint sparse support recovery. & 2012 Elsevier B.V. All rights reserved.