Signal processing with alpha-stable distributions and applications
Signal processing with alpha-stable distributions and applications
Computing Symmetric Rank-Revealing Decompositions via Triangular Factorization
SIAM Journal on Matrix Analysis and Applications
A sparse signal reconstruction perspective for source localization with sensor arrays
IEEE Transactions on Signal Processing - Part II
Sparse solutions to linear inverse problems with multiple measurement vectors
IEEE Transactions on Signal Processing
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Rotation-invariant texture retrieval with gaussianized steerable pyramids
IEEE Transactions on Image Processing
| Hi-index | 0.08 |
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.