Name
Title | ||
|---|---|---|
Covariation-based subspace-augmented MUSIC for joint sparse support recovery in impulsive environments |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.sigpro.2012.11.021 | Signal Processing |
Keywords | Field | DocType |
joint sparse support recovery,subspace-augmented music technique,sparse bayesian,impulsive environment,joint sparse support,recovery performance,signal ensemble,additive impulsive noise,higher recovery rate,covariation-based subspace-augmented music,subspace-augmented music,music,compressive sensing | Bayesian inference,Pattern recognition,Subspace topology,Signal statistics,Computer science,Sparse approximation,Artificial intelligence,Compressed sensing | Journal |
Volume | Issue | ISSN |
93 | 5 | 0165-1684 |
Citations | PageRank | References |
2 | 0.38 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| George Tzagkarakis | 1 | 139 | 17.94 |
| P. Tsakalides | 2 | 954 | 120.69 |
| Jean-Luc Starck | 3 | 1183 | 122.27 |