Paper Info
Title
Compressive Sensing of Temporally Correlated Sources Using Isotropic Multivariate Stable Laws.
Abstract
This paper addresses the problem of compressively sensing a set of temporally correlated sources, in order to achieve faithful sparse signal reconstruction from noisy multiple measurement vectors (MMV). To this end, a simple sensing mechanism is proposed, which does not require the restricted isometry property (RIP) to hold near the sparsity level, whilst it provides additional degrees of freedom to better capture and suppress the inherent sampling noise effects. In particular, a reduced set of MMVs is generated by projecting the source signals onto random vectors drawn from isotropic multivariate stable laws. Then, the correlated sparse signals are recovered from the random MMVs by means of a recently introduced sparse Bayesian learning algorithm. Experimental evaluations on synthetic data with varying number of sources, correlation values, and noise strengths, reveal the superiority of our proposed sensing mechanism, when compared against well -established RIP -based compressive sensing schemes.
Year
DOI
Venue
2018
10.23919/EUSIPCO.2018.8553290
European Signal Processing Conference
Keywords
Field
DocType
Compressive sensing,correlated sources,isotropic multivariate stable laws,sparse Bayesian learning
Bayesian inference,Noise measurement,Computer science,Synthetic data,Sampling (statistics),Law,Restricted isometry property,Compressed sensing,Sparse matrix,Signal reconstruction
Conference
ISSN
Citations 
PageRank 
2076-1465
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
George Tzagkarakis113917.94
John P. Nolan200.68
P. Tsakalides3954120.69