Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns

Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns

We consider the sparse recovery problem of signals with an unknown clustering pattern in the context of multiple measurement vectors (MMVs) using the compressive sensing (CS) technique. For many MMVs in practice, the solution matrix exhibits some sort of clustered sparsity pattern, or clumpy behavio...

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Bibliographic Details
Main Authors: Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Format: Article
Language:English
Published: MDPI AG 2019-03-01
Series:Entropy
Subjects:
compressed sensing (CS)
sparse Bayesian learning (SBL)
joint sparsity
cluster structured sparsity
single measurement vector (SMV)
multiple measurement vectors (MMVs)
Online Access:http://www.mdpi.com/1099-4300/21/3/247

Internet

http://www.mdpi.com/1099-4300/21/3/247

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