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Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Bayesian Compressive Sensing Of Sparse Signals With Unknown Clustering Patterns, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
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 behavior, along each column, as well as joint sparsity across the columns. In this paper, we propose a new sparse Bayesian learning (SBL) method that incorporates a total variation-like prior as a measure of the overall clustering pattern in the solution. We further incorporate a parameter in this prior to account for the emphasis on the …
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Details On O-Sbl(Mcmc): A Compressive Sensing Algorithm For Sparse Signal Recovery For The Smv/Mmv Problem Using Sparse Bayesian Learning And Markov Chain Monte Carlo Inference, Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
Electrical and Computer Engineering Faculty Publications
This report provides details on O-SBL(MCMC) algorithm for the recovery of jointly-sparse signals for the multiple measurement vector (MMV) problem. For the MMVs with this structure, the solution matrix, which is a collection of sparse vectors, is expected to exhibit joint sparsity across the columns. The notion of joint sparsity here means that the columns of the solution matrix share common supports. This algorithm employs a sparse Bayesian learning (SBL) model to encourage the joint sparsity structure across the columns of the solution. While the proposed algorithm is constructed for the MMV problems, it can also be applied to the …