Bayesian Compressive Sensing of Sparse Signals with Unknown Clustering Patterns

Clicks: 186
ID: 117910
2019
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Abstract
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 amount of clumpiness in the supports of the solution to improve the recovery performance of sparse signals with an unknown clustering pattern. This parameter does not exist in the other existing algorithms and is learned via our hierarchical SBL algorithm. While the proposed algorithm is constructed for the MMVs, it can also be applied to the single measurement vector (SMV) problems. Simulation results show the effectiveness of our algorithm compared to other algorithms for both SMV and MMVs.
Reference Key
gunther2019entropybayesian Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Mohammad Shekaramiz,Todd K. Moon,Jacob H. Gunther;Mohammad Shekaramiz;Todd K. Moon;Jacob H. Gunther;
Journal entropy
Year 2019
DOI
10.3390/e21030247
URL
https://sciprofiles.com/publication/view/e257397858b5d1d9d03637f561a77730
Keywords
compressed sensing (cs) joint sparsity sparse bayesian learning (sbl) cluster structured sparsity multiple measurement vectors (mmvs) single measurement vector (smv) compressed sensing (cs) joint sparsity sparse bayesian learning (sbl) cluster structured sparsity multiple measurement vectors (mmvs) single measurement vector (smv)

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