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A Robust Algorithm for Joint-Sparse Recovery
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DOI：10.1109/LSP.2009.2028107.png)
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Abstract
En 中文
We address the problem of finding a set of sparse signals that have nonzero coefficients in the same locations from a set of their compressed measurements. A mixed l(2,0) norm optimization approach is considered. A cost function appropriate to the joint-sparse problem is developed, and an algorithm is derived. Compared to other convex relaxation based techniques, the results obtained by the proposed method show a clear improvement in both noiseless and noisy environments.
Keywords:
Basis pursuit
compressive sampling
joint-sparse
multiple measurement vectors
sparse representation
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W
Organization
Cited Papers
Algorithms for simultaneous sparse approximation. Part II: Convex relaxation.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
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