Novel Algorithm for Sparse Solutions to Linear Inverse Problems with Multiple Measurements

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dc.creatorLi, Lianlin
dc.creatorLi, Fang
dc.date2009-05-20
dc.date.accessioned2026-07-07T13:16:45Z
dc.date.available2026-07-07T13:16:45Z
dc.descriptionIn this report, a novel efficient algorithm for recovery of jointly sparse signals (sparse matrix) from multiple incomplete measurements has been presented, in particular, the NESTA-based MMV optimization method. In a nutshell, the jointly sparse recovery is obviously superior to applying standard sparse reconstruction methods to each channel individually. Moreover several efforts have been made to improve the NESTA-based MMV algorithm, in particular, (1) the NESTA-based MMV algorithm for partially known support to greatly improve the convergence rate, (2) the detection of partial (or all) locations of unknown jointly sparse signals by using so-called MUSIC algorithm; (3) the iterative NESTA-based algorithm by combing hard thresholding technique to decrease the numbers of measurements. It has been shown that by using proposed approach one can recover the unknown sparse matrix X with () Spark A -sparsity from () Spark A measurements, predicted in Ref. [1], where the measurement matrix denoted by A satisfies the so-called restricted isometry property (RIP). Under a very mild condition on the sparsity of X and characteristics of the A, the iterative hard threshold (IHT)-based MMV method has been shown to be also a very good candidate.
dc.identifierhttps://arxiv.org/abs/0905.3245
dc.identifierhttp://arxiv.org/abs/0905.3245
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/230894
dc.subjectInformation Theory
dc.titleNovel Algorithm for Sparse Solutions to Linear Inverse Problems with Multiple Measurements
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