Reconstruction of low-rank jointly sparse signals from multiple measurement vectors

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From: Signal, Image and Video Processing(Vol. 13, Issue 4)
Publisher: Springer
Document Type: Report
Length: 217 words

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Byline: Mehrrad Mehrkam (1), Mohammad Ali Tinati (1), Tohid Yousefi Rezaii (1) Keywords: Compressive sensing; Multiple measurement vectors; Joint sparsity; Rank deficiency Abstract: The multiple measurement vectors problem approximates a set of signals sharing a common sparsity pattern simultaneously, using different linear combinations of those signals obtained through a sensing matrix. In a situation where the signal matrix has full row-rank, MUltiple SIgnal Classification (MUSIC) algorithm guarantees to recover the jointly sparse signals, but for the rank-defective case, the MUSIC performance is voided. To address such a rank deficient case, our proposed method, line search low-rank jointly sparse signals (LS-LRJSS), provides a geometric analysis of the problem by characterizing the linear dependence of the measurements with the linear coefficients that permit the reconstruction of each point from its neighbors. Moreover, a subspace analysis has been done on a Grassmann manifold to obtain the subspace that the signal matrix belongs to. Several numerical experiments evidence that the proposed method is more accurate and less time-consuming compared to existing approaches especially wherever the sparsity level of the signals increases or the number of measurement vectors decreases. Author Affiliation: (1) 0000 0001 1172 3536, grid.412831.d, Department of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran Article History: Registration Date: 17/11/2018 Received Date: 18/01/2018 Accepted Date: 17/11/2018 Online Date: 12/12/2018

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Gale Document Number: GALE|A584335350