Adaptive Identification and Recovery of Jointly Sparse Vectors
نویسندگان
چکیده
منابع مشابه
Efficient Recovery of Jointly Sparse Vectors
We consider the reconstruction of sparse signals in the multiple measurement vector (MMV) model, in which the signal, represented as a matrix, consists of a set of jointly sparse vectors. MMV is an extension of the single measurement vector (SMV) model employed in standard compressive sensing (CS). Recent theoretical studies focus on the convex relaxation of the MMV problem based on the (2, 1)-...
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While single measurement vector (SMV) models have been widely studied in signal processing, there is a surging interest in addressing the multiple measurement vectors (MMV) problem. In the MMV setting, more than one measurement vector is available and the multiple signals to be recovered share some commonalities such as a common support. Applications in which MMV is a naturally occurring phenom...
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An iterative reweighted algorithm is proposed for the recovery of jointly sparse vectors from multiple-measurement vectors (MMV). The proposed MMV algorithm is an extension of the iterative reweighted 1 algorithm for single measurement problems. The proposed algorithm (M-IRL1) is demonstrated to outperform non-reweighted MMV algorithms under noiseless measurements. A regularization of the M-IRL...
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The standard compressive sensing (CS) aims to recover sparse signal from single measurement vector which is known as SMV model. By contrast, recovery of sparse signals from multiple measurement vectors is called MMV model. In this paper, we consider the recovery of jointly sparse signals in the MMV model where multiple signal measurements are represented as a matrix and the sparsity of signal o...
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The problem of jointly sparse support recovery is to determine the common support of jointly sparse signal vectors from multiple measurement vectors (MMV) related to the signals by a linear transformation. The fundamental limit of performance has been studied in terms of a so-called algebraic bound, relating the maximum recoverable sparsity level to the spark of the sensing matrix and the rank ...
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2014
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2013.2288679