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Title: Comparison of iterative sparse recovery algorithms
Other Titles: Yi?nelemeli? seyrek geri? oluşturma algori? tmalarinin karşilaştirilmasi
Authors: Karakuş, C.
Gürbüz, A. C.
Issue Date: 2011
Publisher: IEEE Computer Society
Abstract: Most signals can be represented sparsely in a basis. Recently, Compressive Sensing Theorem which offers convex optimization algorithms based on ?1-minimization for sparse signal recovery is often being used. In this paper, some of the iterative signal recovery algorithms alternative to ?1-minimization solution which are Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP), Iterative Hard Thresholding (IHT) and Lipschitz Iterative Hard Theresholding (LIHT) are compared in noisy and noiseless conditions with various tests. Iterative algorithms alternative to the ?1 optimization method with similar performance are verified. OMP algorithm that works at higher true reconstruction rates in noisy and noiseless conditions can be preferred instead of convex optimization methods. © 2011 IEEE.
ISBN: 9781457704635
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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