Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6137
Title: A Recursive Approach to Reconstruction of Sparse Signals
Authors: Teke, Oğuzhan
Arıkan, Orhan
Gürbüz, Ali Cafer
Keywords: Compressive Sensing
Basis Mismatch
Recursive Solution
Issue Date: 2014
Publisher: IEEE
Source: 22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEY
Series/Report no.: Signal Processing and Communications Applications Conference
Abstract: Compressive Sensing (CS) theory details how a sparsely represented signal in a known basis can be reconstructed using less number of measurements. In many practical systems, the observation signal has a sparse representation in a continuous parameter space. This situation rises the possibility of use of the CS reconstruction techniques in the practical problems. In order to utilize CS techniques, the continuous parameter space have to be discretized. This discritization brings the well-known off-grid problem. To prevent the off-grid problem, this study offers a recursive approach which discritizes the parameter space in an adaptive manner. The simulations show that the proposed approach can estimate the parameters with a high accuracy even if targets are closely spaced.
URI: https://hdl.handle.net/20.500.11851/6137
ISBN: 978-1-4799-4874-1
ISSN: 2165-0608
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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