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Matrix Padding Method for Sparse Signal Reconstruction
Sabna N, P.R.Saseendran Pillai
Pages - 1 - 13     |    Revised - 31-1-2015     |    Published - 28-2-2015
Volume - 9   Issue - 1    |    Publication Date - January / February 2015  Table of Contents
Compressive Sensing, Greedy Algorithms, LMS Approximation, Relaxation Methods, Sparse Recovery, Sub-Nyquist Rate.
Compressive sensing has been evolved as a very useful technique for sparse reconstruction of signals that are sampled at sub-Nyquist rates. Compressive sensing helps to reconstruct the signals from few linear projections of the sparse signal. This paper presents a technique for the sparse signal reconstruction by padding the compression matrix for solving the underdetermined system of simultaneous linear equations, followed by an iterative least mean square approximation. The performance of this method has been compared with the widely used compressive sensing recovery algorithms such as l1_ls, l1-magic, YALL1, Orthogonal Matching Pursuit, Compressive Sampling Matching Pursuit, etc.. The sounds generated by 3-blade engine, music, speech, etc. have been used to validate and compare the performance of the proposed technique with the other existing compressive sensing algorithms in ideal and noisy environments. The proposed technique is found to have outperformed the l1_ls, l1-magic, YALL1, OMP, CoSaMP, etc. as elucidated in the results.
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Miss Sabna N
Department of Electronics, Cochin University of Science and Technology, Cochin 682 022, India - India
Professor P.R.Saseendran Pillai
Department of Electronics - India