Results 31 to 40 of about 3,115 (252)
Orthogonal Matching Pursuit with Replacement
CoRR, 2011 In this paper, we consider the problem of compressed sensing where the goal is to recover almost all the sparse vectors using a small number of fixed linear measurements. For this problem, we propose a novel partial hard-thresholding operator that leads to a general family of iterative algorithms.
Prateek Jain 0002 +2 more
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Backward-Optimized Orthogonal Matching Pursuit Approach [PDF]
IEEE Signal Processing Letters, 2004 A recursive approach for shrinking coefficients of an atomic decomposition is proposed. The corresponding algorithm evolves so as to provide at each iteration 1) the orthogonal projection of a signal onto a reduced subspace and 2) the index of the coefficient to be disregarded in order to construct a coarser approximation minimizing the norm of the ...
Miroslav Andrle +2 more
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Tuning Free Orthogonal Matching Pursuit
CoRR, 2017 Orthogonal matching pursuit (OMP) is a widely used compressive sensing (CS) algorithm for recovering sparse signals in noisy linear regression models. The performance of OMP depends on its stopping criteria (SC). SC for OMP discussed in literature typically assumes knowledge of either the sparsity of the signal to be estimated $k_0$ or noise variance ...
Sreejith Kallummil, Sheetal Kalyani
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Peak Colocalized Orthogonal Matching Pursuit for Seismic Trace Decomposition
IEEE Access, 2020 Orthogonal matching pursuit (OMP) is an efficient method for decomposing a seismic trace with regard to an atom dictionary. The original OMP optimizes one unique single objective in terms of successively maximizing the inner product between an atom and ...
Yongqing Li, Jun Wang, Hui Li, Peng Ren
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An improved reconstruction algorithm based on compressed sensing for power quality analysis
Cogent Engineering, 2016 The application and analysis of compressive sensing theory in power quality has been received more and more attention. Reconstruction algorithm is one of the most important contents of the compressive sensing theory, and as one of the reconstruction ...
Quandang Ma +3 more
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A note on orthogonal matching pursuit under restricted isometry property
IET Signal Processing, 2022 The orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. The number of iterations required for the OMP algorithm to perform exact the recovery of sparse signals is a fundamental problem in signal ...
Xueping Chen +3 more
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Recovery of Sparse Signals via Modified Hard Thresholding Pursuit Algorithms
IET Signal Processing, 2023 In this paper, we propose a modified version of the hard thresholding pursuit algorithm, called modified hard thresholding pursuit (MHTP), using a convex combination of the current and previous points.
Li-Ping Geng +3 more
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Channel Estimation Method of OFDM System based on Compressed Sensing
Guangtongxin yanjiu, 2022 Aiming at the time-domain sparsity and unknown sparsity of wireless channels, compressed sensing technology is applied to the channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) system. This paper proposes a sparsity adaptive matching
LI Gui-yong +4 more
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Fast Non-Negative Orthogonal Matching Pursuit [PDF]
IEEE Signal Processing Letters, 2015 One of the important classes of sparse signals is the non-negative signals. Many algorithms have already been proposed to recover such non-negative representations, where greedy and convex relaxed algorithms are among the most popular methods. The greedy techniques have been modified to incorporate the non-negativity of the representations.
Yaghoobi, Mehrdad +2 more
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Journal of Low Frequency Noise, Vibration and Active Control, 2019 In order to extract fault impulse feature of large-scale rotating machinery from strong background noise, a sparse feature extraction method based on sparse decomposition combined multiresolution generalized S transform is proposed in this paper. In this
Baokang Yan +4 more
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