Results 31 to 40 of about 3,975 (201)
Xibei Gongye Daxue Xuebao, 2020 Efficient localization of multiple targets is one of the basic technical problems in wireless sensor networks (WSN). The traditional sparse representation method based on greedy class is not efficient in multi-target positioning.
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A reduced-complexity compressed sensing channel estimation for underwater acoustic channel
Dianxin kexue, 2021 Aiming at the sparse characteristics of underwater acoustic channels for shallow seas, a reduced-complexity look-ahead backtracking orthogonal matching pursuit (RC-LABOMP) channel estimation algorithm was proposed.Firstly, two types of support sets of ...
Xuan YU, Xuan GENG
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Efficiency of Orthogonal Matching Pursuit for Group Sparse Recovery
Axioms, 2023 We propose the Group Orthogonal Matching Pursuit (GOMP) algorithm to recover group sparse signals from noisy measurements. Under the group restricted isometry property (GRIP), we prove the instance optimality of the GOMP algorithm for any decomposable ...
Chunfang Shao +3 more
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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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Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. [PDF]
Adv Neural Inf Process Syst, 2014 Knudson KC, Yates JL, Huk AC, Pillow J.
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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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