Results 41 to 50 of about 3,975 (201)
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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A Dictionary-Based Pursuit Algorithm for Magnetotelluric Signal-Noise Separation
IEEE AccessA critical challenge in magnetotelluric (MT) studies is the effective suppression of noise in collected data prior to investigating deep geological structures and detecting deep-seated blind ore bodies.
Jin Cai, Jianhua Cai
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On the Noise Robustness of Simultaneous Orthogonal Matching Pursuit
IEEE Transactions on Signal Processing, 2017 In this paper, the joint support recovery of several sparse signals whose supports present similarities is examined. Each sparse signal is acquired using the same noisy linear measurement process, which returns fewer observations than the dimension of the sparse signals.
Jean-François Determe +3 more
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Comparison Of Orthogonal Matching Pursuit Implementations
, 2012 Publication in the conference proceedings of EUSIPCO, Bucharest, Romania ...
Bob L. T. Sturm +1 more
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IET Networks, 2019 This study proposes a construction method of deterministic measurement matrix based on a correlation criterion. On this basis, the authors investigated the massive multiple‐input and multiple‐output (MIMO) channel estimation algorithm using the ...
Ruijin Ma, Huisheng Zhang
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Orthogonal Matching Pursuit: A Brownian Motion Analysis [PDF]
IEEE Transactions on Signal Processing, 2012 A well-known analysis of Tropp and Gilbert shows that orthogonal matching pursuit (OMP) can recover a k-sparse n-dimensional real vector from 4 k log(n) noise-free linear measurements obtained through a random Gaussian measurement matrix with a probability that approaches one as n approaches infinity. This work strengthens this result by showing that a
Alyson K. Fletcher, Sundeep Rangan
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An Optimal Condition for the Block Orthogonal Matching Pursuit Algorithm
IEEE Access, 2018 Recovery of the support of a block K-sparse signal x from a linear model y = Ax + v, where A is a sensing matrix and v is a noise vector, arises from many applications.
Jinming Wen +2 more
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