Results 51 to 60 of about 46,224 (313)
A new result on recovery sparse signals using orthogonal matching pursuit
Statistical Theory and Related Fields, 2022 Orthogonal matching pursuit (OMP) algorithm is a classical greedy algorithm widely used in compressed sensing. In this paper, by exploiting the Wielandt inequality and some properties of orthogonal projection matrix, we obtained a new number of ...
Xueping Chen +2 more
doaj +1 more source
Tuning Free Orthogonal Matching Pursuit
, 2017 13 pages.
Kallummil, Sreejith, Kalyani, Sheetal
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Batch Look Ahead Orthogonal Matching Pursuit [PDF]
2018 Twenty Fourth National Conference on Communications (NCC), 2018 Compressed sensing (CS) is a sampling paradigm that enables sampling signals at sub Nyquist rates by exploiting the sparse nature of signals. One of the main concerns in CS is the reconstruction of the signal after sampling. Many reconstruction algorithms have been proposed in the literature for the recovery of the sparse signals - Basis Pursuit ...
Muralikrishnna, GS +2 more
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Coherence-Based Performance Guarantees of Orthogonal Matching Pursuit
, 2012 In this paper, we present coherence-based performance guarantees of Orthogonal Matching Pursuit (OMP) for both support recovery and signal reconstruction of sparse signals when the measurements are corrupted by noise.
Calderbank, Robert, Chi, Yuejie
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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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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
doaj +1 more source
Efficient Implementations for Orthogonal Matching Pursuit [PDF]
Electronics, 2020 Based on the efficient inverse Cholesky factorization, we propose an implementation of OMP (called as version 0, i.e., v0) and its four memory-saving versions (i.e., the proposed v1, v2, v3 and v4). In the simulations, the proposed five versions and the existing OMP implementations have nearly the same numerical errors.
Hufei Zhu, Wen Chen, Yanpeng Wu
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IEEE transactions on intelligent transportation systems (Print), 2021 Time-delay estimation (TDE) for thin top layers of asphalt pavement is a challenging task due to the limited resolution of ground penetrating radar (GPR) as well as small permittivity difference between top layers.
Meng Sun +6 more
semanticscholar +1 more source
Signal-Dependent Performance Analysis of Orthogonal Matching Pursuit for Exact Sparse Recovery [PDF]
IEEE Transactions on Signal Processing, 2020 Exact recovery of $K$-sparse signals ${\boldsymbol{x}}\in \mathbb{R}^{n}$ from linear measurements ${\boldsymbol{y}}=\boldsymbol{A}{\boldsymbol{x}}$, where $\boldsymbol{A}\in \mathbb {R}^{m\times n}$ is a sensing matrix, arises from many applications ...
Jinming Wen, Rui Zhang, Wei Yu
semanticscholar +1 more source
Sparse Recovery with Orthogonal Matching Pursuit under RIP
, 2011 This paper presents a new analysis for the orthogonal matching pursuit (OMP) algorithm. It is shown that if the restricted isometry property (RIP) is satisfied at sparsity level $O(\bar{k})$, then OMP can recover a $\bar{k}$-sparse signal in 2-norm.
Zhang, Tong
core +1 more source