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Achieving Robust Compressive Sensing Seismic Acquisition with a Two-Step Sampling Approach [PDF]
Sensors, 2023 The compressive sensing (CS) framework offers a cost-effective alternative to dense alias-free sampling. Designing seismic layouts based on the CS technique imposes the use of specific sampling patterns in addition to the logistical and geophysical ...
Anna Titova +2 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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Linear transformations and Restricted Isometry Property [PDF]
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009 The Restricted Isometry Property (RIP) introduced by Candés and Tao is a fundamental property in compressed sensing theory. It says that if a sampling matrix satisfies the RIP of certain order proportional to the sparsity of the signal, then the original signal can be reconstructed even if the sampling matrix provides a sample vector which is much ...
Leslie Ying, Yi Ming Zou
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On the restricted isometry property of the Paley matrix [PDF]
Linear Algebra and its Applications, 2021 12 pages.
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Decay Properties of Restricted Isometry Constants [PDF]
IEEE Signal Processing Letters, 2009 Many sparse approximation algorithms accurately recover the sparsest solution to an underdetermined system of equations provided the matrix's restricted isometry constants (RICs) satisfy certain bounds. There are no known large deterministic matrices that satisfy the desired RIC bounds; however, members of many random matrix ensembles typically satisfy
Jeffrey D. Blanchard +2 more
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Applied Sciences, 2023 Currently, the use of microseismic detection technology for crack detection and localization in rock masses has great potential in detecting structural damage.
Jinfeng Chen, Meng Mei
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A Method of Reweighting the Sensing Matrix for Compressed Sensing
IEEE Access, 2021 In compressed sensing, a small enough restricted isometry constant (RIC) of the sensing matrix satisfying the restricted isometry property (RIP) is the powerful guarantee on the precise reconstruction of a sparse discrete signal.
Lei Shi, Gangrong Qu, Qian Wang
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Suprema of Chaos Processes and the Restricted Isometry Property [PDF]
Communications on Pure and Applied Mathematics, 2014 We present a new bound for suprema of a special type of chaos process indexed by a set of matrices, which is based on a chaining method. As applications we show significantly improved estimates for the restricted isometry constants of partial random circulant matrices and time‐frequency structured random matrices.
Krahmer, Felix +2 more
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An Armijo-Type Hard Thresholding Algorithm for Joint Sparse Recovery
IEEE Access, 2021 Joint sparse recovery (JSR) in compressed sensing simultaneously recovers sparse signals with a common sparsity structure from their multiple measurement vectors obtained through a common sensing matrix.
Lili Pan, Xunzhi Zhu
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Restricted Isometry Property for General p-Norms [PDF]
IEEE Transactions on Information Theory, 2016 The Restricted Isometry Property (RIP) is a fundamental property of a matrix which enables sparse recovery. Informally, an $m \times n$ matrix satisfies RIP of order $k$ for the $\ell_p$ norm, if $\|Ax\|_p \approx \|x\|_p$ for every vector $x$ with at most $k$ non-zero coordinates.
Zeyuan Allen Zhu +2 more
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