Recovery Conditions of Sparse Signals Using Orthogonal Least Squares-Type Algorithms
Author:
Affiliation:
1. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China
2. Department of Electrical and Computer Engineering, George Mason University, Fairfax, VA, USA
Funder
National Natural Science Foundation of China
National Science Foundation
Virginia Research Investment Fund CCI
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://ieeexplore.ieee.org/ielam/78/9675017/9901463-aam.pdf
Reference48 articles.
1. $\ell_{2}/\ell_{1}$-Optimization in Block-Sparse Compressed Sensing and Its Strong Thresholds
2. Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise
3. Optimal Restricted Isometry Condition of Normalized Sampling Matrices for Exact Sparse Recovery With Orthogonal Least Squares
4. Block sparsity and sampling over a union of subspaces
5. Robust Recovery of Signals From a Structured Union of Subspaces
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