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Block Sparse Vector Recovery for Compressive Sensing via $\ell_1-\alpha\ell_q$-minimization Model
  • Hongyan Shi,
  • Shaohua Xie,
  • Jiangtao Wang
Hongyan Shi
Yang-En University
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Shaohua Xie
Guangdong University of Technology Jieyang Institute of Technology

Corresponding Author:xsh1209490052@163.com

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Jiangtao Wang
Zhejiang Yuexiu University of Foreign Languages
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Abstract

This paper solves the problem of block sparse vector recovery using the block $\ell_1-\alpha\ell_q$- minimization model. Based on the block restricted isometry property (B-RIP) condition, we obtain exact block sparse vector recovery result. We also obtain the theoretical bound for the block $\ell_1-\alpha\ell_q$- minimization model when measurements are depraved by the noises.
30 Oct 2023Submitted to Electronics Letters
01 Nov 2023Submission Checks Completed
01 Nov 2023Assigned to Editor
11 Nov 2023Reviewer(s) Assigned