Wen, J.M., Zhou, Z.C., Liu, Z.L., Lai, M.J. and Tang, X.H. (2019) Sharp Sufficient Conditions for Stable Recovery of Block Sparse Signals by Block Orthogonal Matching Pursuit. Applied and Computational Harmonic Analysis, 47, 948-974.
https://doi.org/10.1016/j.acha.2018.02.002

has been cited by the following article:

• TITLE: Some Results for Exact Support Recovery of Block Joint Sparse Matrix via Block Multiple Measurement Vectors Algorithm

  AUTHORS: Yingna Pan, Pingping Zhang

  KEYWORDS: Support Recovery, Compressed Sensing, Block Multiple Measurement Vectors Algorithm, Block Restricted Isometry Property

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.11 No.4, April 28, 2023

  ABSTRACT: Block multiple measurement vectors (BMMV) is a reconstruction algorithm that can be used to recover the support of block K-joint sparse matrix X from Y = ΨX + V. In this paper, we propose a sufficient condition for accurate support recovery of the block K-joint sparse matrix via the BMMV algorithm in the noisy case. Furthermore, we show the optimality of the condition we proposed in the absence of noise when the problem reduces to single measurement vector case.