Author(s)

Yingna Pan^*, Pingping Zhang

School of Science, Chongqing University of Posts and Telecommunications, Chongqing, China.

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.

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

Pan, Y. and Zhang, P. (2023) Some Results for Exact Support Recovery of Block Joint Sparse Matrix via Block Multiple Measurement Vectors Algorithm. Journal of Applied Mathematics and Physics, 11, 1098-1112. doi: 10.4236/jamp.2023.114072.