In this paper, we propose an alternating direction method of multipliers
(ADMM)-based optimization algorithm to achieve better undersampling rate for
multiple measurement vector (MMV) problem. The core is to introduce the
β2,0β-norm sparsity constraint to describe the joint-sparsity of the MMV
problem, which is different from the widely used β2,1β-norm constraint
in the existing research. In order to illustrate the better performance of
β2,0β-norm, first this paper proves the equivalence of the sparsity of
the row support set of a matrix and its β2,0β-norm. Afterward, the MMV
problem based on β2,0β-norm is proposed. Moreover, building on the
Kurdyka-Lojasiewicz property, this paper establishes that the sequence
generated by ADMM globally converges to the optimal point of the MMV problem.
Finally, the performance of our algorithm and comparison with other algorithms
under different conditions is studied by simulated examples.Comment: 24 pages, 5 figures, 4 table