Year: 2020
Author: Klara Leffler, Zhiyong Zhou, Jun Yu
Journal of Computational Mathematics, Vol. 38 (2020), Iss. 6 : pp. 827–838
Abstract
We study the recovery conditions of weighted mixed $\ell_2/\ell_p$ minimization for block sparse signal reconstruction from compressed measurements when partial block support information is available. We show theoretically that the extended block restricted isometry property can ensure robust recovery when the data fidelity constraint is expressed in terms of an $\ell_q$ norm of the residual error, thus establishing a setting wherein we are not restricted to Gaussian measurement noise. We illustrate the results with a series of numerical experiments.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/jcm.1905-m2018-0256
Journal of Computational Mathematics, Vol. 38 (2020), Iss. 6 : pp. 827–838
Published online: 2020-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 12
Keywords: Compressed sensing block sparsity partial support information signal reconstruction convex optimization.