Year: 2021
Author: Yueqi Ge, Wengu Chen, Huanmin Ge, Yaling Li
Analysis in Theory and Applications, Vol. 37 (2021), Iss. 3 : pp. 289–310
Abstract
Weighted $\ell_p$ ($0<p\leq1$) minimization has been extensively studied as an effective way to reconstruct a sparse signal from compressively sampled measurements when some prior support information of the signal is available. In this paper, we consider the recovery guarantees of $k$-sparse signals via the weighted $\ell_p$ ($0<p\leq1$) minimization when arbitrarily many support priors are given. Our analysis enables an extension to existing works that assume only a single support prior is used.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/ata.2021.lu80.02
Analysis in Theory and Applications, Vol. 37 (2021), Iss. 3 : pp. 289–310
Published online: 2021-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 22
Keywords: Adaptive recovery compressed sensing weighted $\ell_p$ minimization sparse representation restricted isometry property.