Weighted $\ell_p$-Minimization for Sparse Signal Recovery under Arbitrary Support Prior

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.

Author Details

Yueqi Ge

Wengu Chen

Huanmin Ge

Yaling Li