TY - JOUR
T1 - Weighted $\ell_p$-Minimization for Sparse Signal Recovery under Arbitrary Support Prior
AU - Ge , Yueqi
AU - Chen , Wengu
AU - Ge , Huanmin
AU - Li , Yaling
JO - Analysis in Theory and Applications
VL - 3
SP - 289
EP - 310
PY - 2021
DA - 2021/09
SN - 37
DO - http://doi.org/10.4208/ata.2021.lu80.02
UR - https://global-sci.org/intro/article_detail/ata/19876.html
KW - Adaptive recovery, compressed sensing, weighted $\ell_p$ minimization, sparse representation, restricted isometry property.
AB - <p style="text-align: justify;">Weighted $\ell_p$ ($0&lt;p\leq1$) 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 $\ell_p$ ($0&lt;p\leq1$) 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.</p>