TY - JOUR
T1 - An Extended Block Restricted Isometry Property for Sparse Recovery with Non-Gaussian Noise
AU - Leffler , Klara
AU - Zhou , Zhiyong
AU - Yu , Jun
JO - Journal of Computational Mathematics
VL - 6
SP - 827
EP - 838
PY - 2020
DA - 2020/06
SN - 38
DO - http://doi.org/10.4208/jcm.1905-m2018-0256
UR - https://global-sci.org/intro/article_detail/jcm/16969.html
KW - Compressed sensing, block sparsity, partial support information, signal reconstruction, convex optimization.
AB - <p style="text-align: justify;">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.</p>