应用数学和力学2017，Vol.38Issue(8)：932-942,11.DOI:10.21656/1000-0887.370230
基于l1-l2范数的块稀疏信号重构
Block-Sparse Signal Recovery Based on Norm Minimization
摘要
Abstract
Compressed sensing (CS) is a newly developed theoretical framework for information acquisition and processing.Through the solution of non-linear optimization problems,sparse and compressible signals can be recovered from small-scale linear and non-adaptive measurements.Block-sparse signals as typical sparse ones exhibit additional block structures where the non-zero elements occur in blocks (or clusters).Based on the previous l1-2 norm minimization method given by YIN Peng-hang,LOU Yi-fei,HE Qi,et al.for common sparse signal recovery,the l1-l2 minimization recovery algorithm was extended to the block-sparse model,the properties of the l1-l2 norm were proved and the sufficient condition for block-sparse signal recovery was established.Meanwhile,an iterative method for block-sparse l1-l2 minimization was presented by means of the DCA (difference of convex functions algorithm) and the ADMM (alternating direction method of multipliers).The numerical simulation results demonstrate that the signal recovery success rate of the proposed algorithm is higher than those of the existing algorithms.关键词
块稀疏/l1-l2范数/压缩感知/重构算法Key words
block-sparse/l1-l2 norm/compressed sensing/recovery algorithm分类
数理科学引用本文复制引用
陈鹏清,黄尉..基于l1-l2范数的块稀疏信号重构[J].应用数学和力学,2017,38(8):932-942,11.基金项目
The Major Research Plan of the National Natural Science Foundation of China(91538112) （91538112）
The National Science Fund for Young Scholars of China(11201450)国家自然科学基金重大研究计划(91538112) （11201450）
国家自然科学基金青年科学基金(11201450) （11201450）
