Open AccessStatistical inference for the block sparsity of complex‐valued signals
Author(s) -
Wang Jianfeng,
Zhou Zhiyong,
Yu Jun
Publication year - 2020
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2019.0200
Subject(s) - block (permutation group theory) , compressed sensing , computer science , algorithm , statistical inference , inference , sensitivity (control systems) , signal (programming language) , signal processing , artificial intelligence , pattern recognition (psychology) , mathematics , statistics , digital signal processing , geometry , electronic engineering , computer hardware , engineering , programming language
Block sparsity is an important parameter in many algorithms to successfully recover block‐sparse signals under the framework of compressive sensing. However, it is often unknown and needs to be estimated. Recently there emerges a few research works about how to estimate block sparsity of real‐valued signals, while there is, to the best of our knowledge, no research that has been done on complex‐valued signals. In this study, the authors propose a method to estimate the block sparsity of complex‐valued signal. Its statistical properties are obtained and verified by simulations. In addition, the authors demonstrate the importance of accurately estimating the block sparsity through a sensitivity analysis.