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Statistical inference for block sparsity of complex signals
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Department of Statistics, Zhejiang University City College, China.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. (Mathematical Statistics)ORCID iD: 0000-0001-5673-620x
2019 (English)Manuscript (preprint) (Other academic)
Abstract [en]

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 beestimated. Recently there emerges a few research work about how to estimate block sparsity of real-valued signals, while there is, to the best of our knowledge, no investigation that has been conductedfor complex-valued signals. In this paper, we propose a new method to estimate the block sparsity of complex-valued signal. Its statistical properties are obtained and verified by simulations. In addition,we demonstrate the importance of accurately estimating the block sparsity in signal recovery through asensitivity analysis.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Block sparsity, Complex-valued signals, Multivariate isotropic symmetric α-stable distribution
National Category
Signal Processing Probability Theory and Statistics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-162998OAI: oai:DiVA.org:umu-162998DiVA, id: diva2:1348429
Part of project
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment, Swedish Research CouncilAvailable from: 2019-09-04 Created: 2019-09-04 Last updated: 2019-09-06

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arXiv September 2019

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Wang, JianfengZhou, ZhiyongYu, Jun

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