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Statistical inference for block sparsity of complex-valued 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
2020 (English)In: IET Signal Processing, ISSN 1751-9675, E-ISSN 1751-9683Article in journal (Refereed) Accepted
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 be estimated. 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 research that has been done for complex-valued signals. In this study, we propose a 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 through a sensitivity analysis.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2020.
Keywords [en]
Block sparsity; Complex-valued signals; Multivariate isotropic symmetric alpha-stable distributions
National Category
Probability Theory and Statistics Signal Processing
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-168016DOI: 10.1049/iet-spr.2019.0200OAI: oai:DiVA.org:umu-168016DiVA, id: diva2:1392716
Part of project
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment, Swedish Research CouncilAvailable from: 2020-02-10 Created: 2020-02-10 Last updated: 2020-02-18

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Publisher's full texthttps://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0200

Authority records BETA

Wang, JianfengZhou, ZhiyongYu, Jun

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CiteExportLink to record
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  • apa
  • ieee
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  • de-DE
  • en-GB
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Output format
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