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Phaseless compressive sensing using partial support information
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)In: Optimization Letters, ISSN 1862-4472, E-ISSN 1862-4480Article in journal (Refereed) Epub ahead of print
Abstract [en]

We study the recovery conditions of weighted ℓ1 minimization for real-valued signal reconstruction from phaseless compressive sensing measurements when partial support information is available. A strong restricted isometry property condition is provided to ensure the stable recovery. Moreover, we present the weighted null space property as the sufficient and necessary condition for the success of k-sparse phaseless recovery via weighted ℓ1 minimization. Numerical experiments are conducted to illustrate our results.

Place, publisher, year, edition, pages
Springer, 2019.
Keywords [en]
Phaseless compressive sensing, Partial support information, Strong restricted isometry property, Weighted null space property
National Category
Signal Processing Probability Theory and Statistics Computational Mathematics
Research subject
Mathematical Statistics
Identifiers
URN: urn:nbn:se:umu:diva-163880DOI: 10.1007/s11590-019-01487-wOAI: oai:DiVA.org:umu-163880DiVA, id: diva2:1358083
Part of project
Statistical modelling and intelligent data sampling in MRI and PET measurements for cancer therapy assessment, Swedish Research Council
Funder
Swedish Research Council, 340-2013-5342Available from: 2019-10-07 Created: 2019-10-07 Last updated: 2019-10-09

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Zhou, ZhiyongYu, Jun

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