Abstract
Matrices with low coherence have applications in compressed sensing and some other areas. In this paper, we present three deterministic constructions of compressed sensing matrices by using algebraic and combinatorial methods. We show that our results outperform Gaussian random matrices. Moreover, some of our matrices are binary entries, and thus can be used in the embedding operations to get more matrices with low coherence recursively.
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This article is part of the Topical Collection on Special Issue on Sequences and Their Applications
First Author is supported by the NNSF of China (11771007, 61572027).
Third Author is supported by the NNSF of China (Grant No. 11601177), the Funding for Outstanding Doctoral Dissertation in NUAA (Grant No. BCXJ16-08), Anhui Provincial Natural Science Foundation (1608085QA05), the Foundation for Distinguished Young Talents in Higher Education of Anhui Province of China (gxyqZD2016258)
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Cao, X., Luo, G. & Xu, G. Three deterministic constructions of compressed sensing matrices with low coherence. Cryptogr. Commun. 12, 547–558 (2020). https://doi.org/10.1007/s12095-019-00375-5
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DOI: https://doi.org/10.1007/s12095-019-00375-5