Newly deterministic construction of compressed sensing matrices via singular linear spaces over finite fields

Yingmo Jie (), Cheng Guo () and Zhangjie Fu ()
Additional contact information
Yingmo Jie: Dalian University of Technology
Cheng Guo: Dalian University of Technology
Zhangjie Fu: Nanjing University of Information Science and Technology

Journal of Combinatorial Optimization, 2017, vol. 34, issue 1, No 17, 245-256

Abstract: Abstract A valuable opportunity is provided by compressed sensing (CS) to accomplish the tasks of high speed sampling, the transmission of large volumes of data, and storage in signal processing. To some extent, CS has brought tremendous changes in the information technologies that we use in our daily lives. However, the construction of compressed sensing matrices still can pose substantial problems. In this paper, we provide a kind of deterministic construction of sensing matrices based on singular linear spaces over finite fields. In particular, by choosing appropriate parameters, we constructed binary sensing matrices that are superior to existing matrices, and they outperform DeVore’s matrices. In addition, we used an embedding manipulation to merge our binary matrices with matrices that had low coherence, thereby improving such matrices. Compared with the quintessential binary matrices, the improved matrices possess better ability to compress and recover signals. The favorable performance of our binary and improved matrices was demonstrated by numerical simulations.

Keywords: Compressed sensing matrices; Singular linear spaces; Coherence; Restricted isometry property (RIP) (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10878-016-0068-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v:34:y:2017:i:1:d:10.1007_s10878-016-0068-y

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-016-0068-y

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().