Construction of Compressed Sensing Measurement Matrix Based on Binary Sequence Family

LU Cunbo; XIAO Song; QUAN Lei

doi:10.11999/JEIT151076

Volume 38 Issue 7

Jul. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(7): 1682-1688

LU Cunbo, XIAO Song, QUAN Lei. Construction of Compressed Sensing Measurement Matrix Based on Binary Sequence Family[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1682-1688. doi: 10.11999/JEIT151076

Citation:

LU Cunbo, XIAO Song, QUAN Lei. Construction of Compressed Sensing Measurement Matrix Based on Binary Sequence Family[J]. Journal of Electronics & Information Technology, 2016, 38(7): 1682-1688. doi: 10.11999/JEIT151076

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Construction of Compressed Sensing Measurement Matrix Based on Binary Sequence Family

doi: 10.11999/JEIT151076

Funds:

The National Natural Science Foundation of China (61372069), The Programme of Introducing Talents of Discipline to Universities (111 Project) (B08038)

Received Date: 2015-09-21
Rev Recd Date: 2016-01-20
Publish Date: 2016-07-19

Abstract

Abstract

It is significant to construct deterministic measurement matrix for the promotion and application of the Compressed Sensing (CS) theory. Originating from the algebraic coding theory, a construction algorithm of Binary Sequence Family (BSF) based deterministic measurement matrix is presented. The coherence is an important criterion to describe the property of matrices. Lower coherence leads to higher reconstruction performance. The coherence of the proposed measurement matrix is derived to be smaller than the corresponding Gaussian random matrix and Bernoulli random matrix. Theoretical analysis and simulation results show that the proposed matrix can obtain better reconstruction results than the corresponding Gaussian random matrix and Bernoulli random matrix. The proposed matrix can make the hardware realization convenient and easy by means of Linear Feedback Shift Register (LFSR) structures, thus being conductive to practical compressed sensing.
- Signal processing,
- Compressed Sensing (CS),
- Measurement matrix,
- Binary Sequence Family (BSF)

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References(21)

References

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