Construction of compressed sensing matrixes based on the singular pseudo-symplectic space over finite fields

doi:10.1016/S1005-8885(16)60074-2

中国邮电高校学报(英文) ›› 2016, Vol. 23 ›› Issue (6): 82-89.doi: 10.1016/S1005-8885(16)60074-2

Construction of compressed sensing matrixes based on the singular pseudo-symplectic space over finite fields

高有,仝丰华,张晓娟

中国民航大学

收稿日期:2016-06-15 修回日期:2016-09-27 出版日期:2016-12-31 发布日期:2016-12-30
通讯作者: 高有 E-mail:gao_you@263.net
基金资助:
中国国家自然科学基金

Construction of compressed sensing matrixes based on the singular pseudo-symplectic space over finite fields

Received:2016-06-15 Revised:2016-09-27 Online:2016-12-31 Published:2016-12-30
Contact: You GAO E-mail:gao_you@263.net
Supported by:
National Natural Science Foundation of China

摘要/Abstract

摘要： Compressed sensing (CS) provides a new approach to acquire data as a sampling technique and makes it sure that a sparse signal can be reconstructed from few measurements. The construction of compressed matrixes is a central problem in compressed sensing. This paper provides a construction of deterministic CS matrixes, which are also disjunct and inclusive matrixes, from singular pseudo-symplectic space over finite fields of characteristic 2. Our construction is superior to DeVore’s construction under some conditions and can be used to reconstruct sparse signals through an efficient algorithm.

关键词: compressed sensing matrix, singular pseudo-symplectic space, sparse signal, disjunct matrix, inclusive matrix

Abstract: Compressed sensing (CS) provides a new approach to acquire data as a sampling technique and makes it sure that a sparse signal can be reconstructed from few measurements. The construction of compressed matrixes is a central problem in compressed sensing. This paper provides a construction of deterministic CS matrixes, which are also disjunct and inclusive matrixes, from singular pseudo-symplectic space over finite fields of characteristic 2. Our construction is superior to DeVore’s construction under some conditions and can be used to reconstruct sparse signals through an efficient algorithm.

Key words: compressed sensing matrix, singular pseudo-symplectic space, sparse signal, disjunct matrix, inclusive matrix

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Construction of compressed sensing matrixes based on the singular pseudo-symplectic space over finite fields

Construction of compressed sensing matrixes based on the singular pseudo-symplectic space over finite fields

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