JOURNAL OF CHINA UNIVERSITIES OF POSTS AND TELECOM ›› 2016, Vol. 23 ›› Issue (6): 82-89.doi: 10.1016/S1005-8885(16)60074-2

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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

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