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

• 作 者: Gao You Tong Fenghua Zhang Xiaojuan
• 机 构: College of Science,Civil Aviation University of China,
• 出 处: The Journal of China Universities of Posts and Telecommunica
• 发表日期: 2016年06期
• 关键词: symplectic compressed sparse reconstruct inclusive deterministic singular recove

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 De Vore’s construction under some conditions and can be used to reconstruct sparse signals through an efficient algorithm.

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 De Vore’s construction under some conditions and can be used to reconstruct sparse signals through an efficient algorithm.