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压缩感知(CS)是近年来针对稀疏信号或可压缩信号提出的,在信号采样的同时对其进行高效压缩的一种新理论。在压缩感知理论中,测量矩阵对信号采样方式和重建精度有着重要的影响。文中阐述了测量矩阵的构造对压缩感知性能的影响,介绍了设计测量矩阵时所需满足的约束等距性(R IP)条件,回顾了已有的测量矩阵构造方法,总结了近几年来测量矩阵构造的新方法及所构造矩阵的特点,并指出了测量矩阵研究的发展方向。
Compressed sensing (CS) is a new theory proposed in spite of the sparse signal or compressible signal in recent years to compress the signal efficiently while compressing it. In the compressed sensing theory, the measurement matrix has an important influence on the signal sampling method and reconstruction accuracy. In this paper, the influence of the structure of the measurement matrix on the compressive sensing performance is expounded. The constraint isomerism (R IP) condition required to design the measurement matrix is introduced. The existing measurement matrix construction methods are reviewed. The measurement methods The new method of matrix construction and the characteristics of the constructed matrix, and points out the research direction of the measurement matrix.