This paper considers the construction of measurement matrices with low storage space and good performance. Aiming at source-limited devices, this paper proposes a novel block sampling model based on the embedding operation. In the sampling procedure, the proposed model saves a lot of storage space by storing two smaller seed matrices. Meanwhile, the block sampling mechanism makes the proposed model have a lower sampling complexity. Since the embedding operation is oriented to binary matrices, the construction of binary matrices plays a crucial role in the proposed model. In order to construct binary matrices with low coherence, this paper proposes a modified progressive edge growth algorithm by removing some limited condition of the traditional progressive edge growth algorithm. Based on the modified progressive edge growth algorithm and the embedding operation, this paper proposes a flexible method to construct sparse measurement matrices with low coherence. Simulation experiments demonstrate that our measurement matrices give better performance than several typical measurement matrices.

本文考虑了存储空间少，性能好的测量矩阵的构建。针对源受限的设备，本文提出了一种基于嵌入操作的新型块采样模型。在采样过程中，该模型通过存储两个较小的种子矩阵节省了大量的存储空间。同时，块采样机制使得所提出的模型具有较低的采样复杂度。由于嵌入操作面向二进制矩阵，因此二进制矩阵的构造在所提出的模型中起着至关重要的作用。为了构造低相干性的二进制矩阵，本文提出了一种改进的渐进边缘增长算法，该算法消除了传统渐进边缘增长算法的某些局限性。基于改进的渐进边增长算法和嵌入操作，提出了一种构造低相干性的稀疏度量矩阵的灵活方法。仿真实验表明，我们的测量矩阵比几种典型的测量矩阵具有更好的性能。