Compressive Sensing Based on Deterministic Sparse Toeplitz Measurement Matrices with Random Pitch
基于随机间距稀疏Toeplitz测量矩阵的压缩传感

Selecting an appropriate measurement matrix is one of the key points of compressive sensing. Due to randomly introducing zero elements into these matrices to form sparse Toeplitz matrices with random pitch, the number of random independent variables can be reduced to 1/2 and 1/16 less than that of original Toeplitz matrices, the number of non-zero elements can also be reduced significantly, which is conducive to data transmission and storage. Simulation results show that the reconstructions of sparse Toeplitz measurement matrices with random pitch are better than Gaussian and original Toeplitz matrices. Moreover, the time of reconstruction is only about 15% to 40% of the time of Gauss and general Toeplitz reconstruction.