Improved optimization algorithm of the Gram measurement matrix based on gradient projection

被引：0
|
作者：
Liu J. [1 ]
Yang C. [1 ]
Fang J. [1 ]
Wei G. [1 ]
机构：
[1] School of Electronic and Information Engineering, South China University of Technology, Guangzhou
来源：
Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2016年 / 44卷 / 08期
关键词：
Compressed sensing; Gram matrix; Measurement matrix; Optimization algorithm; QR decomposition;
D O I：
10.13245/j.hust.160813
中图分类号：
学科分类号：
摘要：
To solve the optimization problem of measurement matrix in the compressed sensing, the independence of measurement matrix columns and the coherence between rows of the measurement matrix and columns of sparse basis were analyzed to find out whether they can influence the quality of the reconstruction, so the QR decomposition was used to enhance the independence of measurement matrix column. By combining the QR decomposition with the Gram measurement matrix based on gradient projection, an improved algorithm was proposed. The proposed algorithm reduces the correlation between the measurement matrix and sparse matrix by using equiangular tight frame. Secondly, the gradient projection method was used to solve the measurement matrix. Finally, QR decomposition was used to enhance the independence of measurement matrix column. Simulation results show that the proposed algorithm improves the quality of reconstructed signals compared with the Gram matrix optimization algorithm based on gradient projection. © 2016, Editorial Board of Journal of Huazhong University of Science and Technology. All right reserved.
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页码：62 / 65
页数：3
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