计算机技术与发展2017，Vol.27Issue(5)：73-76,4.DOI:10.3969/j.issn.1673-629X.2017.05.016
基于优化观测矩阵的共轭梯度改进算法
Improved Conjugate Gradient Algorithm Based on Optimization ofMeasurement Matrix
摘要
Abstract
The design of measurement matrix and the reconstruction of signal is the key in the study of compressed sensing theory.QR decomposition measurement matrix optimization based on gradient descent method makes it possible to preserve the main information during the process of observation,and the conjugate gradient algorithm is ideal for the reconstruction of the signal.The measurement matrix has been introduced to optimize the conjugate gradient reconstruction algorithm.In view of the conjugate gradient reconstruction algorithm,QR decomposition based on gradient descent method is used to optimize the measurement matrix and a new reconstruction algorithm,a conjugate gradient algorithm based on optimization of the measurement matrix,is obtained.In the improved algorithm,the matrix has larger column independence and lower correlation with sparse matrix,and has better performance with conjugate gradient method.Simulation experiments with Matlab have verified the new algorithm is feasible and effective.The results show that the conjugate gradient optimization algorithm with measurement matrix has been reduced 2~3 times in running time,which is greatly superior to other reconstruction algorithm.关键词
压缩感知/观测矩阵/共轭梯度法/梯度下降法/QR分解Key words
compressive sensing/measurement matrix/conjugate gradient method/gradient descent method/QR decomposition分类
信息技术与安全科学引用本文复制引用
兰明然,王友国,郑丹青..基于优化观测矩阵的共轭梯度改进算法[J].计算机技术与发展,2017,27(5):73-76,4.基金项目
国家自然科学基金资助项目(61179027) （61179027）
