摘要
针对目前压缩感知观测矩阵普适性低和优化算法鲁棒性不足的问题,提出了一种基于紧框架和稀疏表示误差的观测矩阵优化算法。该算法首先将格拉姆矩阵同时逼近单位矩阵和紧框架,以降低感知矩阵的平均互相干性;然后将稀疏表示误差作为正则项加入传统优化模型中,来提高观测矩阵的鲁棒性;最后利用解析法求解观测矩阵,保证算法的收敛性。实验结果表明,与现有的优化矩阵相比,所构造观测矩阵的平均互相干系数至少可降低0.03,且具有更强的鲁棒性。
An observation matrix optimization algorithm based on the tight frame and sparse representation error is proposed.First,the average mutual coherence of the sensing matrix is reduced by the Glam matrix which approximates the unit matrix and the constructed tight frame.Second,the sparse representation error as a regularization term is added to the conventional optimization model to improve the robustness of the observation matrix.Finally,the analytical method is applied to solve the observation matrix to ensure the convergence of the algorithm.Experimental results show that,compared with the contrast optimization matrix,the average mutual coherence of the proposed sensing matrix can be reduced by at least 0.03 with more robustness.
作者
赵辉
张乐
刘莹莉
张静
张天骐
ZHAO Hui;ZHANG Le;LIU Yingli;ZHANG Jing;ZHANG Tianqi(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China;Chongqing Key Laboratory of Signal and Information Processing,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2019年第6期171-178,共8页
Journal of Xidian University
基金
国家自然科学基金（6167095）
关键词
观测矩阵
平均互相干性
紧框架
稀疏表示误差
observation matrix
average mutual coherence
tight frame
sparse representation error
作者简介
赵辉(1980-),女,教授,E-mail:zhaohui@cqupt.edu.cn.
