The Optimal Method of Sensing Matrix Based on Matrix Decomposition

摘要：

　　Different from traditional Nyquist sampling theorem,compressive sensing realizes sampling and compressing signals at the same time.The design of sensing matrix plays an implicit role in compressive sensing.In order to improve the quality of reconstruction,we propose the optimal methods to sensing matrix based on matrix decomposition: the SVD method and the QR method.Since the larger the minimum singular value of sensing matrix is,the more independent the matrix will be.Thus,we increase the singular values and eigenvalues through these two methods,respectively.Finally,the effect of the methods proposed in this paper is validated through the reconstruction of images.

关键词： compressive sensing sensing matrix singular value decomposition QR decomposition

作者: Yuting Li Jiying Liu Jubo Zhu

作者单位: College of Science,National University of Defense Technology Changsha,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 1468-1471

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）