Compressive Sensing Sparse Sampling Method for Composite Material Based on Principal Component Analysis

作     者：Sun Yajie Gu Feihong Ji Sai Wang Lihua 

作者机构：Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 P. R. China School of Information and Control Nanjing University of Information Science and Technology Nanjing 210014 P. R. China 

出 版 物：《Transactions of Nanjing University of Aeronautics and Astronautics》 (南京航空航天大学学报（英文版）)

年 卷 期：2018年第35卷第2期

页      面：282-289页

核心收录：

学科分类：08[工学] 080202[工学-机械电子工程] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 0704[理学-天文学] 

基　　金：supported by the National Natural Science Foundation of China(Nos.51405241,61672290) the Jiangsu Government Scholarship for Overseas Studies and the PAPD Fund 

主　　题：principal component analysis compressive sensing sparse representation signal reconstruction 

摘      要：Signals can be sampled by compressive sensing theory with a much less rate than those by traditional Nyquist sampling theorem,and reconstructed with high probability,only when signals are sparse in the time domain or a transform domain.Most signals are not sparse in real world,but can be expressed in sparse form by some kind of sparse transformation.Commonly used sparse transformations will lose some information,because their transform bases are generally fixed.In this paper,we use principal component analysis for data reduction,and select new variable with low dimension and linearly correlated to the original variable,instead of the original variable with high dimension,thus the useful data of the original signals can be included in the sparse signals after dimensionality reduction with maximize portability.Therefore,the loss of data can be reduced as much as possible,and the efficiency of signal reconstruction can be improved.Finally,the composite material plate is used for the experimental verification.The experimental result shows that the sparse representation of signals based on principal component analysis can reduce signal distortion and improve signal reconstruction efficiency.