Open AccessAn Optimization Method for Measurement Matrix Based on Double Decomposition
Author(s) -
Zhaoyang Mao,
Lan Li
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/799/1/012003
Subject(s) - singular value decomposition , matrix (chemical analysis) , matrix decomposition , eigendecomposition of a matrix , eigenvalues and eigenvectors , decomposition , algorithm , coherence (philosophical gambling strategy) , mathematical optimization , computer science , sampling (signal processing) , mathematics , statistics , materials science , physics , computer vision , ecology , filter (signal processing) , quantum mechanics , composite material , biology
This paper introduces a novel method of measurement matrix in compressed sensing. In order to overcome the difficulties associated with coherence of measurement matrix, we propose double optimization methods by eigenvalue decomposition and singular value decomposition under mild conditions. An efficient algorithm (SVD-EIG) is used to recover sparse inputs from the optimized measurement matrix, based on the adaptation of the optimized matrix by eigenvalue decomposition. Lastly, compared with the other methods as the same sampling rate, we demonstrate through simulations that SVD-EIG algorithm can improve accuracy and probability of the reconstruction.