Open AccessResearch on Deterministic Measurement Matrix of Power Line Carrier Compressed Sensing
Author(s) -
Jiliang Jin,
Liyun Xing,
Yuqing Miao,
Jianqiang Shen,
Yanhua Dong
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2095/1/012017
Subject(s) - chebyshev filter , circulant matrix , matrix (chemical analysis) , compressed sensing , chaotic , algorithm , power (physics) , computer science , sparse matrix , chebyshev polynomials , gaussian , mathematics , electronic engineering , physics , engineering , materials science , mathematical analysis , artificial intelligence , computer vision , quantum mechanics , composite material
To verify the advantages of deterministic matrix applied to power line carrier communication (PLCC) based on compressed sensing (CS). This article analyzed the research status of commonly used deterministic measurement matrices, and made simulation comparison. It is found that different types of deterministic measurement matrices generated based on chaotic mapping had higher reconstruction accuracy and higher reconstruction efficiency than Gaussian random matrix. Then, according to simulation results and the characteristics of PLCC signal, the Chebyshev sparse circulant (CSC) measurement matrix was designed by combining eighth-order Chebyshev chaotic and the idea of sparse and circulant. Actual circuit measurement shows that when compression rate was 40% and 60%, the reconstruction loss of CSC is 0.72dB and 0.49dB higher than that of Chebyshev chaotic measurement matrix and Chebyshev circulate measurement matrix, respectively. Obviously, the CSC measurement matrix designed in this paper can effectively improve the reconstruction accuracy.