A Method of Fiber Bragg Grating Sensing Signal De-Noise Based on Compressive Sensing | Zendy

Yong Chen | Zendy; Zhiqiang Liu | Zendy; Huanlin Liu | Zendy

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A Method of Fiber Bragg Grating Sensing Signal De-Noise Based on Compressive Sensing

Author(s) -

Yong Chen,

Zhiqiang Liu,

Huanlin Liu

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2819647

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

A novel de-noising method based on the compressive sensing reconstruction algorithm is proposed in this paper, which is used to solve the problem that the fiber Bragg grating (FBG) sensing signal is easily affected by environment interference. By analyzing the characteristics of the FBG signal in the sparse domain, we calculated the sparsity of the FBG signal through the exponential fitting method. Considering the complexity of the traditional algorithm, we proposed a reasonable threshold for selecting multi-atom to reduce the run time of the reconstruction algorithm. In addition, we designed the elimination strategy and double termination conditions to improve the precision of the reconstructed signal. The experiment results show that the maximum reconstructed signal-to-noise ratio of our method is 49.2 dB, with a relative error of 0.0034~0.0074. The run time of the proposed algorithm is much lower than the same-type algorithms. The reconstruction precision is far beyond the wavelet threshold de-noising and empirical mode decomposition.

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