The Processing of EFPI Fiber Gas Detection Signal Based on Hilbert-Huang Transforms Algorithm

Article Preview

Abstract:

In order to obtain spectra from EFPI interference with the noise in the signal spectrum which can get demodulation precision cavity length, HHT method is proposed based gas detection algorithm processing EFPI fiber optic signals. The principle of HHT algorithm is to deal with the noise spectral signal EFPI through empirical mode decomposition (EMD), a group based on the signal obtained their intrinsic mode function (IMF), through to the IMF for Hilbert transform the Hilbert spectrum for time-frequency analysis. Through simulation and numerical analysis, the algorithm to solve the original non-stationary signal interference spectrum , nonlinear shortcomings, through a series of changes to remove noise, which can reduce the difficulty of data processing and increase the accuracy of the gas signal analysis .

You might also be interested in these eBooks

Info:

Periodical:

Pages:

985-989

Citation:

Online since:

October 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Chai Hang: Design and Realization of Optical Fiber Methane Concentration Detecting System. Huazhong University of Science & Technology. ( Master Thesis), (2011).

Google Scholar

[2] He Junfeng, Liu Wenqing, Zhang Yujun, Chen Zhenyi, Ruan Jun, Wang Liming: A Denoising Method for Backscatter Signal of Laser Ceilometer Based on Hilbert-Huang Transform. Acta Optica Sinica, 31(2), pp.1-10, (2011).

DOI: 10.3788/aos201131.0201001

Google Scholar

[3] Wu Jin, Ji Weiping. Study on Frequency-shift Signal Demodulation Using HHT Algorithm. Computer Measurement & Control, 21(4), pp.1054-1056, (2013).

Google Scholar

[4] Wang Xueming, Monitoring Signal Analysis and Processing for Bridges and Time-varying Modal Parameter Identification Based on Hilbert-Huang Transform. Central South University. ( Master Thesis), (2008).

Google Scholar

[5] Shen Tao, Feng Gang. Speech Endpoint Detection Algorithm Based on EMD in Highly Noisy Background. Audio Engineering , 38(1), pp.69-72, (2014).

Google Scholar

[6] Peng Mingjin, Li Zhi. Analysis and Feature Extraction of Laser Micro-Doppler Signatures Based on Hilbert-Huang Transforms. Chinese Journal of Lasers, 40(8) , pp.48-52, (2013).

DOI: 10.3788/cjl201340.0809004

Google Scholar

[7] Sun G L, Zhang C L, Fang Y H, Huang H H. Wavelet transform in spectral feature extraction applications. Chinese Journal of Quantum Electronics, 23(1), pp.22-25, (2006).

Google Scholar