• Laser & Optoelectronics Progress
  • Vol. 59, Issue 18, 1830003 (2022)
Jing Wang1, Lifang Zhang1、2、*, Jusheng Yang1, Yanxia Yang1, and Guanjia Zhao1
Author Affiliations
  • 1Department of Thermal Engineering, College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, Shanxi , China
  • 2The Postdoctoral Workstation of Taiyuan Boiler Group Co., Ltd., Taiyuan 030024, Shanxi , China
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    DOI: 10.3788/LOP202259.1830003 Cite this Article Set citation alerts
    Jing Wang, Lifang Zhang, Jusheng Yang, Yanxia Yang, Guanjia Zhao. Noise Reduction of Wavelength-Modulated Signal Based on Wavelet and Empirical Mode Decomposition[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1830003 Copy Citation Text show less

    Abstract

    Wavelength modulation signal (WMS) based on tunable diode laser absorption spectroscopy (TDLAS) is an effective trace gas detection technology. It offers several advantages such as high selectivity, high precision, and high sensitivity, as well as real-time online monitoring. In the field measurement process, background noise and optical fringes significantly reduce the system’s measurement accuracy, affecting the concentration measurement results. To effectively eliminate the effect of these noise on the measurement and improve the signal to noise ratio (SNR) of the system, this study proposes a digital filtering denoising method based on wavelet transform combined with empirical mode decomposition (EMD). The NH3 with low mass concentration was measured experimentally, and the wavelet transform filtering, EMD filtering, and wavelet transform combined with the EMD filtering were used to reduce noise in the obtained harmonic signals. The experimental results show that the wavelet transform combined with the EMD filtering has the best noise reduction effect compared to the other two methods. The SNR of the wavelet transform combined with the EMD filtering increases from 28.4 dB to 446 dB for NH3 with a mass concentration of 11.38 mg/m3. Thus, the proposed method significantly improves the system’s measurement accuracy.
    Jing Wang, Lifang Zhang, Jusheng Yang, Yanxia Yang, Guanjia Zhao. Noise Reduction of Wavelength-Modulated Signal Based on Wavelet and Empirical Mode Decomposition[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1830003
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