• Spectroscopy and Spectral Analysis
  • Vol. 36, Issue 10, 3369 (2016)
WANG Zhe1, WANG Yan1, ZHANG Rui2, ZHAO Xue-hong1, LIU Qiao-jun2, and LI Cong-rong2
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3964/j.issn.1000-0593(2016)10-3369-08 Cite this Article
    WANG Zhe, WANG Yan, ZHANG Rui, ZHAO Xue-hong, LIU Qiao-jun, LI Cong-rong. A Singular Value Decomposition Method for Tunable Diode Laser Absorption Spectroscopy System to Remove Systematic Noises[J]. Spectroscopy and Spectral Analysis, 2016, 36(10): 3369 Copy Citation Text show less

    Abstract

    Detection of gas concentration with tunable diode laser absorption spectroscopy (TDLAS) techniques is affected by baseline drift and high-frequency noise. Therefore, how to remove the systematic noises has been a hot spot. This paper analyzes the significance of singular value decomposition (SVD) in TDLAS detection system with two different methods of constructing a matrix, and it discusses the differences of processing results for different noises. The second harmonic signal is arranged in a matrix and decomposed. We select the appropriate threshold and putthose singular values smaller than the threshold into zero, then reconstruct the matrix. Experiments show that SVD method does not require additional system components or pass into the zero gas to subtract background. This method is able to remove noises of TDLAS system quickly and effectively. We found that the method of constructing a hankel matrix is suitable for removing high-frequency noise. However, the method of constructing a continuous-cutoff-signal matrix is suitable for removing baseline drift. For example, we set up a TDLAS system to measure the concentration of NH3 while the noise removal rate of the second harmonic curve is up to 80% with this method.
    WANG Zhe, WANG Yan, ZHANG Rui, ZHAO Xue-hong, LIU Qiao-jun, LI Cong-rong. A Singular Value Decomposition Method for Tunable Diode Laser Absorption Spectroscopy System to Remove Systematic Noises[J]. Spectroscopy and Spectral Analysis, 2016, 36(10): 3369
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