Performance analysis of moving average filtering used in TDLAS carbon dioxide sensing system

Chen Yang; Lin Gui; Hao Lin; Pan Dai; Zijun Liu

doi:10.1117/12.2683121

24 May 2023 Performance analysis of moving average filtering used in TDLAS carbon dioxide sensing system

Chen Yang, Lin Gui, Hao Lin, Pan Dai, Zijun Liu

Proceedings Volume 12706, First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023); 1270612 (2023) https://doi.org/10.1117/12.2683121
Event: First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023), 2023, Shanghai, China

Abstract

Reducing carbon emissions is a global priority due to human impact on atmospheric pollution and the greenhouse effect. Achieving carbon peak and neutrality requires real-time monitoring of CO₂ concentrations. However, developing high-sensitivity, portable, and anti-jamming gas detection solutions is challenging. Among spectroscopic techniques, Tunable Diode Laser Absorption Spectroscopy (TDLAS) is highly sensitive for detecting CO₂ concentrations. This paper elaborates on the principles of TDLAS for detecting CO₂concentrations and proposes a noise reduction algorithm to meet diverse environmental requirements. Simulations were performed using software to simulate CO₂ absorption spectra at approximately 1.57866535μm under high-intensity noise (0.1mW - 1mW). Based on this simulation, we applied the Wavelength Modulation Spectroscopy (WMS) technique to calculate the ratio of the output differential signal's second harmonic intensity to the first harmonic S_2f/1f(T) and output power to reduce light intensity influence and improve concentration inversion linearity. The weighted convolutional moving average filtering was utilized to optimize WMS denoising, utilizing weight transfer to make the process more precise and reliable. After analyzing various window functions, it was concluded that a window length of 9 would be the most optimal. The algorithm improved the signal-to-noise ratio (SNR) by 22.435% under these conditions. When the noise level increased fourfold from the original signal, the algorithm enhanced the SNR by 59.514%, enabling reliable CO₂ monitoring even under challenging conditions.

Citation Download Citation

Chen Yang, Lin Gui, Hao Lin, Pan Dai, and Zijun Liu "Performance analysis of moving average filtering used in TDLAS carbon dioxide sensing system", Proc. SPIE 12706, First International Conference on Spatial Atmospheric Marine Environmental Optics (SAME 2023), 1270612 (24 May 2023); https://doi.org/10.1117/12.2683121

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KEYWORDS

Signal to noise ratio

Tunable filters

Windows

Denoising

Signal processing

Modulation

Interference (communication)

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