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

Abstract

Reducing carbon emissions is a global priority due to human impact on atmosphe ric pollution and the greenhouse effect. Achieving carbon peak and neutrality requires real-time monitoring of CO 2 concentrations. However, developing highsensitivity, portable, and anti-jamming gas detection solutions is challenging. Among spectroscopic techniques, Tunable Diode Laser Absorption Spectroscopy (TDLAS) is highly sensitive for detecting CO 2 concentrations. This paper elaborates on the principles of TDLAS for detecting CO 2 concentrations and proposes a noise reduction algorithm to meet diverse environmental requirements. Simulations were performed using software to simulate CO 2 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-tonoise ratio (SNR) by 22.435% under these conditions. When the noise level increased fourfold from the origi nal signal, the algorithm enhanced the SNR by 59.514%, enabling reliable CO 2 monitoring even under challenging conditions.