Noise suppression: Empirical modal decomposition in non-dispersive infrared gas detection systems

Abstract Gas sensors based on non-dispersive infrared (NDIR) technology have been researched extensively and dual-channel measurement schemes are widely adopted due to their unique advantages. However, there is a lack of comprehensive research on the performance of optical sensors with reference signals in existing two-channel measurement schemes. In this study, a methane gas detection system based on mid-infrared LED and photodiodes was designed with an empirical modal decomposition (EMD) algorithm for signal processing. The EMD processes measurement and reference signals to improve the signal-to-noise ratio (SNR) to 5 dB over the traditional white noise suppression method. The interference signal is identified and suppressed through joint analysis of the intrinsic mode function (IMFs) of the double-channel signals, thus further improving the SNR to 2 dB. The EMD is an adaptive method for processing non-stationary signals to optimize sensor performance.

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