Measurement precision improving with adaptive Kalman filter for quartz-enhanced photoacoustic spectroscopy methane sensor
Source abstract
Abstract
Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) has shown significant progress in trace gas sensing in recent years; however, its detection performance remains constrained by system noise. Conventional filtering algorithms typically require prior noise characterization and manual parameter tuning, limiting their adaptability in dynamic environments. This study introduces an Adaptive Kalman Filter (AKF) algorithm for real-time CH4 detection, improving the precision of QEPAS systems. The proposed algorithm improves detection accuracy by a factor of 2.41 without the need for manual parameter optimization, while maintaining a rapid response time. By eliminating parameter dependency, the AKF algorithm enhances the robustness and applicability of QEPAS in complex and time-varying conditions.
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Research context
OpenAlex topics: Atmospheric and Environmental Gas Dynamics · Spectroscopy and Laser Applications · Atmospheric Ozone and Climate
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mathematics and Physics, Bengbu University 1 , Bengbu, Anhui 233030,
School of Earth and Space Science, University of Science and Technology of China 2 , Hefei, Anhui 230026,
College of Environmental Science and Optoelectronic Technology, University of Science and Technology of China 3 , Hefei, Anhui 230026,
School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University 4 , Anqing, Anhui 246133,
Funding
National Natural Science Foundation of China · University Natural Science Research Project of Anhui Province · Anhui Province Research and Innovation Team for Optoelectronic Information Technology and Devices
References (29)
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