Zhou Sheng, Shen Chong-Yang, Zhang Lei, Liu Ning-Wu, He Tian-Bo, Yu Ben-Li, Li Jing-Song
Opt Express. 2019 Oct 28;27(22):31874-31888. doi: 10.1364/OE.27.031874.
A dual-optimized adaptive Kalman filtering (DO-AKF) algorithm based on back propagation (BP) neural network and variance compensation was developed for high-sensitivity trace gas detection in laser spectroscopy. The BP neural network was used to optimize the Kalman filter (KF) parameters. Variance compensation was introduced to track the state of the system and to eliminate the variations in the parameters of dynamic systems. The proposed DO-AKF algorithm showed the best performance compared with the traditional multi-signal average, extended KF, unscented KF, KF optimized by BP neural network (BP-KF) and KF optimized by variance compensation (VC-KF). The optimized DO-AKF algorithm was applied to a QCL-based gas sensor system for an exhaled CO analysis. The experimental results revealed a sensitivity enhancement factor of 23. The proposed algorithm can be widely used in the fields of environmental pollutant monitoring, industrial process control, and breath gas diagnosis.
为实现激光光谱中高灵敏度痕量气体检测，开发了一种基于反向传播（BP）神经网络和方差补偿的双优化自适应卡尔曼滤波（DO-AKF）算法。BP神经网络用于优化卡尔曼滤波器（KF）参数。引入方差补偿以跟踪系统状态并消除动态系统参数的变化。与传统的多信号平均、扩展卡尔曼滤波、无迹卡尔曼滤波、BP神经网络优化的卡尔曼滤波（BP-KF）和方差补偿优化的卡尔曼滤波（VC-KF）相比，所提出的DO-AKF算法表现出最佳性能。将优化后的DO-AKF算法应用于基于量子级联激光器（QCL）的气体传感器系统进行呼出CO分析。实验结果显示灵敏度增强因子为23。所提出的算法可广泛应用于环境污染物监测、工业过程控制和呼气诊断等领域。
