改善的LMS自适应算法在TDLAS甲烷检测的降噪研究

Improved LMS adaptive algorithm for noise reduction in TDLAS methane detection

为进一步有效抑制检测系统的噪声,提高检测精度,研究了改善的最小均方差自适应算法在可调谐二极管激光吸收光谱甲烷浓度检测系统的降噪方法。通过基于TDLAS技术的matlab仿真实验,搭建的甲烷检测系统,选取1 653.72 nm的甲烷吸收峰位,分析LMS自适应算法中滤波阶数、步长因子和采样周期三者对噪声的关系,改善参数的选取以最佳的滤波效果进行降噪优化处理。研究表明在高频采样时刻收敛到最佳的滤波阶数和步长因子可对系统起到最优滤波效果。研究结果显示信噪比有效提升了94%,拟合优度R~2达到0.997,证明改善的LMS自适应滤波算法可以有效抑制噪声对二次谐波信号的影响。

To further effectively suppress the noise of the detection system and improve the detection accuracy,an improved minimum mean squared deviation adaptive algorithm is studied for noise reduction in the methane concentration detection system of tunable diode laser absorption spectroscopy.Through matlab simulation experiments based on TDLAS technology,the methane detection system is built,and the methane absorption peak position at 1 653.72 nm is selected to analyze the relationship between the filtering order,step factor and sampling period on noise in the LMS adaptive algorithm,and improve the selection of parameters to optimize the noise reduction process with the best filtering effect.It is shown that the optimal filtering effect can be achieved by converging to the optimal filtering order and step factor at the high frequency sampling time.The results show that the signal-to-noise ratio is effectively improved by 94% and the goodness-of-fit R2 reaches 0.997,which proves that the improved LMS adaptive filtering algorithm can effectively suppress the effect of noise on the second harmonic signal.