基于经验模态分解的SO_2浓度检测信号处理方法

Signal Processing Method Based on Empirical Mode Decomposition in the SO_2 Concentration Monitoring

荧光法测量SO2浓度是大气监测中常用的检测手段.双光路技术可以消除光源和光路的噪音干扰,但光电转换器件在激发光照射下产生的背景噪音也会影响定量分析的准确度.本文采用经验模态分解滤波算法降低检测中存在的各种噪音,在实现有效降噪的基础上较好地保存了有用的原始信号.仿真结果表明,针对SO2浓度检测系统,利用经验模态分解去噪后信号的信噪比达到204.273 6,均方误差为0.007 0.与小波去噪法相比,经验模态分解检测效果更佳.最后将经两组不同方法处理后的信号应用于气体检测系统中,实验数据的线性关系更好地验证了经验模态分解方法应用到浓度检测系统的可行性.

The fluorescent spectrometry is a common method to detect the concentration of SO2 in the atmospheric monitoring. The detection system adopting double light paths can eliminate the noise jamming from the light source and light path. However, background noise produced by photoelectric converting device under the laser irradiation will also affect the accuracy of quantitative analysis. Empirical Mode Decomposition （EMD） filtering algorithm was used to reduce various kinds of noise existing in the detection, which could retain the useful original signal and reduce the noise effectively. The simulation results show that for the sulfur dioxide concentration detection system, using EMD de- noising, the Signal Noise Ratio （SNR） increases to 204. 273 6, and the Mmean Squared Error （MSE） is 0. 007 0. Compared with the wavelet de-noising method, the effect of EMD detection is much better. Finally, the signal processed with the two signal methods were applied to the gas detection system. From the experimental data of the linear relationship, it can be concluded that the EMD method applied to the proposed concentration detection system is feasible.