基于Boxcar滤波器的荧光检测二氧化硫信号的去噪研究

荧光法以物质发射的荧光强度与浓度之间的线性关系进行定量分析。当采用荧光法检测大气中的二氧化硫等有害气体时，由于光电探测器等光电元件在无荧光时，仍会产生暗电流噪声，使得本底噪声信号对测量结果有直接的影响。在分析Boxcar滤波算法的基础上，运用小波滤波、EMD滤波和Boxcar滤波三种算法对淹没在本底噪声中的荧光信号进行有效提取和恢复，较之前两种滤波方法，Boxcar滤波效果更佳，对本底噪声的抑制更强，并验证了取样次数影响着荧光信号的信噪比。

Research on Denoising Fluorescence Signal of Sulfur Dioxide by Boxcar Filter

The fluorescence detection method is based on the linear relationship between fluorescence intensity emitted by the material and the concentration of material to make a quantitative analysis. When using the fluorescence detection of atmospheric sulfur dioxide and other harmful gases, photodetectors and other optoelectronic components without fluorescence will continue to produce the dark current noise, and the background signal has a direct impact on the measurement results. On the base of analysis Boxcar filtering algorithm, the research used three algorithms of wavelet filtering, EMD filter and Boxcar filter to extract and recover the fluorescence signal drowned in the noise floor. In comparison with the previous two filtering methods, Boxcar filter had a better effect on the suppression of the background noise. It also verified that the number of sampling affects the fluorescence signal to noise ratio improvement.