针对湍流噪声的傅里叶光谱数据处理方法

Fourier Spectrum Data Processing Method for Turbulent Noise

应用傅里叶变换红外光谱法进行在线工业窑炉高温气体监测时,湍流形成的噪声会影响光谱信噪比和浓度反演的精度。研究新的红外干涉信号-光谱转换的数据处理方法,该方法与传统傅里叶变换的光谱数据处理方法不同,它以零光程差为基准对齐干涉信号,实现多次扫描干涉信号的平均,同时采用复数窗函数与光谱数据卷积的方法来降低光谱旁瓣引起的光谱混叠程度。这种数据处理算法可以减小湍流噪声对气体浓度反演的影响,提高反演精度,减少计算量,提高光谱数据率。以叠加湍流的一氧化碳被动测量实验为例,分析了采用不同数据处理方法得到的光谱信噪比、光谱相关性和浓度反演结果。分析结果表明研究的信号数据处理方法在湍流噪声存在的在线检测中优于传统数据处理方法,采用新的数据处理方法得到的光谱更加精确(光谱相关性更好),气体反演的浓度也更准确,同时可减少系统计算量并缩短系统在线测量的响应时间,这对于在线监测气体浓度的准确性是至关重要的。

When Fourier-transform infrared spectroscopy is used to on-line detection of high-temperature gas in an industrial furnace,the noise formed by turbulence will affect the spectral signal-to-noise ratio and the accuracy of concentration inversion.This paper introduces a new data processing method for infrared interference signal-spectrum conversion.Different from the traditional data processing method for Fourier transform spectra,this method takes the zero optical path difference as the reference to align the interference signals and averages multiple scanning interference signals.It adopts the complex window function and spectral data convolution to reduce the spectral aliasing caused by spectral sidelobes.This data processing algorithm can mitigate the effect of turbulent noise on the gas concentration inversion,improve the inversion accuracy,reduce the amount of system calculation,and increase the spectral data rate.Taking the passive measurement experiment of carbon monoxide with superimposed turbulence as an example,this paper analyzes the spectral signal-to-noise ratio,spectral correlation,and concentration inversion results using different data processing methods.The results show that the proposed signal data processing method is better than traditional data processing methods in the online detection of turbulent noise.The spectrum obtained by the new data processing method is more precise(with better spectral correlation),and the concentration of gas inversion is also more accurate.In addition,it can decrease the amount of system computation and shorten the time of the system’s online measurement.This is essential for the accuracy of online gas concentration monitoring.