为了提高傅里叶光谱仪光谱定标精度, 减小光谱定标误差, 基于风云四号大气垂直探测仪实验室气体池光谱定标数据, 进行傅里叶光谱仪高精度光谱定标算法研究。 首先, 分析了傅里叶光谱仪的分光原理, 并在对参考激光波数漂移、 光线离轴、 以及有限视场引起光谱波数偏移的原理进行分析后, 得出傅里叶光谱仪光谱定标公式及定标参数的计算方法； 接着分析了快速傅里叶变换(FFT)的栅栏效应和干涉图截断产生的sinc函数造成的光谱定标误差较大的原因； 然后通过对比几种不同的光谱细化方法, 选择高效的快速Chirp Z-transform(CZT)进行光谱细化, 解决FFT光谱分辨率较低导致光谱误差较大的问题； 通过对气体池参考气体在HITRAN数据库中的理论谱线, 用Gaussian线型展宽并卷积sinc函数处理后作为光谱定标参考谱线的方式, 减小由sinc函数引起的谱线间串扰造成的光谱定标误差, 从而提高光谱定标精度。 最后, 使用实验测得的数据对该光谱定标算法进行验证, 对比使用CZT细化光谱前后定标误差, 和参考谱线处理前后的定标误差, 证明该算法可以有效提高光谱定标精度, 最高可将光谱定标误差减小10倍以上。

In order to improve the Fourier spectrometer spectral calibration accuracy and reduce spectral calibration error, we studied the Fourier spectrometer spectral calibration algorithm. The study is based on the data of spectral calibration experiment of the atmospheric vertical detector, which is a payload of FY-4 meteorology satellite. Firstly, the spectroscopic principle of the Fourier spectrometer is analyzed. Secondly, after analyzing the principle of the spectrum shift caused by the reference laser wave number drift, the light off-axis, and the limited field of view, the Fourier spectrometer spectral calibration formula and the calibration parameter calculation method are obtained. Then, the reasons for the large spectral calibration error caused by the barrier effect of Fast Fourier Transform(FFT) and the toe function generated by the truncation of the interferogram are analyzed. By comparing several different spectral refinement methods, fast Chirp Z-transform(CZT) for spectral refinement is selected to solve the problem of lower spectral resolution of FFT resulting in larger spectral error. Gaussian expansion and convoluting the sinc function of reference gas spectral can reduce the calibration error caused by limited optical path difference. At last, the spectral calibration algorithm is validated by using experimentally measured data. By comparing the calibration errors before and after refining the spectra using CZT, and the calibration errors before and after the processing of the reference gas spectrum, the algorithm is proved to be effective in improving spectral calibration accuracy. Spectral calibration error can be reduced about 10 times at most.