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反射转镜式干涉光谱仪光程差非线性修正方法比较 被引量:9

Comparison of Correction Methods for Nonlinear Optic Path Difference of Reflecting Rotating Fourier Transform Spectrometer
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摘要 介绍了反射转镜式干涉光谱仪的原理。转镜的转动会导致光程差存在非线性问题,是转镜式干涉光谱仪普遍存在的问题。由于光程差的非线性会导致反演光谱的严重失真,因此需要对其进行修正处理。对非线性修正问题,目前常用的有光程差替换法、干涉图二次采样法和NUFFT法。实验结果证明,NUFFT法和光程差替换法运算精度较高,相对偏差优于0.13%,NUFFT法精度又略高于光程差替换法;在运行效率上,NUFFT法运行速度最快,其次为光程差替换法。由于干涉图本身是一个剧变信号(特别是在零光程差附近),不适合进行多项式拟合,所以干涉图二次采样法运算精度最低,另外,由于干涉图二次采样法需要做分段拟合运算,所以运行效率也最低,从而可以认为NUFFT方法是目前最适用于反射转镜式干涉光谱仪非线性采样误差修正的方法。 The principle of reflecting rotating Fourier transform spectrometer was introduced in the present paper.The nonlinear problem of optical path difference (OPD) of rotating Fourier transform spectrometer universally exists,produced by the rotation of rotating mirror.The nonlinear OPD will lead to fictitious recovery spectrum,so it is necessary to compensate the nonlinear OPD.Three methods of correction for the nonlinear OPD were described and compared in this paper,namely NUFFT method,OPD replace method and interferograms fitting method.The result indicates that NUFFT was the best method for the compensation of nonlinear OPD,OPD replace method was better,its precision was almost the same as NUFFT method,and their relative error are superior to 0.13%,but the computation efficiency of OPD replace method is slower than NUFFT method,while the precision and computation efficiency of interferograms fitting method are not so satisfied,because the interferograms are rapid fluctuant especially around the zero optical path difference,so it is unsuitable for polynomial fitting,and because this method needs piecewise fitting,its computation efficiency is the slowest,thus the NUFFT method is the most suited method for the nonlinear OPD compensation of reflecting rotating Fourier transform spectrometer.
出处 《光谱学与光谱分析》 SCIE EI CAS CSCD 北大核心 2010年第6期1678-1682,共5页 Spectroscopy and Spectral Analysis
基金 国家自然科学基金项目(40805013) 国家重点基础研究发展计划项目(2009CB724005)资助
关键词 光谱仪 反射转镜 光程差 非线性采样 Spectrometer Rotating mirror Optical path difference(OPD) Nonlinear sample
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