同时进行atmospheric pressure和温度的最小互信息检索
A Minimized Mutual Information retrieval for simultaneous atmospheric pressure and temperature
摘要：
美国航天局和欧空局在火星痕量气体轨道器（TGO）方面的合作主要侧重于利用掩星傅里叶变换光谱仪探测大气痕量气体的时空变化。为了从这些测量中检索任何痕量气体混合比，必须准确地知道大气压力和温度。因此，提出了一个原型检索模型，用于从宽带高分辨率红外傅里叶变换光谱仪实验中确定压力和温度，该实验以太阳为源，搭载在绕火星轨道运行的航天器上。结果表明，在不考虑状态空间参数先验信息和其他约束条件的情况下，利用正则化总体最小二乘法和选择的微窗口对，可以从遥感光谱测量中唯一地求解压力和温度。合适的微窗口对的选择是基于信息内容分析。一个比较的信息含量计算使用贝叶斯理论和超空间制定了解测量中可用的信息。利用互信息最小化的方法寻找合适的微窗口，同时反演压力和温度。
英文摘要：
The primary focus of the Mars Trace Gas Orbiter (TGO) collaboration between NASA and ESA is the detection of the temporal and spatial variation of the atmospheric trace gases using a solar occultation Fourier transform spectrometer. To retrieve any trace gas mixing ratios from these measurements, the atmospheric pressure and temperature have to be known accurately. Thus, a prototype retrieval model for the determination of pressure and temperature from a broadband high resolution infrared Fourier Transform spectrometer experiment with the Sun as a source on board a spacecraft orbiting the planet Mars is presented. It is found that the pressure and temperature can be uniquely solved from remote sensing spectroscopic measurements using a Regularized Total Least Squares method and selected pairs of micro-windows without any a-priori information of the state space parameters and other constraints. The selection of the pairs of suitable micro-windows is based on the information content analysis. A comparative information content calculation using Bayes theory and a hyperspace formulation are presented to understand the information available in measurement. A method of minimization of mutual information is used to search the suitable micro-windows for a simultaneous pressure and temperature retrieval.
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