基于极大似然的测高数据沿轨和交叉轨噪声研究——以火星轨道器激光测高为例

IF 0.7 Q4 ASTRONOMY & ASTROPHYSICS
W. Jarmołowski, Jacek Łukasiak
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引用次数: 3

摘要

摘要研究了火星轨道激光高度计(MOLA)轨道数据的空间相关性,重点研究了协方差矩阵中的噪声方差问题。关于最小二乘配置(LSC),轨道或剖面数据沿轨道方向和交叉方向的相关参数不同的问题仍在讨论中。在这类数据的背景下,经常考虑到沿轨道和横向的不同间距以及各向异性问题。因此,本文使用MOLA数据样本对问题进行了分析。本文的分析主要集中在与数据中存在的白噪声相对应的先验误差上,并通过两个垂直方向的最大似然(ML)估计进行。此外,通过ML拟合经验协方差函数(ECF)确定了假设平面协方差模型的相关长度。综合考虑的所有估计都证实了沿航迹和横向不同数据分辨率对协方差参数的实质性影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on Along-Track and Cross-Track Noise of Altimetry Data by Maximum Likelihood: Mars Orbiter Laser Altimetry (Mola) Example
Abstract The work investigates the spatial correlation of the data collected along orbital tracks of Mars Orbiter Laser Altimeter (MOLA) with a special focus on the noise variance problem in the covariance matrix. The problem of different correlation parameters in along-track and crosstrack directions of orbital or profile data is still under discussion in relation to Least Squares Collocation (LSC). Different spacing in along-track and transverse directions and anisotropy problem are frequently considered in the context of this kind of data. Therefore the problem is analyzed in this work, using MOLA data samples. The analysis in this paper is focused on a priori errors that correspond to the white noise present in the data and is performed by maximum likelihood (ML) estimation in two, perpendicular directions. Additionally, correlation lengths of assumed planar covariance model are determined by ML and by fitting it into the empirical covariance function (ECF). All estimates considered together confirm substantial influence of different data resolution in along-track and transverse directions on the covariance parameters.
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CiteScore
1.00
自引率
11.10%
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