首页> 外文期刊>Artificial Satellites: A journal of Planetary geodesy >A Study on Along-Track and Cross-Track Noise of Altimetry Data by Maximum Likelihood: Mars Orbiter Laser Altimetry (Mola) Example
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A Study on Along-Track and Cross-Track Noise of Altimetry Data by Maximum Likelihood: Mars Orbiter Laser Altimetry (Mola) Example

机译:通过最大似然法研究测高数据沿轨道和跨轨道的噪声:火星轨道激光测高(Mola)示例

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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.
机译:这项工作研究了沿火星轨道激光高度计(MOLA)的轨道轨迹收集的数据的空间相关性,并特别关注了协方差矩阵中的噪声方差问题。关于最小二乘配置(LSC),仍在讨论轨道或轮廓数据沿轨道和跨轨道方向的不同相关参数的问题。在此类数据的背景下,经常考虑沿轨道和横向方向的不同间距以及各向异性问题。因此,在这项工作中,使用MOLA数据样本分析了问题。本文的分析着重于与数据中存在的白噪声相对应的先验误差,并通过在两个垂直方向上的最大似然(ML)估计来执行。另外,假定平面协方差模型的相关长度由ML确定,并将其拟合到经验协方差函数(ECF)中。一起考虑的所有估计值证实了沿轨迹和横向的不同数据分辨率对协方差参数的重大影响。

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