Fast Variance Component Estimation in GOCE Data Processing
Fast Variance Component Estimation in GOCE Data Processing
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DOI：
10.1007/978-3-642-10634-7_25
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发表时间：
2010
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影响因子：
--
通讯作者：
J. Brockmann;W. Schuh
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文献类型：
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作者：
J. Brockmann;W. Schuh
为了处理GOCE（重力场和稳态海洋环流探测器）数据，设计了一个程序systempcgma（预处理共轭梯度多重调整），作为根据球谐分析确定地球重力场的定制解决方案策略。在GOCE-HPF内（高级处理设备）pcgma算法与调谐机的目的一起工作，因为它用于优化滤波器设计并确定关于卫星到卫星跟踪（sst）数据的组合的最佳方差分量，卫星重力梯度测量（sgg）数据和关于重力场平滑度的附加先验信息pcgmais基于迭代共轭梯度（CG）算法的扩展版本，其允许在观测和正常方程方面进行数据组合。处理两种迭代方法（方差分量估计（VCE）和使用CG的参数估计）的嵌套的基本先决条件是高效且快速的实现，因为VCE需要系统的重复解。在本文中，我们将展示如何嵌套可以组织在一个最佳的方式。我们将集中讨论CG迭代步骤的减少。
For the processing of GOCE (Gravity Field and steady-state Ocean Circulation Explorer) data the program systempcgma(Preconditioned Conjugate Gradient Multiple Adjustment) was designed as a tailored solution strategy for the determination of the Earth’s gravity field in terms of a spherical harmonic analysis. Within GOCE-HPF (High Level Processing Facility) thepcgmaalgorithm works with the purpose of a tuning machine in that it is used to optimize the filter design and to determine optimal variance components with respect to the combination of satellite-to-satellite tracking (sst) data, satellite gravity gradiometry (sgg) data and additional prior information about the smoothness of the gravity field (the latter especially with regard to the polar regions).pcgmais based on an extended version of the iterative conjugate gradient (CG) algorithm, which allows for data combination in terms of observation and normal equations. A basic prerequisite for handling the nesting of the two iterative methods (variance component estimation (VCE) and parameter estimation using CG) is an efficient and fast implementation, because the VCE requires a repeated solution of the system. In this paper we will show how the nesting can be organized in an optimal way. We will concentrate on the reduction of CG iteration steps.