Recursive algorithm for fast GNSS orbit fitting
Recursive algorithm for fast GNSS orbit fitting
复制标题
快速 GNSS 轨道拟合的递归算法
DOI：
10.1007/s10291-014-0424-2
复制
发表时间：
2016-04
期刊：
影响因子：
4.9
通讯作者：
Yang Yuanxi
中科院分区：
文献类型：
--
作者：
Xue Shuqiang;Yang Yuanxi
Gaussian elimination is an efficient and numerically stable algorithm for estimating parameters and their precision. However, before estimating the parameters, it is often prudent to perform statistical tests to achieve the best fitting model. We use Gaussian elimination to select the best fitting model among candidate models. A succinct relationship between the weighted sum of squared residuals and the previous one is revealed by a volume formula. For quick parameter estimation and determination of weighted sum of squared residuals, a recursive elimination algorithm is proposed in the context of Gaussian elimination. In order to improve the model selection efficiency, the parameter estimation and the determination of the weighted sum of squared residuals are carried out in parallel using the proposed recursive elimination algorithm in which the improvement at each recursive stage is judged by the Bayesian information criterion. Ultimately, the computational complexity and numerical stability of the recursive elimination proposed are briefly discussed, and a GNSS orbit interpolation example is used to verify the results. It shows that the proposed recursive elimination algorithm inherits the numerical stability of the Gaussian elimination, and this algorithm can be used to examine the gain from the newly introduced parameter, dynamically assess the fitting model, and fix the optimal model efficiently. The optimal fitting model with the lowest information is very close to the real situation verified by checkpoints.
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影响因子：
4.9
作者：
M. Schenewerk
通讯作者：
M. Schenewerk
DOI：
10.1007/978-1-4842-0154-1
发表时间：
2014-12
期刊：
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影响因子：
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作者：
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通讯作者：
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DOI：
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发表时间：
1998-12
期刊：
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影响因子：
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作者：
G. Lindfield;J. Penny
通讯作者：
G. Lindfield;J. Penny
DOI：
10.53347/rid-35429
发表时间：
2015-04
期刊：
Radiopaedia.org
影响因子：
--
作者：
H. Knipe
通讯作者：
H. Knipe
影响因子：
--
作者：
C. H. Wadleigh;M. Fireman
通讯作者：
C. H. Wadleigh;M. Fireman