A comparison of statistical methods for fitting telescope pointing models

被引：1
|
作者：
Meeks, RL [1 ]
机构：
[1] EOS Technol Inc, Tucson, AZ 85705 USA
来源：
MODELING AND SYSTEMS ENGINEERING FOR ASTRONOMY | 2004年 / 5497卷
关键词：
telescopes; Bayesian regression; ridge regression; bootstrapping; pointing models;
D O I：
10.1117/12.550754
中图分类号：
P1 [天文学];
学科分类号：
0704 ;
摘要：
Telescope mount models use a mathematical model to introduce commanded position adjustments to compensate for predictable pointing errors. The parameters of the model are estimated from observed pointing deviations on a set of calibration stars. These calibration measurements generally contain random noise and other features that limit the precision of the parameter estimates and ultimately degrade pointing. This paper compares the ability of various statistical solution methods to improve the precision of the parameter estimates and improve pointing.
引用
收藏
页码：486 / 496
页数：11
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