基于高斯过程回归的光电检测系统指向误差补偿

Q3 Engineering
Qijian Tang, Qingping Yang, Xiangjun Wang, A. Forbes
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引用次数: 2

摘要

瞄准精度是光电探测系统的一个重要指标,对系统性能有重要影响。然而,由于制造和装配过程中的不对准、不垂直以及相机畸变和角度传感器误差等传感器误差,对指向精度产生了重大影响。在使用系统之前,应该对这些误差进行补偿。首先提出了参数模型来补偿误差,然后又提出了加入非线性的半参数模型。这两种方法都必须首先对参数部分进行分析,这是一个复杂而不准确的过程。本文提出了一种不含机械尺寸等先验信息的非参数模型。它只取决于测试数据。使用高斯过程回归来表示数据之间的关系并预测补偿后的输出。检验结果表明,回归方差减小了一个数量级以上,均值也显著减小,指向误差得到了很好的改善。因此,基于高斯过程的非参数模型是一种有效而有力的指向误差补偿工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Pointing error compensation of electro-optical detection systems using Gaussian process regression
Pointing accuracy is an important indicator for electro-optical detection systems, as it significantly affects the system performance. However, as a result of misalignment, nonperpendicularity in the manufacturing and assembly processes, as well as the sensor errors such as camera distortion and angular sensor error, the pointing accuracy is significantly affected. These errors should be compensated before using the system. Parametric models are firstly proposed to compensate for the errors, whilst the semi-parametric models with the nonlinearity added are also put forward. Both methods should analyse the parametric part first, which is a complicated and inaccurate process. This paper presents a nonparametric model, without any prior information about mechanical dimensions, etc. It depends only on the test data. Gaussian Process regression is used to represent the relationship between data and predict the compensated output. The test results have shown that the regression variances have decreased by more than an order of magnitude, and the means have also been significantly reduced, with the pointing error well improved. The nonparametric model based on Gaussian Process is thus demonstrated to be an effective and powerful tool for the pointing error compensation.
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来源期刊
International Journal of Metrology and Quality Engineering
International Journal of Metrology and Quality Engineering Engineering-Safety, Risk, Reliability and Quality
CiteScore
1.70
自引率
0.00%
发文量
8
审稿时长
8 weeks
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