Int. J. Metrol. Qual. Eng.
Volume 12, 2021
Topical Issue - Advances in Metrology and Quality Engineering
|Number of page(s)||6|
|Published online||10 September 2021|
Pointing error compensation of electro-optical detection systems using Gaussian process regression
Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen Guangdong 518060, PR China
2 MOEMS Education Ministry Key Laboratory, Tianjin University, Tianjin 300072, PR China
3 College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
4 National Physical Laboratory, Hampton Road, Teddington, Middlesex TW11 0LW, UK
* Corresponding author: QingPing.Yang@brunel.ac.uk
Accepted: 10 August 2021
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.
Key words: Gaussian process regression / pointing error / nonparametric model / EODS
© Q. Tang et al., Published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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