@inproceedings{02f1a6501d4b450885e65bc523f71158,
title = "An in-orbit correction method based on CNN for the figure errors and component misalignments of TMA telescope",
abstract = "To realize the fast and simple in-orbit aberration correction of TMA telescope, an aberration correction method based on Convolutional Neural Network (CNN) is proposed. CNN is trained to establish the relationship between the defocus point spread function and the misalignments of the secondary mirror. The wavefront aberration caused by the figure errors of the primary mirror and the misalignments of the secondary mirror and the tertiary mirror can be compensated by adjusting the secondary mirror according to the outputs of the well-trained CNN (named as Cor-Net). This method can correct the system aberration quickly and the RMS of the system wavefront aberration is reduced from about 1.5 λ to 0.1 λ by only three correction cycles.",
keywords = "CNN, In-orbit correction, defocus point spread function, figure errors, misalignments",
author = "Bingdao Li and Xiaofang Zhang and Yun Gu and Xinqi Hu",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Optical Design and Testing XII 2022 ; Conference date: 05-12-2022 Through 11-12-2022",
year = "2023",
doi = "10.1117/12.2643878",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Yongtian Wang and Kidger, {Tina E.} and Rengmao Wu",
booktitle = "Optical Design and Testing XII",
address = "United States",
}