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Active alignment of space astronomical telescopes by matching arbitrary multi-field stellar image features
Author(s) -
Xiaoquan Bai,
Guohao Ju,
Boqian Xu,
Yan Gao,
Chunyue Zhang,
Shuaihui Wang,
Hao Ma,
Shuyan Xu
Publication year - 2021
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.432412
Subject(s) - wavefront , optics , physics , telescope , deformable mirror , adaptive optics , image quality , computer science , field of view , position (finance) , field (mathematics) , optical aberration , matching (statistics) , computer vision , image (mathematics) , statistics , mathematics , finance , pure mathematics , economics
Space-based optical astronomical telescopes are susceptible to mirror misalignments due to space disturbance in mechanics and temperature. Therefore, it is of great importance to actively align the telescope in orbit to continuously maintain imaging quality. Traditional active alignment methods usually need additional delicate wavefront sensors and complicated operations (such as instrument calibration and pointing adjustment). This paper proposes a novel active alignment approach by matching the geometrical features of several stellar images at arbitrary multiple field positions. Based on nodal aberration theory and Fourier optics, the relationship between stellar image intensity distribution and misalignments of the system can be modeled for an arbitrary field position. On this basis, an objective function is established by matching the geometrical features of the collected multi-field stellar images and modeled multi-field stellar images, and misalignments can then be solved through nonlinear optimization. Detailed simulations and a real experiment are performed to demonstrate the effectiveness and practicality of the proposed approach. This approach eliminates the need for delicate wavefront sensors and pointing adjustment, which greatly facilitates the maintainance of imaging quality.

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