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Proceedings Paper

A star identification algorithm for large FOV observations
Author(s): Yu Duan; Zhaodong Niu; Zengping Chen
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Paper Abstract

Due to the broader extent of observation and higher detection probability of space targets, large FOV (field of vision) optical instruments are widely used in astronomical applications.. However, the high density of observed stars and the distortion of the optical system often bring about inaccuracy in star locations. So in large FOV observations, many conventional star identification algorithms do not show very good performance. In this paper, we propose a star identification method with a low requirement for observation accuracy and thus suitable for large FOV circumstances. The proposed method includes two stages. The former is based on the match group algorithm, in addition to which we exploit the information of differential angles of inclination for verification. The inclinations of satellite stars are computed by reference to the selected pole stars. Then we obtain a set of identified stars for further recognition. The latter stage involves four steps. First, we derive the relationship between the rectangular coordinates of catalog stars and sensor stars with the identified locations obtained. Second, we transform the sensor coordinates to the catalog coordinates and find the catalog stars at close range as candidates. Third, we calculate the angle of inclination of each unidentified sensor star in relation to the nearest previously identified one, and the angular separation between them as well, to compare with those of the candidates. At last, candidates satisfying the limitations are considered the appropriate correspondences. The experimental results show that in large FOV observations, the proposed method presents better performance in comparison with several typical star identification methods in open literature.

Paper Details

Date Published: 18 October 2016
PDF: 9 pages
Proc. SPIE 10004, Image and Signal Processing for Remote Sensing XXII, 100041G (18 October 2016); doi: 10.1117/12.2240908
Show Author Affiliations
Yu Duan, National Univ. of Defense Technology (China)
Zhaodong Niu, National Univ. of Defense Technology (China)
Zengping Chen, National Univ. of Defense Technology (China)

Published in SPIE Proceedings Vol. 10004:
Image and Signal Processing for Remote Sensing XXII
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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