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Proceedings Paper

Statistical learning modeling method for space debris photometric measurement
Author(s): Wenjing Sun; Jinqiu Sun; Yanning Zhang; Haisen Li
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Paper Abstract

Photometric measurement is an important way to identify the space debris, but the present methods of photometric measurement have many constraints on star image and need complex image processing. Aiming at the problems, a statistical learning modeling method for space debris photometric measurement is proposed based on the global consistency of the star image, and the statistical information of star images is used to eliminate the measurement noises. First, the known stars on the star image are divided into training stars and testing stars. Then, the training stars are selected as the least squares fitting parameters to construct the photometric measurement model, and the testing stars are used to calculate the measurement accuracy of the photometric measurement model. Experimental results show that, the accuracy of the proposed photometric measurement model is about 0.1 magnitudes.

Paper Details

Date Published: 8 March 2017
PDF: 5 pages
Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 1025530 (8 March 2017); doi: 10.1117/12.2265967
Show Author Affiliations
Wenjing Sun, Northwestern Polytechnical University (China)
Jinqiu Sun, Northwestern Polytechnical Univ. (China)
Yanning Zhang, Northwestern Polytechnical Univ. (China)
Haisen Li, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 10255:
Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016
Yueguang Lv; Jialing Le; Hesheng Chen; Jianyu Wang; Jianda Shao, Editor(s)

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