Statistical learning modeling method for space debris photometric measurement

Wenjing Sun; Jinqiu Sun; Yanning Zhang; Haisen Li

doi:10.1117/12.2265967

8 March 2017 Statistical learning modeling method for space debris photometric measurement

Wenjing Sun, Jinqiu Sun, Yanning Zhang, Haisen Li

Proceedings Volume 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016; 1025530 (2017) https://doi.org/10.1117/12.2265967
Event: Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 2016, Jinhua, Suzhou, Chengdu, Xi'an, Wuxi, China

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.

Citation Download Citation

Wenjing Sun, Jinqiu Sun, Yanning Zhang, and Haisen Li "Statistical learning modeling method for space debris photometric measurement", Proc. SPIE 10255, Selected Papers of the Chinese Society for Optical Engineering Conferences held October and November 2016, 1025530 (8 March 2017); https://doi.org/10.1117/12.2265967

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