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Proceedings Paper

Space objects attitude discrimination via light-curve measurements and deep convolutional neural networks
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Paper Abstract

The attitude discrimination of objects in geosynchronous earth orbit (GEO) is vital for detailed understanding of the space objects population in Space Situational Awareness(SSA) domain. In this paper, a data-driven method is presented to discriminate the attitude of GEO space objects based on a deep learning approach. The convolutional neural networks(CNNs) is designed and trained to validate the ability to discriminate the attitude of GEO space objects from collected light-curve measurements. The temporal variation of in apparent object brightness across observations between the attitude stabilized and rotated space objects is exploited. Thousands of light-curves of attitude stabilized and rotated space objects are selected and transformed into the spectrum figures by the short-time Fourier transform (STFT). These spectrum figures are employed to train the deep CNNs and to evaluate the performance on the limited training set. Comparing with the traditional machine learning algorithms, the CNNs has a better performance on the attitude discrimination accuracy with the measured data.

Paper Details

Date Published: 14 February 2020
PDF: 8 pages
Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300F (14 February 2020); doi: 10.1117/12.2538045
Show Author Affiliations
Weijun Zhong, Northwestern Polytechnical Univ. (China)
State Key Lab. of Astronautic Dynamics (China)
Xi'an Satellite Control Ctr. (China)
Hong Liu, State Key Lab. of Astronautic Dynamics (China)
Xi'an Satellite Control Ctr. (China)
Yuhan Gong, State Key Lab. of Astronautic Dynamics (China)
Xi'an Satellite Control Ctr. (China)
Yan Geng, State Key Lab. of Astronautic Dynamics (China)
Xi'an Satellite Control Ctr. (China)
Zhenqian Yang, State Key Lab. of Astronautic Dynamics (China)
Xi'an Satellite Control Ctr. (China)
Congyu Zhao, State Key Lab. of Astronautic Dynamics (China)
Xi'an Satellite Control Ctr. (China)


Published in SPIE Proceedings Vol. 11430:
MIPPR 2019: Pattern Recognition and Computer Vision
Nong Sang; Jayaram K. Udupa; Yuehuan Wang; Zhenbing Liu, Editor(s)

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