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

Weijun Zhong; Hong Liu; Yuhan Gong; Yan Geng; Zhenqian Yang; Congyu Zhao

doi:10.1117/12.2538045

14 February 2020 Space objects attitude discrimination via light-curve measurements and deep convolutional neural networks

Weijun Zhong, Hong Liu, Yuhan Gong, Yan Geng, Zhenqian Yang, Congyu Zhao

Author Affiliations +

Proceedings Volume 11430, MIPPR 2019: Pattern Recognition and Computer Vision; 114300F (2020) https://doi.org/10.1117/12.2538045
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

Citation Download Citation

Weijun Zhong, Hong Liu, Yuhan Gong, Yan Geng, Zhenqian Yang, and Congyu Zhao "Space objects attitude discrimination via light-curve measurements and deep convolutional neural networks", Proc. SPIE 11430, MIPPR 2019: Pattern Recognition and Computer Vision, 114300F (14 February 2020); https://doi.org/10.1117/12.2538045

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