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Received: 2019-10-30

Revision Accepted: 2020-04-14

Crosschecked: 2020-07-20

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Hao Wang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.11 P.1661-1670


An artificial intelligence enhanced star identification algorithm

Author(s):  Hao Wang, Zhi-yuan Wang, Ben-dong Wang, Zhuo-qun Yu, Zhong-he Jin, John L. Crassidis

Affiliation(s):  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   roger@zju.edu.cn

Key Words:  Star tracker, Lost-in-space, Star identification, Convolutional neural network

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Hao Wang, Zhi-yuan Wang, Ben-dong Wang, Zhuo-qun Yu, Zhong-he Jin, John L. Crassidis. An artificial intelligence enhanced star identification algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(11): 1661-1670.

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publisher="Zhejiang University Press & Springer",

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An artificial intelligence enhanced star identification algorithm is proposed for star trackers in lost-in-space mode. A convolutional neural network model based on Vgg16 is used in the artificial intelligence algorithm to classify star images. The training dataset is constructed to achieve the networks’ optimal performance. Simulation results show that the proposed algorithm is highly robust to many kinds of noise, including position noise, magnitude noise, false stars, and the tracker’s angular velocity. With a deep convolutional neural network, the identification accuracy is maintained at 96% despite noise and interruptions, which is a significant improvement to traditional pyramid and grid algorithms.


王昊1,王志远1,王本冬1,于卓群1,金仲和1,John L.CRASSIDIS2



Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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