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CLC number: V447

On-line Access: 2020-11-13

Received: 2019-10-30

Revision Accepted: 2020-04-14

Crosschecked: 2020-07-20

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Hao Wang

https://orcid.org/0000-0002-0383-7258

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

http://doi.org/10.1631/FITEE.1900590


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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pages="1661-1670",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900590"
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Abstract: 
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.

一种基于人工智能的星图识别算法


王昊1,王志远1,王本冬1,于卓群1,金仲和1,John L.CRASSIDIS2
1浙江大学航空航天学院,中国杭州市,310027
2纽约州立大学布法罗分校机械与航天工程系,美国纽约州艾摩斯特市,14260-4400

摘要:针对星敏感器在姿态失锁状态下的星图识别问题,提出一种基于人工智能的星图识别算法。该方法基于Vgg16的卷积神经网络模型对星图分类。为达到最优性能,构建了一个星图训练集。仿真结果表明该算法对星图识别问题中的多种噪声具有强鲁棒性,包括星点位置噪声、星等噪声、伪星以及星敏感器角速度。在多种噪声影响下,该方法的识别率依然保持在96%,相比传统的金字塔形算法和栅格算法有显著提升。

关键词:星敏感器;姿态失锁;星图识别;卷积神经网络

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

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