CLC number: V447
On-line Access: 2020-11-13
Received: 2019-10-30
Revision Accepted: 2020-04-14
Crosschecked: 2020-07-20
Cited: 0
Clicked: 4708
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.
@article{title="An artificial intelligence enhanced star identification algorithm",
author="Hao Wang, Zhi-yuan Wang, Ben-dong Wang, Zhuo-qun Yu, Zhong-he Jin, John L. Crassidis",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="11",
pages="1661-1670",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900590"
}
%0 Journal Article
%T An artificial intelligence enhanced star identification algorithm
%A Hao Wang
%A Zhi-yuan Wang
%A Ben-dong Wang
%A Zhuo-qun Yu
%A Zhong-he Jin
%A John L. Crassidis
%J Frontiers of Information Technology & Electronic Engineering
%V 21
%N 11
%P 1661-1670
%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900590
TY - JOUR
T1 - An artificial intelligence enhanced star identification algorithm
A1 - Hao Wang
A1 - Zhi-yuan Wang
A1 - Ben-dong Wang
A1 - Zhuo-qun Yu
A1 - Zhong-he Jin
A1 - John L. Crassidis
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 11
SP - 1661
EP - 1670
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900590
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.
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