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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Project supported by the National Natural Science Foundation of China (No. 6152403
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Hao WANG designed the research and drafted the manuscript. Zhi-yuan WANG and Ben-dong WANG trained thenetwork andprocessedthe data. Zhuo-qunYU helped acquire the data. Zhong-he JIN and John L. CRASSIDIS offered advice. Hao WANG and Zhi-yuan WANG revised and finalized the paper.
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Hao WANG, Zhi-yuan WANG, Ben-dong WANG, Zhuo-qun YU, Zhong-he JIN, and John L. CRASSIDIS declare that they have no conflict of interest.
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Wang, H., Wang, Zy., Wang, Bd. et al. An artificial intelligence enhanced star identification algorithm. Front Inform Technol Electron Eng 21, 1661–1670 (2020). https://doi.org/10.1631/FITEE.1900590
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DOI: https://doi.org/10.1631/FITEE.1900590