A Robust Star Identification Algorithm for Resident Space Object Surveillance

被引:0
|
作者
Wu, Liang [1 ]
Hao, Pengyu [1 ]
Zhang, Kaixuan [1 ]
Zhang, Qian [1 ]
Han, Ru [1 ]
Cao, Dekun [2 ]
机构
[1] Changchun Univ Technol, Dept Comp Sci & Engn, Changchun 130012, Peoples R China
[2] Chang Guang Satellite Technol Co Ltd, Changchun 130000, Peoples R China
来源
PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING | 2024年 / 90卷 / 09期
基金
中国国家自然科学基金;
关键词
GRID ALGORITHM; RECOGNITION; TRACKING;
D O I
10.14358/PERS.23-00086R2
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance.
引用
收藏
页码:565 / 574
页数:68
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