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用于恒星识别的球极模式匹配

Spherical Polar Pattern Matching for Star Identification.

作者信息

Fu Jingneng, Lin Ling, Li Qiang

机构信息

Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.

University of Chinese Academy of Sciences, Beijing 100049, China.

出版信息

Sensors (Basel). 2025 Jul 5;25(13):4201. doi: 10.3390/s25134201.

DOI:10.3390/s25134201
PMID:40648455
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12252525/
Abstract

To endow a star sensor with strong robustness, low algorithm complexity, and a small database, this paper proposes an all-sky star identification algorithm based on spherical polar pattern matching. The proposed algorithm consists of three main steps. First, the guide star is rotated to be a polar star, and the polar and azimuth angles of neighboring stars are used as polar pattern elements of the guide star. Then, the relative azimuth histogram is applied to the spherical polar pattern matching, and a star pair after spherical polar pattern matching is identified through angular distance cross-verification. Finally, a reference star image is generated from the identified star pair to complete the matching process of all guide stars in the field of view. The proposed algorithm is verified by simulation experiments. The simulation results show that for a star sensor with a medium field of view (15° × 15°, 1024 × 1024 pixel) and a limiting magnitude of 6.0 Mv, the required database size is 161 KB. When false and missing star spots account for 50% of the guide stars and the star spot extraction error is 1.0 pixel, the average star identification time is 0.35 ms (@i7-4790), and the identification probability is 99.9%. However, when false and missing star spots account for 100% of the guide stars and the star spot extraction error is 5.0 pixel, the average star identification time is less than 2.0 ms, and the identification probability is 97.1%.

摘要

为了赋予星敏感器强大的鲁棒性、低算法复杂度和小数据库,本文提出了一种基于球极模式匹配的全天星识别算法。该算法主要包括三个步骤。首先,将引导星旋转至北极星,利用相邻恒星的极角和方位角作为引导星的极模式元素。然后,将相对方位直方图应用于球极模式匹配,并通过角距离交叉验证识别球极模式匹配后的星对。最后,从识别出的星对生成参考星图,以完成视场中所有引导星的匹配过程。通过仿真实验对该算法进行了验证。仿真结果表明,对于一个中视场(15°×15°,1024×1024像素)、极限星等为6.0 Mv的星敏感器,所需数据库大小为161 KB。当误识和漏识星点占引导星的50%且星点提取误差为1.0像素时,平均星识别时间为0.35 ms(@i7-4790),识别概率为99.9%。然而,当误识和漏识星点占引导星的100%且星点提取误差为5.0像素时,平均星识别时间小于2.0 ms,识别概率为97.1%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdae/12252525/e89e6c5ce907/sensors-25-04201-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdae/12252525/e89e6c5ce907/sensors-25-04201-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bdae/12252525/e89e6c5ce907/sensors-25-04201-g005.jpg

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本文引用的文献

1
An Efficient and Robust Star Identification Algorithm Based on Neural Networks.一种基于神经网络的高效稳健恒星识别算法。
Sensors (Basel). 2021 Nov 19;21(22):7686. doi: 10.3390/s21227686.
2
The -dimensional -vector and its application to orthogonal range searching.d维向量及其在正交范围搜索中的应用。 (你提供的原文中“-dimensional”和“-vector”处的横杠有误,推测应该是具体维度数和具体向量类型,这里按照正确理解翻译)
Appl Math Comput. 2020 May 1;372. doi: 10.1016/j.amc.2019.125010. Epub 2020 Jan 8.
3
Non-Dimensional Star-Identification.无量纲星识别
Sensors (Basel). 2020 May 9;20(9):2697. doi: 10.3390/s20092697.