一种基于人工智能的星图识别算法

王昊; 王志远; 王本冬; 于卓群; 金仲和; John L. CRASSIDIS

doi:10.1631/FITEE.1900590

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一种基于人工智能的星图识别算法

常规文章 | Updated：2022-05-19

- 一种基于人工智能的星图识别算法
- An artificial intelligence enhanced star identification algorithm
- 信息与电子工程前沿（英文） 2020年21卷第11期页码：1661-1670
- Affiliations：
  
  School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
  Department of Mechanical and Aerospace Engineering, University at Buffalo, State University of New York, Amherst, NY 14260-4400, USA
- Author bio：
  
  Hao WANG, E-mail: roger@zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 6152403)
- DOI：10.1631/FITEE.1900590
  中图分类号： V447
- 收稿：2019-10-30，
  
  修回：2020-;7-20，
  
  网络出版：2020-08-26，
  
  纸质出版：2020-11
- Accepted：
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王昊, 王志远, 王本冬, 等. 一种基于人工智能的星图识别算法[J]. 信息与电子工程前沿（英文）, 2020,21(11):1661-1670.

Hao WANG, Zhi-yuan WANG, Ben-dong WANG, et al. An artificial intelligence enhanced star identification algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(11): 1661-1670.
王昊, 王志远, 王本冬, 等. 一种基于人工智能的星图识别算法[J]. 信息与电子工程前沿（英文）, 2020,21(11):1661-1670. DOI： 10.1631/FITEE.1900590.

Hao WANG, Zhi-yuan WANG, Ben-dong WANG, et al. An artificial intelligence enhanced star identification algorithm[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(11): 1661-1670. DOI： 10.1631/FITEE.1900590.

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。

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.

关键词

Keywords

references

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