SU-Net:用于轨道上的非合作航天器的位置估计网络
Hu Gao1, Zhihui Li2, Ning Wang1
1School of Artificial Intelligence, Beijing Normal University, Beijing, 100000, China.
Scientific reports
|July 21, 2023
概括
这项研究介绍了SU-Net,这是一种新的深度学习模型,用于使用反向合成光圈雷达 (ISAR) 成像进行准确的航天器姿势估计. SU-Net在具有挑战性的太空条件下增强了特征提取,为非合作型航天器实现了最先进的结果.
科学领域:
- 太空飞船工程 太空飞船工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 太空船的姿势估计对于诸如对接和碎片清除等任务至关重要.
- 由于照明不好,分辨率低,数据稀疏,很难估计太空中的姿势.
- 当前的方法与基于太空的成像的独特挑战作斗争.
研究的目的:
- 开发一个深度学习模型,使用ISAR图像准确地估计非合作型航天器的姿势.
- 在不利的太空条件下增强航天器特征提取.
- 为了提高轨道作业的姿势估计的稳定性和准确性.
主要方法:
- 提出了一个新的深度学习架构,SU-Net,基于密集的残余U-Net.
- 嵌入了密集的残留块,以尽量减少在下采样期间的特征损失.
- 利用多头自我注意区块来捕获全球空间信息.
- 员工转移学习和图像增强技术 (对比度提高,降噪) 来解决数据稀疏性问题.
主要成果:
- 在航天器中实现了最先进的性能,构成估计.
- 证明了精度的显著改善,绝对误差范围为0.128°至0.4491°.
- 报告的平均误差约为0.282°,标准偏差约为0.065°.
结论:
- SU-Net架构有效地从ISAR图像中提取特征,以精确地估计航天器的姿势.
- 该模型克服了太空成像中低分辨率,噪音和数据稀疏性的挑战.
- SU-Net 提供了一个强大的解决方案,用于关键的轨道航天器操作.
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