地基观测数据估计空间目标特性研究进展
doi: 10.11728/cjss2025.06.2024-0181 cstr: 32142.14.cjss.2024-0181
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李徽 男, 1998年6月出生于安徽省淮北市, 现为中国科学院大学天文与空间科学学院硕士研究生, 主要研究方向为空间目标特性分析建模. E-mail: lihui@ynao.ac.cn -
李荣旺 男, 1985年1月出生于云南省大理白族自治州, 现为中国科学院云南天文台应用天文研究团组研究员, 硕士生导师, 中国科学院青促会会员, 云南省兴滇英才青年人才, 主要研究方向为人造天体动力学、空间碎片旋转特性等. E-mail: lirw@ynao.ac.cn
作者简介:
通讯作者:
Research Progress on Estimating Space Objects Characteristics Using Ground-based Observation Data
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摘要: 空间活动日益频繁, 目标解体和碰撞生成大量空间碎片可能引发灾难性后果, 因此对空间目标进行监测与表征变得尤为重要. 空间目标的姿态、形状、材质等特性信息对于目标识别、碰撞规避和主动清除具有重要意义. 对空间态势感知领域的重要学术会议AMOS (Advanced Maui Optical and Space Surveillance Technologies)近年来文集中的相关技术论文进行系统分析, 涵盖地基观测数据在空间目标表征中的应用, 从姿态估计、形状估计到姿态演化以及机器学习辅助决策等, 研究结果可为空间目标综合分析提供丰富的技术手段和估计方法, 并为未来空间目标表征技术发展提供了有价值的参考. 此外, 针对特性估计相关数据日益丰富、反演算法愈发成熟的现状和趋势, 提出中国应该建立体系化的空间目标特性估计机制新思路.Abstract: With the rapid increase in space activities, the proliferation of space debris from space objects’ breakup and collisions poses catastrophic risks to orbital operations. Consequently, the monitoring and characterization of space objects, including their attitude, shape, and material properties, have become critical for target identification, collision avoidance, and active debris removal. This study systematically reviews relevant technical papers from recent proceedings of the AMOS Conference, a key academic forum in space situational awareness. The analysis encompasses ground-based observational data applications in space object characterization, attitude estimation, shape reconstruction, attitude evolution, and machine learning-assisted decision-making. These methodologies provide a comprehensive toolkit for the integrated analysis of space objects, offering valuable insights for future advancements in characterization technologies. Against the current situation and trend of increasingly abundant data related to characteristic estimation and increasingly mature propagation algorithms, this paper proposes a new idea that China should establish a systematic space target characteristic estimation mechanism.
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Key words:
- Astrometry /
- Space objects /
- Light curves /
- Space situational awareness /
- Feature characterization /
- Machine learning
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图 1 J2000历元赤道坐标下长征三号乙(CZ-3B)运载火箭上面级可能的翻滚轴方向
Figure 1. Possible tumble axis directions of CZ-3B upper stage in equatorial coordinates at J2000 epoch
图 2 各参数后验分布的最终估计
Figure 2. Final estimates of the posterior distributions of each parameter
图 3 航天飞机RGB图像第1, 5, 10, 100帧
Figure 3. The 1st, 5th, 10th, and 100th frame of the RGB shuttle sequence
图 4 在测试集上评估多分类模型得到的混淆矩阵
Figure 4. Confusion matrix of the multiclassification model evaluated on the test subset
图 6 空间目标特性估计体系
Figure 6. Schematic diagram of the space object characteristics estimation system
表 1 不同类型数据对17种特性描述的支持情况
Table 1. Support for 17 properties description of different types of data
目标特性 光学 雷达 红外 遥测信号 目标测量特性 位置 是 是 是 是 速度 是 是 是 是 目标物理特性 尺寸 ― 是 ― ― 质量 ― 支持 ― ― 平台类型 是 支持 ― ― 目标旋转特性 旋转速度 是 是 是 ― 旋转轴 是 是 是 ― 目标推进特性 推进类型 生命模式 生命模式 生命模式 ― 可用燃料 支持 ― ― ― 速度变化 生命模式 生命模式 生命模式 ― 目标电量特性 电能产生 ― ― 支持 ― 电能消耗 ― ― 支持 支持 目标其他特性 线框简图 是 是 ― ― 材料成分 是 ― ― ― 热辐射 ― ― 是 ― 任务状态 支持 支持 支持 是 气体排放 ― ― 是 ― 
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