基于分段寻优的轨道异常检测方法
doi: 10.11728/cjss2024.05.2023-0122 cstr: 32142.14.cjss2024.05.2023-0122
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曹纪峰 男, 1997年10月出生于河南省周口市, 2022年毕业于三峡大学数学与应用数学专业, 现为中国科学院国家空间科学中心飞行器设计专业在读硕士. 研究方向为空间碎片建模与碰撞预警. E-mail: caojifeng22@mails.ucas.ac.cn
作者简介:
Orbit Anomaly Detection Technology Based on Segmentation Optimization
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摘要: 低轨巨型星座卫星的大量部署严重影响了低轨道卫星的运行安全, 及时发现其轨道异常意义重大. 以星链卫星星座为研究对象进行轨道异常检测,提出了一种基于轨道半长轴数据的改进轨道异常分析方法—分段寻优法. 该方法基于动态优化的思想, 通过对时间窗口内的半长轴数据进行随机分段, 根据每段数据的方差构造损失函数, 以总损失函数的数值大小为判别标准, 通过优化迭代方法得到使总损失函数最小的分段点分布, 得到的分段点即为检测出的异常点. 通过对大量不同数据的测试, 发现该方法对差分处理后的半长轴数据的异常检测效果最好. 利用2023年2月28日发射的星链卫星的TLE(Two-Line Element)数据和星历数据进行了实例验证, 结果证明该方法简单高效, 可以有效检测星座卫星的轨道异常.Abstract: In recent years, the large number of deployments of low-orbit giant constellation satellites have a significant impact on the operational safety of low-orbit satellites in orbit, and it is critical to detect orbital anomalies of constellation satellites in time. As a result, this study picks the Starlink satellite constellation as the research objects for detecting orbit anomaly and presents an improved orbit anomaly detection approach - segmentation optimization. Based on the concept of dynamic optimization, the approach improves the orbit anomaly detection method by using satellite orbit semi-major axis data as an analysis parameter, transforming it from threshold screening to optimization search. First, by assuming the segmentation points and randomly distributing them across the entire semi-major axis data series, the data in the time window are randomly segmented. Based on the variance of the segmented data, each segment's loss function is built. The iterative function are then designed using the loss function for optimization iterations. In order to determine the optimal segmentation method, the random segmentation is finally optimized with the goal of minimizing the sum of the total loss functions. This research finds that the anomaly detection effect is the best for the semi-major axis data by the differential processing after evaluating a range of data. The segmentation optimization approach has various sensitivities to different data after removing the noisy data. In conclusion, this work uses the TLE (Two-line element) data and the ephemeris data from the Starlink satellite for example verification, which was launched on 28 February 2023. The method's effectiveness in detecting orbital anomaly of constellation satellites is demonstrated, which are simple and efficient.
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Key words:
- Semi-major axis change /
- Orbital anomaly /
- Mega-constellations
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图 3 基于TLE数据的迷你星链二代卫星异常点检测结果I (红色为算法检测到的异常点, 蓝色为正常点)
Figure 3. Anonmaly results I for Starlink V2 Mini based on TLE (Red indicates anomaly points detected by the algorithm, while blue indicates normal points)
图 4 基于TLE数据的迷你星链二代卫星异常点检测结果II (红色为算法检测到的异常点, 蓝色为正常点)
Figure 4. Anonmaly results II for Starlink V2 Mini based on TLEs (Red indicates anomaly points detected by the algorithm, while blue indicates normal points)
图 5 基于星历数据迷你星链二代卫星的异常点检测结果I (红色为算法检测到的异常点, 蓝色为正常点)
Figure 5. Anonmaly results I for Starlink V2 Mini based on Ephemeris data (Red indicates anomaly points detected by the algorithm, while blue indicates normal points)
图 6 基于星历数据迷你星链二代卫星的异常点检测结果II (红色为算法检测到的异常点, 蓝色为正常点)
Figure 6. Anonmaly results II for Starlink V2 Mini based on Ephemeris data (Red indicates anomaly points detected by the algorithm, while blue indicates normal points)
图 7 Starlink-30054卫星不同阈值下的检测结果
Figure 7. Detection results under different thresholds for Starlink-30054 satellite
表 1 TLE数据的异常检测结果
Table 1. Anomaly detection results for TLE data
异常点数量 变轨段数量 程序计算 人工判断 漏检率 误判率 程序计算 人工判断 漏检率 误判率 129 139 6.0% 9.1% 85 78 8.9% 10.2% 
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表 2 精密星历平根数的异常点检测结果
Table 2. Outlier detection results for precision ephemeris flat root counts
异常点数量 变轨段数量 程序计算 人工判断 漏检率 误判率 程序计算 人工判断 漏检率 误判率 1855 1719 2.3% 6.7% 922 859 3.1% 6.1%
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