张浩越, 赵春梅, 何正斌. TLE机动检测中的抗差高斯混合模型[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230360
引用本文: 张浩越, 赵春梅, 何正斌. TLE机动检测中的抗差高斯混合模型[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230360
ZHANG Haoyue, ZHAO Chunmei, HE Zhengbin. Robust Gaussian Mixture Model for Maneuver Detection Using TLE Data[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230360
Citation: ZHANG Haoyue, ZHAO Chunmei, HE Zhengbin. Robust Gaussian Mixture Model for Maneuver Detection Using TLE Data[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230360

TLE机动检测中的抗差高斯混合模型

Robust Gaussian Mixture Model for Maneuver Detection Using TLE Data

  • 摘要: 基于两行根数(two-line elements,TLE)进行空间目标机动检测是空间态势感知的重要手段。然而两行根数精度有限且带有噪声,机动检测的难度和不确定性较大。为解决该问题,本文结合传统高斯混合模型和抗差估计理论,提出一种抗差高斯混合模型(Robust Gaussian mixturemodel,RGMM)。该模型以TLE半长轴预报误差变化率为基础,在参数解算过程中加入抗差校正函数,通过约束可疑数据的后验概率来提高模型的鲁棒性。以典型的空间目标为例,与传统高斯混合模型对比TLE机动检测性能。实验结果表明,相较于传统高斯混合模型,RGMM检测召回率和F1分数分别提高了60.6%和18%,可有效提高机动事件检测的综合性能,对于空间态势感知中机动事件敏感的任务具有重要意义。

     

    Abstract: Objectives: The number of space objects has grown exponentially due to increased space activities, significantly increasing the collision risk in the Earth’s orbit. Proactive detection and accurate monitoring of changes in the orbits of space objects, including in-orbit collisions and orbital maneuvers of space objects, have become essential. The two-line element (TLE) set is a data format containing information on the movement of objects in the Earth’s orbit. It is the primary resource for monitoring space objects. However, frequent variations in the orbits can cause anomalies in the TLE data, potentially affecting the accuracy of maneuver detection. Therefore, we propose a new maneuver detection methodology that uses a robust Gaussian mixture model (RGMM) to perform probabilistic adjustment of the TLE. Methods: The method used the rate of change of TLE semi-major axis prediction error to detect maneuvers. The robustness of the model is improved by pruning the Gaussian mixture model (GMM) and constraining the a posteriori probability of suspicious data through the incorporation of a robust correction function in the parameter solving process. We compared the performance of the proposed approach for detecting the maneuver of a typical space object with the GMM. Results: The results show that: (1) The RGMM demonstrated greater stability and robustness to outliers in comparison to the GMM. It is effective in accurately modelling the probability distribution of the rate of change of TLE semi-major axis prediction error. (2) Maneuver detection experiments indicated that the RGMM outperformed the GMM. It had a 60.6% higher recall and 18% higher F1 score than the GMM. Conclusions: The appropriate processing of the anomalous data can improve the model’s performance for maneuver detection using TLE data with errors. RGMM can be used to analyze the movements of space objects and ensure greater safety in executing future complex space missions. We plan to improve the model’s performance in future research by incorporating more advanced algorithms.

     

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