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非合作巨型星座的在线机动检测

Online Maneuver Detection of Non-cooperative Mega-constellations

  • 摘要: 随着巨型星座目标数量的增加, 卫星的在线机动检测成为了空间态势感知(Space Situational Awareness, SSA)领域的重要问题. 无奇点推力傅立叶系数(Nonsingular Thrust-Fourier Coefficients, NSTFC)模型可以有效拟合巨型星座的机动, 但基于该模型的在线机动检测方法是否可行还需验证. 该检测方法以估计轨道变化作为分类特征, 以朴素贝叶斯(naive Bayes)作为分类器. 仿真结果显示, 该方法能够准确识别出无机动、升轨和降轨这3类空间事件. 分类的Macro F_1 分数可达 97.1\% . 验证表明, 该方法有望提升编目流程中机动检测的效率与精度.

     

    Abstract: As the number of mega-constellations increases, maneuver detection of non-cooperative satellites becomes an important part of Space Situational Awareness (SSA). The nonsingular thrust-Fourier coefficients (NSTFC) model can effectively fit the maneuvers of mega-constellations, but the feasibility of the online maneuver detection method based on this model needs to be verified. The method uses the orbital variations estimated by a filter as the classification attributes and uses the Naive Bayes as the classifier. The calculation results based on simulation show that this method can effectively identify three classes of space events: no maneuver, orbit raising, and orbit descending. The Macro F_1 score of classification can reach as high as 97.1 \% . The verification shows that this method may help improve the efficiency and accuracy of maneuver detection in the cataloging.

     

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