Since the late 1950s, when the first artificial satellite was launched, the number of Resident Space Objects has steadily increased. It is estimated that around one million objects larger than one cm are currently orbiting the Earth, with only thirty thousand larger than ten cm being tracked. To avert a chain reaction of collisions, known as Kessler Syndrome, it is essential to accurately track and predict debris and satellites’ orbits. Current approximate physics-based methods have errors in the order of kilometers for seven-day predictions, which is insufficient when considering space debris, typically with less than one meter. This failure is usually due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, and unknown characteristics such as the mass or geometry of the space object. Operators can enhance Orbit Prediction accuracy by deriving unmeasured objects’ characteristics and improving non-conservative forces’ effects by leveraging data-driven techniques, such as Machine Learning. In this survey, we provide an overview of the work in applying Machine Learning for Orbit Determination, Orbit Prediction, and atmospheric density modeling.

中文翻译：

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轨道估计中的机器学习：一项调查

自 20 世纪 50 年代末第一颗人造卫星发射以来，驻留空间物体的数量稳步增加。据估计，目前大约有一百万个大于一厘米的物体绕地球运行，只有三万个大于十厘米的物体正在被跟踪。为了避免碰撞的连锁反应（称为凯斯勒综合症），必须准确跟踪和预测碎片和卫星的轨道。目前基于物理的近似方法对于 7 天的预测存在公里量级的误差，这在考虑空间碎片（通常小于一米）时是不够的。这种失败通常是由于空间物体在轨迹开始时状态的不确定性、大气阻力等环境条件的预测误差以及空间物体的质量或几何形状等未知特征造成的。操作员可以通过导出未测量物体的特征来提高轨道预测的准确性，并通过利用机器学习等数据驱动技术来改善非保守力的影响。在本次调查中，我们概述了应用机器学习进行轨道确定、轨道预测和大气密度建模的工作。