【摘要】 轨道预测对于评估空间物体之间发生碰撞的可能性以及对近地空间环境进行更好管理具有重要意义。传统的轨道预测方法基于物理的动态模型，需要对复杂的空间环境和空间物体建模，现实中由于对很多非引力扰动因素的认识是有限的，因此限制了轨道预测精度。针对传统轨道预测的局限性，提出一种基于长短期记忆网络（Long ShortTerm Memory,LSTM）的方法对轨道进行预测，通过提取卫星轨道数据在时间上的特征，发现其运行规律。实验结果表明，该方法提高了卫星轨道预测的精度，为获得更好的空间态势感知能力提供理论基础。更多还原

【Abstract】 orbit prediction is crucial for assessing the likelihood of collisions between space objects and for better managing the near-earth space environment. Traditional orbit prediction methods rely on physical dynamic models, which necessitate the modeling of complex space environments and space objects. In reality, the limited understanding of many non-gravitational perturbations restricts the accuracy of orbit predictions. Considering the limitations of traditional orbit prediction methods, we proposed a technique to predict orbits based on the long short-term memory(LSTM) network. This approach leveraged a series of convolutions to extract features from the satellite orbit data over time, uncovering the underlying operational patterns. Experimental results indicated that this method improved the accuracy of satellite orbit predictions and provided a theoretical foundation for improving space situational awareness capabilities.更多还原