基于LSTM的卫星轨道预测技术研究  

Research on satellite orbit prediction technology based on LSTM

作　　者：吴潇鹤 张琨[2] 袁帅[1] WU Xiaohe;ZHANG Kun;YUAN Shuai(School of Cyberspace Security,Nanjing University of Science and Technology,Nanjing 210094,China;School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China)

机构地区：[1]南京理工大学网络空间安全学院,南京210094 [2]南京理工大学计算机科学与工程学院,南京210094

出　　处：《网络空间安全科学学报》2024年第4期18-28,共11页Journal of Cybersecurity

基　　金：江苏省研究生科研与实践创新计划项目(KYCX24_0724)。

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 Short-Term 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。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 or-bits 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.

关 键 词：低轨卫星 轨道预测 长短期记忆网络 双线性元 时间序列预测 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]