spacetech-ssa:IBM Space Tech-太空态势感知

上传者: 42122881 | 上传时间: 2022-07-03 07:58:24 | 文件大小: 5.74MB | 文件类型: ZIP
低地球轨道的空间态势感知 (LEO)是一个拥挤的地方,进入LEO的人为空间物体(ASO)的数量正在Swift增加。 随着人口的激增,物体之间的近距离接触(连接)也不可避免地增加。 空间态势感知(SSA)问题的核心是预测ASO的位置以及去向。 从油漆规格到国际空间站,应有尽有。 当前用于轨道预测的最新方法是基于物理学的模型,这些模型需要对物体的轨迹,其运行环境以及物体进行操纵的意图的非常准确的了解。 实际上,我们无权访问此数据。 轨迹很少通过嘈杂的地面雷达系统进行测量,我们对太空天气和大气密度的理解是有限的,并且卫星运营商并不热衷于分享他们的机动计划。 该项目旨在成为使用ML改善SSA的实验实验室和游乐场,并提供了一条端到端的管道以: 来自USSTRATCOM Space Track API的LEO中有关ASO的ETL轨道数据。 根据物理模型进行轨道预测。 训练并使用机器学习模型来

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