EAGER-DynamicData: Reducing Orbital Position Uncertainty with Ensembles of Upper Atmospheric Models

美国在近地轨道上拥有数百亿美元的资产，其中包括国际空间站。此外，超过1.5万件碎片正在绕地球运行，所有这些物体都是潜在的抛射物，可以轻易摧毁宝贵的资产。此外，如果这些碎片中的任何一个发生碰撞，将会产生更多的碎片，可能导致一连串的碰撞，称为凯斯勒综合征。因此，美国目前的目标是尽可能多地跟踪近地轨道上的物体，当预测到碰撞时，移动运行卫星以避免碰撞。问题是需要燃料来移动运行中的卫星，而这些卫星无法补充燃料。因此，必须尽可能准确地预测轨道上所有物体的轨迹。实际上，这些轨迹是不确定的，就像飓风未来的路径是不确定的一样。为了更准确地预测所有这些物体的轨迹，重要的是要考虑到大气密度，以及大气密度如何随着能量输入而变化，比如当北极光增强时。本研究将探索如何使用模型来预测进入高层大气的能量输入，以便更好地预测轨道物体的轨迹。本文的主要技术目标是：(1)利用概率分布函数确定热层和电离层的驱动因素；(2)利用新技术消除不同高层大气模式的偏差；(3)利用这些预测驱动一系列高层大气模式；(4)利用模型预测和当前卫星位置目录来确定近地轨道上物体的轨道；(5)培养研究生进行科学调查和使用高层大气模型的能力。预测将使用历史数据的统计分析。偏差消除将使用回顾性成本自适应输入和状态估计来完成，这允许通过将模型输出与数据源进行比较并调整模型来解释模型中缺失的物理。各种基于统计和物理的高层大气模型将由驱动程序集合驱动，以创建模型输出的集合，从而允许在轨道轨道预测中考虑热层密度的不确定性。研究生将运行模型集合并跟踪由空军目录提供的各种卫星。学生将撰写论文和参加会议，以提供最新的研究。

The United States has many tens of billions of dollars of assets in low-Earth orbit, including the International Space Station. In addition, more than 15,000 pieces of debris are orbiting the Earth, and all of these objects are potential projectiles that could easily destroy valuable assets. Further, if any of this debris collides, more debris will result, possibly causing a cascade of collisions, termed the Kessler Syndrome. The present goal of the United States, therefore, is to track as many objects in low-Earth orbit as possible and, when a collision is predicted, move the operational satellites to avoid collision. The problem is fuel is needed to move operational satellites, and those satellites cannot be refueled. Therefore, it is essential to have as accurate as possible predictions of the tracks of all of the objects in orbit. In reality, these tracks are uncertain, just as the future path of a hurricane is uncertain. In order to more accurately predict the tracks of all of these objects, it is important to take into account the atmospheric density and account for how that density changes as a function of energy inputs, such as when the northern lights intensify. This research will explore how to use models to predict energy inputs into the upper atmosphere in order to better predict the tracks of orbiting objects.The main technical goals of this proposal are to: (1) use probability distribution functions to determine the drivers of the thermosphere and ionosphere; (2) use a new technique to remove bias in different models of the upper atmosphere; (3) drive an ensemble of models of the upper atmosphere using those predictions; (4) use model predictions along with a catalog of current satellite locations to determine the orbital tracks of objects in low-Earth orbit; and (5) train a graduate student to conduct scientific investigations and use models of the upper atmosphere. The predictions will be made using a statistical analysis of historical data. The bias removal will be done using Retrospective Cost Adaptive Input and State Estimation, which allows missing physics within models to be accounted for by comparing the model output to data sources and adjusting the model. Various statistical and physics-based models of the upper atmosphere will be driven with ensembles of drivers to create ensembles of model output, allowing the uncertainty in the thermospheric density to be accounted for in orbital-track prediction. The graduate student will run the ensemble of models and track various satellites provided by the Air Force catalog. The student will write papers and attend conferences in order to provide updates on the research.