标题： 一种机器学习和特征工程方法用于预测空间物体的不可控再入 显示英文标题

标题： A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects

摘要： 围绕地球运行的物体数量持续增长，预计伴随着进入地球大气层的物体频率增加。 其中许多再入过程是不受控的，使得其预测具有挑战性，并且受到多种不确定性的影响。 传统上，再入预测是基于使用最先进的建模技术来传播物体的动力学。 然而，建模误差，特别是与大气阻力预测相关的误差可能导致预测精度较差。 在此背景下，我们探讨从基于物理的方法向数据驱动方法转变的可能性。 为此，我们提出了一个深度学习模型，用于低地轨道（LEO）中不受控物体的再入预测。 该模型基于序列到序列架构的修改版本，并在一组超过400个物体的两行元素（TLE）数据中得出的平均高度剖面进行训练。 这项工作的创新之处在于，在深度学习模型中，除了平均高度外，还引入了三个新的输入特征：一个类似阻力的系数（B*）、平均太阳指数和物体的面积质量比。 所开发的模型在国际机构空间碎片协调委员会（IADC）活动研究的一组物体上进行了测试。 结果表明，当物体具有与训练集相同的类似阻力系数和偏心率分布时，性能最佳。 显示英文摘要

摘要： The continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth's atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Traditionally, re-entry predictions are based on the propagation of the object's dynamics using state-of-the-art modelling techniques for the forces acting on the object. However, modelling errors, particularly related to the prediction of atmospheric drag may result in poor prediction accuracies. In this context, we explore the possibility to perform a paradigm shift, from a physics-based approach to a data-driven approach. To this aim, we present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO). The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, three new input features: a drag-like coefficient (B*), the average solar index, and the area-to-mass ratio of the object. The developed model is tested on a set of objects studied in the Inter-Agency Space Debris Coordination Committee (IADC) campaigns. The results show that the best performances are obtained on bodies characterised by the same drag-like coefficient and eccentricity distribution as the training set.