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.

翻译：围绕地球运行的物体数量持续增长，预计将伴随物体再入地球大气层频率的同步增加。其中许多再入过程将是无控的，导致其预测极具挑战性且面临诸多不确定性。传统上，再入预测基于对物体动力学特性的传播，采用最先进的建模技术描述作用于物体上的各种力。然而，建模误差——特别是与大气阻力预测相关的误差——可能导致预测精度低下。在此背景下，我们探索实现研究范式转变的可能性，即从基于物理的方法转向数据驱动方法。为此，我们提出一种用于低地球轨道（LEO）无控物体再入预测的深度学习模型。该模型基于改进的序列到序列（Sequence-to-Sequence）架构，并利用从400余个天体的两行轨道根数（TLE）数据中提取的平均高度廓线进行训练。本研究的创新之处在于，在深度学习模型中引入三个新输入特征：类阻力系数（B*）、平均太阳指数以及物体面质比，与平均高度作为联合输入。该模型基于机构间空间碎片协调委员会（IADC）研究活动中考察的天体数据集进行测试。结果表明，对于与训练集具有相同类阻力系数和偏心率分布特征的物体，模型可获得最优性能。