Uncontrolled spacecraft will disintegrate and generate a large amount of debris in the reentry process, and ablative debris may cause potential risks to the safety of human life and property on the ground. Therefore, predicting the landing points of spacecraft debris and forecasting the degree of risk of debris to human life and property is very important. In view that it is difficult to predict the process of reentry process and the reentry point in advance, and the debris generated from reentry disintegration may cause ground damage for the uncontrolled space vehicle on expiration of service. In this paper, we adopt the object-oriented approach to consider the spacecraft and its disintegrated components as consisting of simple basic geometric models, and introduce three machine learning models: the support vector regression (SVR), decision tree regression (DTR) and multilayer perceptron (MLP) to predict the velocity, longitude and latitude of spacecraft debris landing points for the first time. Then, we compare the prediction accuracy of the three models. Furthermore, we define the reentry risk and the degree of danger, and we calculate the risk level for each spacecraft debris and make warnings accordingly. The experimental results show that the proposed method can obtain high accuracy prediction results in at least 15 seconds and make safety level warning more real-time.

翻译：不受控航天器在再入过程中会解体并产生大量碎片，烧蚀碎片可能对地面人员生命和财产安全造成潜在风险。因此，预测航天器碎片的落点以及评估碎片对生命财产的风险程度至关重要。鉴于提前预测再入过程及再入点存在困难，且服务期满的不受控空间飞行器再入解体产生的碎片可能造成地面损害，本文采用面向对象方法，将航天器及其解体组件视为由简单基本几何模型构成，首次引入支持向量回归（SVR）、决策树回归（DTR）和多层感知器（MLP）三种机器学习模型，预测航天器碎片落点的速度、经度和纬度。随后，我们比较了三种模型的预测精度。此外，我们定义了再入风险和危险程度，计算了每个航天器碎片的风险等级并据此发出预警。实验结果表明，所提方法能在至少15秒内获得高精度预测结果，并使安全等级预警更具实时性。