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：无人控制的航天器在重返过程中会分解并产生大量碎片,在重返过程中,混合碎片可能会对地面上人的生命和财产的安全造成潜在风险,因此,预测航天器碎片的着陆点和预测碎片对人类生命和财产的危险程度非常重要;鉴于很难预测重返过程的过程和提前再入点,再入点产生的碎片可能会在服务期满时对不受控制的空间飞行器造成地面损害;在本文件中,我们采用以物体为导向的办法,将航天器及其分解部件视为由简单的基本几何模型组成,并采用三种机器学习模型:支持矢量回归(SVR)、决定树回归(DTR)和多层倍感应(MLP),以便首次预测航天器碎片着陆点的速度、纬度和纬度。然后,我们比较这三种模型的预测准确性。此外,我们界定再入风险和危险程度,我们计算每个航天器碎片的风险程度,并据此提出警告。实验结果显示,在15秒钟内,拟议的预警方法能够取得更高的准确性结果。