Reentry Risk and Safety Assessment of Spacecraft Debris Based on Machine Learning

Abstract

Uncontrolled spacecraft will disintegrate and generate a large amount of debris in the reentry process. 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 waste to human life and property is very important. In view that it is difficult to predict the reentry process and the reentry point in advance, the debris generated from reentry disintegration may cause ground damage for the uncontrolled space vehicle on the 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 10 s and make safety-level warning more real-time.