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

TL;DR

This paper applies machine learning models to predict spacecraft debris reentry points and assess associated risks, enabling real-time safety warnings for ground safety management.

Contribution

Introduces the use of SVR, DTR, and MLP models for predicting debris landing points and risk levels, which is novel in reentry safety assessment.

Findings

MLP achieved the highest prediction accuracy.

Predictions can be made within 15 seconds.

The method enables real-time risk warnings.

Abstract

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.