Scholarlink

The rapid expansion of space exploration has led to an increase in space debris formation, especially in Low Earth Orbits [LEO]. These space debris poses a significant threat to both ongoing and future space missions. This paper investigates the risk of satellite collisions with space debris and it presents a machine learning model that predicts the collision probability with the help of the orbital data gathered from Space-track.org . The idea is to initially calculate the distance of closest approach between a satellite and nearby debris by splitting the orbit into an interval of points and calculate the distance between the satellite, then we find the minimum value from all those distances which gives us the approximate value of minimum distance. Then, we trained the model with a labeled data which is a collection of minimum distances and their probability of collision through linear regression and random forest algorithms, and the results were promising. With the help of this model, space agencies can determine collision-free satellite orbits and mitigate the threats posed by space debris.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predictive Modeling of Satellite Collision Risk Using Machine Learning Models

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • M. Ayyadurai,
  • M. Darshan,
  • K. Deepak,
  • S. Aravinthaa,
  • D. Pratheba,
  • D. A. Daniel Leve Manickam

摘要

The rapid expansion of space exploration has led to an increase in space debris formation, especially in Low Earth Orbits [LEO]. These space debris poses a significant threat to both ongoing and future space missions. This paper investigates the risk of satellite collisions with space debris and it presents a machine learning model that predicts the collision probability with the help of the orbital data gathered from Space-track.org . The idea is to initially calculate the distance of closest approach between a satellite and nearby debris by splitting the orbit into an interval of points and calculate the distance between the satellite, then we find the minimum value from all those distances which gives us the approximate value of minimum distance. Then, we trained the model with a labeled data which is a collection of minimum distances and their probability of collision through linear regression and random forest algorithms, and the results were promising. With the help of this model, space agencies can determine collision-free satellite orbits and mitigate the threats posed by space debris.