Collision probability of space objects

Abstract

The primary objective of this research project was to conduct a comprehensive analysis of state- of-the-art algorithms designed for the calculation of Collision Probability between satellites and other space objects, particularly space debris. Our investigation revealed a categorization of space object encounters into three main types: instantaneous, short-term, and long-term encounters. Notably, we identified that collision probability assessment for each encounter type could be further subcategorized as cumulative and instantaneous. To evaluate the accuracy of collision probability calculations, we focused on short-term encounters and implemented two widely utilized methods: Patera’s method and Alfano’s method. These methods were rigorously tested against a reference collision probability derived through the Monte Carlo Approach. Our findings shed light on the effectiveness and precision of these methodologies in predicting collision probabilities for short-term space object encounters. In addition, two collision scenarios, ”Sure Shot” and ”Miss Case,” were simulated to further analyze the algorithms in real-world scenarios. These simulations aimed to assess the accuracy and efficiency of the algorithms in both guaranteed collision situations and instances where collisions were avoided. Incorporating these scenarios provided deeper insights into the performance of the algorithms under varying conditions. In conclusion, this research contributes valuable insights into the categorization of space object encounters and the efficacy of specific algorithms in predicting collision probabilities. The comparative analysis of Patera’s and Alfano’s methods, along with the simulation of collision scenarios, offers a nuanced understanding of their performance against a benchmark Monte Carlo Approach, thereby providing implications for the enhancement of collision probability calculations in space debris management.