Space Situational Awareness: Conjunction-based Collision Analysis Among Debris and Active Assets in Space

Abstract

In recent years, the escalating concern of space debris and its threats to space assets has driven the need for advanced analysis. This study’s primary objective is to analyze satellite data for collision prediction. Specialized algorithms have been developed to forecast orbital trajectories, assess proximity between objects, conduct conjunction analyses, visualize paths, and compare the conjunction analysis results with theoretical expectations. Extensive satellite data and the PYTHON coding platform were used, to predict orbital paths, distances between objects, and potential collisions within 24 hours. Key PYTHON libraries, including MATPLOTlib, SKYFIELD, PYTZ, Numpy, and Pandas, were utilized. The proposed algorithm accurately predicted orbital paths and distances, with a focus on x, y, and z coordinates. Notably, the algorithms can predict potential collisions within 24 hours. An extended version accommodates thousands of satellite input Two- Line Element (TLE) data, enhancing collision prediction. Rigorous validation compared the built-in to manual calculations based on the orbital elements. The study provides a visualization of predicted satellite collisions, emphasizing the importance of addressing space debris challenges for safer space missions.