Deep Learning-Based Space Debris Tracking and Mitigation

Abstract

The increasing threat of space debris, whether deliberately generated or inadvertently produced, necessitates vigilant monitoring and forecasting to safeguard both crewed and uncrewed space missions. This study evaluates eight prevalent models for monitoring and predicting space debris: TLE-based SGP4, ORDEM, MASTER, Debrisat, SDebrisNet, SDTS, CARA, and SSN. A comprehensive strategy is used for each model, considering its diverse attributes, precision, complexity, data requirements, adaptability, dependability, and usability. This evaluation outlines the advantages and disadvantages of each technique in addressing the primary challenges of data, computing, and system building. The study moreover examines the advancement of tracking gadgets and current methods, together with potential enhancements to address real-time issues. The comparative evaluation of the models in this research will strategically enhance existing methods for space debris control equipment, hence promoting safety and sustainable operational practices in outer space. This research aims to develop techniques that align with the expanding and dynamic efforts of space exploration by monitoring debris with maximum efficiency and accuracy.