Enhanced Space Debris detection and monitoring using a hybrid Bi-LSTM- CNN and Bayesian Optimization

Abstract

Monitoring space debris is vital for ensuring the safety of space missions and satellite operations amid the increasing number of satellites and spacecraft in orbit. The study addresses this challenge by proposing a novel approach based on a hybrid Bi-LSTM-CNN architecture optimized using Bayesian Optimization. Through extensive analysis utilizing machine learning and deep learning techniques, the study develops a robust space debris detection system capable of classifying both the object type and Radar Cross Section (RCS) size. The proposed method outperforms existing approaches by demonstrating superior performance across multiple evaluation metrics, including accuracy, precision, recall, and F1 score. Moreover, the study considers the practical aspect of training time, ensuring efficiency in real-time applications. Empirical validation on real-world datasets confirms the effectiveness and efficiency of the hybrid model in accurately detecting and predicting space debris types. Overall, this research significantly advances space debris monitoring capabilities, mitigating risks associated with space exploration and satellite operations, and offers comprehensive insights into potential hazards and optimizing mitigation strategies.