Deep Learning Approach to Satellite Collision Avoidance Using Long Short-Term Memory

Satellites are essential to our contemporary way of life in a variety of fields, including communication, navigation, crisis management, and science. With an increasing number of satellites, spacecraft, and other objects launched into space, collisions occur, and the amount of debris in orbit increases, satellite collisions are a key problem in space operations, and there are various challenges and issues, as a result, accurate and efficient orbit forecasting is becoming more important for increased space situational awareness. Deep learning (DL) has the potential to improve orbit prediction and collision avoidance in space by improving accuracy, automating responses, and providing real-time decision assistance. The proposed approach has the potential to significantly improve the safety and sustainability of satellite operations in increasingly crowded orbital environments. This paper introduces a satellite orbit forecasting model based on historical position and velocity data that uses long short-term memory (LSTM) and benchmark data to avoid satellite collisions by anticipating satellite position and velocity. According to the results, the model of LSTM can increase the precision of orbit prediction, with position accuracy ranging in R-square (R2) from 95 to 99% and for mean absolute error (MAE) from 0.05 to 0.02.