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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Additional title:

    Advs in Intelligent Syst., Computing


    Contributors:

    Conference:

    Doctoral Symposium on Intelligence Enabled Research ; 2023 ; Cooch Behar, India December 20, 2023 - December 21, 2023



    Publication date :

    2024-05-16


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English






    MODEL PREDICTIVE APPROACH FOR SHORT-TERM COLLISION AVOIDANCE

    Dikarew, A. | British Library Conference Proceedings | 2021


    Ship Collision Avoidance Autonomous Avoidance System using Deep Learning

    CHO SEOK JE / KIM HYUNG JIN / KIM SOO HO et al. | European Patent Office | 2021

    Free access