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

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Recent Trends in Intelligence Enabled Research (DoSIER 2023)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1457))

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Abstract

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.

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Authors and Affiliations

  1. Faculty of Science, Helwan University, Cairo, Egypt

    Alaa Osama, Mourad Raafat & Ashraf Darwish

  2. Computer Science School, Canadian International College (CIC), Cairo, Egypt

    Sara Abdelghafar

  3. College of Business Administration (CBA), Kuwait University, Kuwait City, Kuwait

    Aboul Ella Hassanien

  4. Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  5. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Alaa Osama, Mourad Raafat, Sara Abdelghafar, Ashraf Darwish & Aboul Ella Hassanien

Authors
  1. Alaa Osama
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  2. Mourad Raafat
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  3. Sara Abdelghafar
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  4. Ashraf Darwish
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  5. Aboul Ella Hassanien
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Corresponding author

Correspondence to Alaa Osama .

Editor information

Editors and Affiliations

  1. Rajnagar Mahavidyalaya, Birbhum, India

    Siddhartha Bhattacharyya

  2. Cooch Behar Government Engineering College, Cooch Behar, West Bengal, India

    Gautam Das

  3. Cooch Behar Government Engineering College, Cooch Behar, West Bengal, India

    Sourav De

  4. Algebra University College, Zagreb, Croatia

    Leo Mrsic

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Osama, A., Raafat, M., Abdelghafar, S., Darwish, A., Hassanien, A.E. (2024). Deep Learning Approach to Satellite Collision Avoidance Using Long Short-Term Memory. In: Bhattacharyya, S., Das, G., De, S., Mrsic, L. (eds) Recent Trends in Intelligence Enabled Research. DoSIER 2023. Advances in Intelligent Systems and Computing, vol 1457. Springer, Singapore. https://doi.org/10.1007/978-981-97-2321-8_9

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