Satellite Orbit Prediction Based on Recurrent Neural Network using Two Line Elements

Because of the hazards and challenges of the space environment, Satellites are usually exposed to orbit deviation, collisions with debris, or loss of tracking control. Therefore, orbit prediction can be defined as the critical and significant role for satellite monitoring and tracking control. This paper proposes a novel orbit prediction approach based on Two-Line Elements (TLE) using A Recurrent Neural Network (RNN) architecture with Long Short-Term Memory (LSTM). The proposed approach has been verified and evaluated its efficiency using the popular benchmark Clark tracks that describe the orbital satellites datasets. In the experimental study, the results show that the proposed approach can predict satellite orbits with high accuracy, which is presented by the two variables, position and velocity. The evaluation measured are R
²
represents the goodness of fitness for the prediction accuracy is 98%, and the mean square error in position is
$9.7^{\ast}10^{-5}$
and in velocity is
$10^{\ast}10^{-3}$
.