Robust Satellite-Orbit Prediction Using Artificial Neural Network Based on Levenberg-Marquardt Algorithm
2022 International Wireless Communications and Mobile Computing, IWCMC 2022
High-accuracy satellite-orbit prediction is perceived to be very important for future sixth-generation (6G)) communication networks. It is crucial to acquire the precise satellites' instantaneous location information in order to facilitate the future satellite-aided communication networks. Because of the nonlinear characteristics of satellite orbits, we propose a new advanced artificial neural network (ANN) which is built upon the Levenberg-Marquardt algorithm for robust satellite-orbit prediction. Since the Levenberg-Marquardt algorithm (LMA) involves a second-order derivative of the cost function, our proposed novel LMA-based ANN approach can achieve a better performance compared to the conventional first-order derivative methods, including stochastic gradient and conjugate gradient methods. A standard satellite-orbit dataset, namely Two-Line Element (TLE) Catalog, is employed to validate our proposed new LMA-based ANN approach. Numerical results are presented to demonstrate the effectiveness of our proposed novel LMA-based ANN approach for satellite-orbit prediction.
6G (sixth generation), Artificial neural network (ANN), Levenberg-Marquardt algorithm (LMA), orbit prediction, satellite
Applied Data Science
Shih Yu Chang, Hsiao Chun Wu, Fotios Sotiropoulos, and Usman S. Goni. "Robust Satellite-Orbit Prediction Using Artificial Neural Network Based on Levenberg-Marquardt Algorithm" 2022 International Wireless Communications and Mobile Computing, IWCMC 2022 (2022): 1329-1334. https://doi.org/10.1109/IWCMC55113.2022.9824105