Digital Twin-Driven TLE Error Correction for Precise LEO Satellite Orbit Prediction

Low earth orbit (LEO) satellite orbit prediction is one of the key measures to compensate for position errors and ensure position accuracy, which plays an important role in the aerospace communication network for undertaking functions such as routing relay, real-time communication, and signal forwarding. However, existing learning-based satellite orbit prediction models that are recognized as the best measurement inevitably face the problem of distribution bias. Orbit predictions can lead to a decrease in model performance due to different types of satellites (LEO and SSO) and different time scales. In this article, a new method is explored to overcome these shortcomings. Unlike previous methods that explore the temporal correlation of orbit data, this novel orbit prediction method converts satellite orbit data into the frequency domain via Fourier transformation, using a third-order Fourier-derivative convolution framework. Specifically, the proposed Fourier dilation convolution (FDC) model demonstrates better generalization ability across different types of satellites and different time scales by combining frequency domain analysis and dilated convolution. Two real datasets are applied for experimental validation, and the results show the effectiveness of our proposed FDC model. Meanwhile, the proposed FDC model shows a decrease in mean absolute error (MAE) values compared to the temporal convolutional network based seasonal and trend decomposition using a Loess (STL-TCN) model. Quantitative comparisons demonstrate that compared to the STL-TCN model, the FDC model reduces the mean absolute error (MAE) by approximately 10% to 85% across different orbital dimensions. Finally, we conducted further analysis of the interpretability of the model.