LTE: Lightweight Transformer Encoder for Orbit Prediction

As the focus of space exploration has recently shifted from national efforts to private enterprises, interest in the space industry has increased. With the rising number of satellite launches, the risk of collisions between satellites and between satellites and space debris has grown, which can lead not only to property damage but also casualties caused by the debris. To address this issue, various machine learning and deep learning-based methods have been researched to improve the accuracy of satellite orbit prediction and mitigate these risks. However, most studies have applied basic machine learning models to orbit prediction without considering the model size and execution time, even though satellite operations require lightweight models that offer both a strong prediction performance and rapid execution. In this study, we propose a time series forecasting framework, the Lightweight Transformer Encoder (LTE), for satellite orbit prediction. The LTE is a prediction model that modifies the encoder structure of the Transformer model to enhance the accuracy of satellite orbit prediction and reduce the computational resources used. To evaluate its performance, we conducted experiments using about 4.8 million data points collected every minute from January 2016 to December 2018 by the KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites, which are part of the Korea Multi-Purpose Satellite (KOMPSAT) series operated by the Korea Aerospace Research Institute (KARI). We compare the performance of our model against various baseline models in terms of prediction error, execution time, and the number of parameters used. Our LTE model demonstrates significant improvements: it reduces the orbit prediction error by 50.61% in the KOMPSAT-3 dataset, 42.40% in the KOMPSAT-3A dataset, and 30.00% in the KOMPSAT-5 dataset compared to the next-best-performing model. Additionally, in the KOMPSAT-3 dataset, it decreases the execution time by 36.86% (from 1731 to 1093 s) and lowers the number of parameters by 2.33% compared to the next-best-performing model.