Ruibo Wei1,2, Yao Kong3, Mengzhao Li1,2, Feng Liu1,2,*, Fang Cheng4,*
CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1507-1528, 2025, DOI:10.32604/cmc.2025.063722
- 09 June 2025
Abstract To address uncertainties in satellite orbit error prediction, this study proposes a novel ensemble learning-based orbit prediction method specifically designed for the BeiDou navigation satellite system (BDS). Building on ephemeris data and perturbation corrections, two new models are proposed: attention-enhanced BPNN (AEBP) and Transformer-ResNet-BiLSTM (TR-BiLSTM). These models effectively capture both local and global dependencies in satellite orbit data. To further enhance prediction accuracy and stability, the outputs of these two models were integrated using the gradient boosting decision tree (GBDT) ensemble learning method, which was optimized through a grid search. The main contribution of this More >
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