An Autonomous Orbit Prediction Approach for BDS MEO Satellites Using a Short-Sequence Adaptive Model

The new-generation global navigation satellite system (GNSS) demands enhanced satellite autonomy, where high-precision orbit prediction plays a pivotal role. Traditional dynamic models depend heavily on long-term on-orbit observations, making hybrid deep-learning-based orbit prediction models an efficient alternative. Although existing studies have validated that temporal networks can effectively capture orbit error variations, improving prediction accuracy under short input sequences remains a critical challenge. To address this issue, this paper proposes an improved short-sequence-adaptive Bidirectional Long Short-Term Memory (BiLSTM) network to enhance orbit prediction performance of BeiDou Medium Earth Orbit satellites. Specifically, we design a scale-aware hybrid convolution module and an attention-driven feature fusion module to generate feature representations with high information density, which outperform the standalone BiLSTM under short input sequences. Experiments on the BeiDou system (BDS) C19 satellite demonstrate that our method reduces the mean residual rates from 54.03%, 41.18%, 80.10% to 4.36%, 6.12%, 5.39% in the X, Y, and Z axes, respectively, surpassing BiLSTM alone by over 85% across all metrics. Notably, the proposed method exhibits robust generalization capabilities across similar satellites with similar orbital configurations and dynamic environments.