Combining Semiparametric and Machine Learning Approaches for Short-term Prediction of Satellite Clock Bias

Abstract Accurate modeling of satellite clock bias (SCB) is critical for enhancing high-precision positioning capabilities. Existing approaches, including semiparametric adjustment models and neural networks, address the nonlinearity and non-stationarity of SCB time series, as well as potential distortions from trend and noise component overlap. However, its practicality is limited when selecting the kernel functions in semiparametric models and the initial parameters in neural networks. This paper proposes a novel integrated model, the Semi-LFA-Informer (SLFAI) model, which combines semiparametric techniques with optimized self-attention neural networks to solve the above limitations. We utilize the proposed model to conduct the SCB prediction for BDS-3, and the results are compared with those of the quadratic polynomial (QP), spectral analysis (SA), and long short-term memory (LSTM) models in terms of prediction stability and accuracy. The experimental results show that the proposed method can not only effectively solve the problem of the generalization ability, but also significantly enhance the computational efficiency and accuracy. The SLFAI model achieves average prediction accuracies exceeding 0.15 ns, 0.25 ns, and 0.35 ns for 3-hour, 6-hour, and 12-hour forecasts, respectively, representing a new approach to acquiring high-quality SCB.