From Historical Errors to Future Trajectories: Learning Transferable Knowledge for Satellite Orbit Prediction

Satellite orbit prediction is fundamental to modern space operations, with an increasing demand for accurate trajectory forecasts driven by the rapid expansion of satellite constellations. Recent studies employ deep learning to model the residuals between predicted and true trajectories, providing a promising solution for orbit forecasting. However, such residual learning paradigms simply treat prediction errors as supervision, overlooking the inherent periodicity of orbital motion and thereby limiting their predictive performance. To this end, we introduce OrbitEcho, a novel residual correction framework that encodes historical prediction experience into transferable knowledge for accurate trajectory forecasting. Specifically, OrbitEcho first stores forecasting errors from preceding orbital cycles in a memory bank. It then retrieves phase-aligned trajectory errors at similar orbital states as informative references for residual correction. For each retrieved candidate, OrbitEcho further exploits orbital periodicity to model its residual evolution over one orbital cycle, which enables historical errors to be adapted to the current prediction. Together, OrbitEcho distills historical predictions into two complementary forms of transferable knowledge, i.e., trajectory errors and residual dynamics, which are jointly leveraged to refine orbit forecasts. Extensive experiments on real-world satellites demonstrate the superior performance of OrbitEcho, reducing the average MAE from 8.40 km for a state-of-the-art learning-based baseline to 0.31 km, yielding an order-of-magnitude improvement. OrbitEcho also exhibits strong generalization to unseen satellites and robust performance under severe geomagnetic storms.