DynaBR: Dynamics-Integrated Base–Residual Learning Framework for Non-Cooperative Satellite Orbit Forecasting

Growing congestion in low Earth orbit (LEO) makes accurate orbit forecasting essential for spacecraft safety. For non-cooperative targets, forecasters rely solely on external observations. Uncertain target properties and environments limit physics-based perturbation modeling, while data-driven models fit complex patterns but struggle to incorporate known dynamics. We first construct a dataset covering 2,000 LEO targets, including satellites, rocket bodies, and space debris, by propagating discrete two-line element (TLE) records into regularly sampled orbital state sequences. We then propose DynaBR, a unified forecasting model. Global Dynamics Reference provides the dominant orbital structure; Local Dynamics Alignment calibrates future references using historical phase discrepancies; and Dynamics-Conditioned Residual Forecasting predicts corrections from historical residuals and aligned future references to compensate for perturbation effects that approximate dynamics cannot explain. Experiments show that DynaBR achieves the strongest overall forecasting performance on unseen targets, achieves the strongest overall performance across forecasting horizons while substantially outperforming both physics-based and data-driven methods at longer horizons and across all target types. Over a 48-hour horizon, DynaBR achieves an average relative reduction of 99.45% in mean position error across 12 SOTA baselines.