MrSOP: A Multi-Regime Satellite Orbit Prediction Model Based on Tensor Decomposition

Accurate satellite orbit prediction is critical for collision avoidance and sustainable space operations. Existing learning-based methods, however, are typically trained on few satellites or a single orbital regime. We construct a Two-Line Element (TLE) dataset of 18,004 satellites across LEO, MEO, NSO, and GEO, and propose MrSOP: a single multi-regime forecaster with physics-anchored routing and a Decomposed Mixture of Experts (DMoE). It recovers semi-major axis and eccentricity from the input, and needs no separate learned gating network or orbital-regime label at inference. MrSOP attains the lowest error in all eight in-domain regime–metric comparisons, reducing MAE by up to 60.2% over the second-best model. It achieves the lowest error among learned models in all six zero-shot source–metric comparisons. A 16-expert DMoE cuts parameters by 94.2% relative to the full-rank model, with a gain in mean in-domain accuracy.