Onboard Hybrid Orbit Prediction with Lightweight Machine-Learning Error Correction

Autonomous spacecraft operations are increasingly important as missions grow more complex, ground contact opportunities remain limited, and the number of LEO satellites continue to rise. Reliable onboard orbit determination (OD) and orbit prediction (OP) are essential for mission planning, resource allocation, and communication scheduling. Operational OD/OP typically relies on physics-based models that estimate parameters (initial state, drag coefficient, etc.) from tracking data. However, environmental modeling is not perfect, and uncertainties in atmospheric density can cause prediction errors to grow rapidly. This limits OP reliability.

We present an onboard-oriented hybrid OD/OP concept that augments a classical physics-based OD/OP chain with a lightweight machine-learning (ML) correction module to compensate for systematic OP errors in real time. While data-driven correction of propagator errors has been explored previously, this work emphasizes the tight integration of a compact correction model into an operational workflow under onboard constraints. The implementation is based on the Python OD/OP toolbox Artificial Intelligence for Precise Orbit Determination (AI4POD) and targets deployment within the Autonomous Space Operations Planner and Scheduler (ASOPS) experiment, that is planned for validation on the ATHENE-1 satellite.

The approach is demonstrated using simulated GPS-like tracking data generated with a high-fidelity reference model, while OD/OP are performed with a reduced-complexity model representative of onboard settings. A compact artificial neural network (ANN) is trained to predict OP errors in the RSW frame from available onboard data, reducing the maximum three-day along-track error from ~5 km to ~1.2 km.

To assess operational robustness, we complement the baseline results with a statistical consistency check of the residuals across all prediction cases and outline planned tests with additional ML/DL correction models.