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| Main Author: | |
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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.19434499 |
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Table of Contents:
- <p>We present a multi-modal deep learning framework for spacecraft orbit prediction that fuses historical trajectory data with real-time solar wind measurements via cross-attention. Using three years (2023–2025) of NASA SSC position data for ISS, DSCOVR, and MMS-1 combined with OMNI solar wind parameters, we train and compare bidirectional LSTM, Transformer, and residual gated fusion architectures for 6-hour trajectory prediction. Our LSTM achieves 125 km MAE on ISS at the 6-hour horizon, while the multi-modal architecture improves to 135 km during geomagnetic storms — a 17% improvement that validates solar wind as a meaningful leading indicator for LEO drag perturbations. We introduce a two-phase training strategy and sigmoid gating mechanism that guarantees the multi-modal model cannot underperform its single-modality baseline. All data, code, and trained model checkpoints are publicly available.</p>