Multi-Modal Deep Learning for Spacecraft Orbit Prediction: Incorporating Solar Wind Perturbations via Cross-Attention Fusion
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2026
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| _version_ | 1866901533158801408 |
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| author | Rubin, Ted |
| author_facet | Rubin, Ted |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19434499 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Multi-Modal Deep Learning for Spacecraft Orbit Prediction: Incorporating Solar Wind Perturbations via Cross-Attention Fusion Rubin, Ted omputer and Information Sciences → Machine Learning Computer and Information Sciences → Artificial Intelligence Physical Sciences → Space Sciences Physical Sciences → Astronomy and Astrophysics <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> |
| title | Multi-Modal Deep Learning for Spacecraft Orbit Prediction: Incorporating Solar Wind Perturbations via Cross-Attention Fusion |
| topic | omputer and Information Sciences → Machine Learning Computer and Information Sciences → Artificial Intelligence Physical Sciences → Space Sciences Physical Sciences → Astronomy and Astrophysics |
| url | https://doi.org/10.5281/zenodo.19434499 |