A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in Space
Accurate prediction of the on-orbit positions of Low Earth Orbit (LEO) satellites is essential for mission success, operational efficiency, and safety. Nevertheless, the non-stationary nature of orbital data and sensor noise presents significant challenges for accurate prediction. To address these c...
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MDPI AG
2025-02-01
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| Series: | Aerospace |
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| author | Yang Guo Boyang Wang Zhengxu Zhao |
| author_facet | Yang Guo Boyang Wang Zhengxu Zhao |
| author_sort | Yang Guo |
| collection | DOAJ |
| description | Accurate prediction of the on-orbit positions of Low Earth Orbit (LEO) satellites is essential for mission success, operational efficiency, and safety. Nevertheless, the non-stationary nature of orbital data and sensor noise presents significant challenges for accurate prediction. To address these challenges, we propose a novel forecasting model, EMD-ICA-DLinear, which combines trend-residual representation with EMD-ICA in an innovative manner. By integrating the TSR (Trend, Seasonality, and Residual) framework with the EMD-ICA dual perspective, this approach provides a comprehensive understanding of time series data and outperforms traditional models in capturing subtle nonlinear relationships. When predicting the orbital position of the Fengyun-3C satellite, the model uses MSE and MAE as evaluation metrics. Experimental results indicate that the proposed EMD-ICA-DLinear hybrid model achieves MSE and MAE values of 0.1101 and 0.1567, respectively, when predicting the orbital position of the Fengyun-3C satellite 6 h in advance, representing reductions of 37.87% and 19.85% compared to the best baseline model, TimesNet. This advancement enhances satellite orbit prediction accuracy, supports operational stability, and enables timely adjustments, thereby improving mission efficiency and safety. |
| format | Article |
| id | doaj-art-c2b1a91c78cb4efcb61aac5d45d4a8ff |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Aerospace |
| spelling | doaj-art-c2b1a91c78cb4efcb61aac5d45d4a8ff2025-08-20T02:11:00ZengMDPI AGAerospace2226-43102025-02-0112320410.3390/aerospace12030204A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in SpaceYang Guo0Boyang Wang1Zhengxu Zhao2Shandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, ChinaShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, ChinaShandong Key Laboratory of Space Debris Monitoring and Low-Orbit Satellite Networking, Qingdao University of Technology, Qingdao 266520, ChinaAccurate prediction of the on-orbit positions of Low Earth Orbit (LEO) satellites is essential for mission success, operational efficiency, and safety. Nevertheless, the non-stationary nature of orbital data and sensor noise presents significant challenges for accurate prediction. To address these challenges, we propose a novel forecasting model, EMD-ICA-DLinear, which combines trend-residual representation with EMD-ICA in an innovative manner. By integrating the TSR (Trend, Seasonality, and Residual) framework with the EMD-ICA dual perspective, this approach provides a comprehensive understanding of time series data and outperforms traditional models in capturing subtle nonlinear relationships. When predicting the orbital position of the Fengyun-3C satellite, the model uses MSE and MAE as evaluation metrics. Experimental results indicate that the proposed EMD-ICA-DLinear hybrid model achieves MSE and MAE values of 0.1101 and 0.1567, respectively, when predicting the orbital position of the Fengyun-3C satellite 6 h in advance, representing reductions of 37.87% and 19.85% compared to the best baseline model, TimesNet. This advancement enhances satellite orbit prediction accuracy, supports operational stability, and enables timely adjustments, thereby improving mission efficiency and safety.https://www.mdpi.com/2226-4310/12/3/204LEO satellitesorbital predictiondeep learningempirical mode decompositionindependent component analysisDLinear |
| spellingShingle | Yang Guo Boyang Wang Zhengxu Zhao A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in Space Aerospace LEO satellites orbital prediction deep learning empirical mode decomposition independent component analysis DLinear |
| title | A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in Space |
| title_full | A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in Space |
| title_fullStr | A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in Space |
| title_full_unstemmed | A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in Space |
| title_short | A Hybrid EMD-ICA-DLinear Multi-View Representation Model for Accurate Satellite Orbit Prediction in Space |
| title_sort | hybrid emd ica dlinear multi view representation model for accurate satellite orbit prediction in space |
| topic | LEO satellites orbital prediction deep learning empirical mode decomposition independent component analysis DLinear |
| url | https://www.mdpi.com/2226-4310/12/3/204 |
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