An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting
The ionosphere is vital for satellite navigation and radio communication, but observational limitations necessitate ionospheric forecasting. The least squares collocation (LSC) method is commonly used for global navigation satellite system (GNSS)-based global ionospheric forecasting, though its accu...
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| Veröffentlicht in: | IEEE geoscience and remote sensing letters Jg. 22; S. 1 - 5 |
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| Sprache: | Englisch |
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2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| Abstract | The ionosphere is vital for satellite navigation and radio communication, but observational limitations necessitate ionospheric forecasting. The least squares collocation (LSC) method is commonly used for global navigation satellite system (GNSS)-based global ionospheric forecasting, though its accuracy and stability need improvement. This study introduces two optimized models based on the ConvLSTM cell with an encoder-decoder structure to enhance forecasting performance. Using seven years of historical data, the model provides stable forecasts for the following year. Tests from 2015 to 2020 show that optimization reduces root mean square error (RMSE) by 10.159%-16.363% compared to the unoptimized method. The encoder-decoder ConvLSTM-B model achieves the best performance, lowering RMSE by 2.031%-8.547% compared to the ConvLSTM-A model. These results highlight the effectiveness of the proposed approach in improving ionospheric forecast accuracy. |
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| AbstractList | The ionosphere is vital for satellite navigation and radio communication, but observational limitations necessitate ionospheric forecasting. The least squares collocation (LSC) method is commonly used for global navigation satellite system (GNSS)-based global ionospheric forecasting, though its accuracy and stability need improvement. This study introduces two optimized models based on the ConvLSTM cell with an encoder-decoder structure to enhance forecasting performance. Using seven years of historical data, the model provides stable forecasts for the following year. Tests from 2015 to 2020 show that optimization reduces root mean square error (RMSE) by 10.159%–16.363% compared to the unoptimized method. The encoder-decoder ConvLSTM-B model achieves the best performance, lowering RMSE by 2.031%–8.547% compared to the ConvLSTM-A model. These results highlight the effectiveness of the proposed approach in improving ionospheric forecast accuracy. |
| Author | Wang, Cheng Xue, Kaiyu Shi, Chuang |
| Author_xml | – sequence: 1 givenname: Cheng orcidid: 0000-0002-2603-1177 surname: Wang fullname: Wang, Cheng email: acheng@buaa.edu.cn organization: School of Space and Earth Sciences, Beihang University, Beijing, China – sequence: 2 givenname: Kaiyu surname: Xue fullname: Xue, Kaiyu email: xuekaiyu@buaa.edu.cn organization: School of Electronic Information Engineering, Beihang University, Beijing, China – sequence: 3 givenname: Chuang orcidid: 0000-0002-1600-9160 surname: Shi fullname: Shi, Chuang email: shichuang@buaa.edu.cn organization: School of Space and Earth Sciences, Beihang University, Beijing, China |
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| SubjectTerms | Accuracy Collocation methods ConvLSTM model Data models Decoding encoder-decoder structure Encoders-Decoders Forecast accuracy Forecasting Global navigation satellite system Ionosphere Ionospheric forecasting Long short term memory Mathematical models Navigation Navigation satellites Navigation systems Navigational satellites Optimization optimized models Predictive models Radio communications Root-mean-square errors Satellite navigation Satellite observation Satellites Training |
| Title | An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting |
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