A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets
The satellite clocks carried on the BeiDou navigation System (BDS) are a self-manufactured hydrogen clock and improved rubidium clock, and their on-orbit performance and stabilities are not as efficient as GPS and Galileo satellite clocks caused of the orbital diversity of the BDS and the complexity...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | International Journal of Aerospace Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/4066275 |
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| _version_ | 1832561361814552576 |
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| author | Tailai Wen Gang Ou Xiaomei Tang Pengyu Zhang Pengcheng Wang |
| author_facet | Tailai Wen Gang Ou Xiaomei Tang Pengyu Zhang Pengcheng Wang |
| author_sort | Tailai Wen |
| collection | DOAJ |
| description | The satellite clocks carried on the BeiDou navigation System (BDS) are a self-manufactured hydrogen clock and improved rubidium clock, and their on-orbit performance and stabilities are not as efficient as GPS and Galileo satellite clocks caused of the orbital diversity of the BDS and the complexity of the space operating environment. Therefore, the existing BDS clock product cannot guarantee the high accuracy demand for precise point positioning in real-time scenes while the communication link is interrupted. To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. The ultrarapid predicted clock products provided by IGS (IGU-P) and four widely used prediction methods (the linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Autoregressive Integrated Moving Average (ARIMA) model) are selected to compare with the QPLSTM. The results show that the prediction residual is lower than clock products of IGU-P during 6-hour forecasting and the QPLSM shows a greater performance than the mentioned four models. The average prediction accuracy has improved by approximately 79.6, 69.2, 80.4, and 77.1% and 68.3, 52.7, 66.5, and 69.8% during a 30 min and 1-hour forecasting. Thus, the QPLSTM can be considered as a new approach to acquire high-precision satellite clock offset prediction. |
| format | Article |
| id | doaj-art-6bf90d05ba844fc1985f027dc7486ffa |
| institution | Kabale University |
| issn | 1687-5966 1687-5974 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Aerospace Engineering |
| spelling | doaj-art-6bf90d05ba844fc1985f027dc7486ffa2025-02-03T01:25:12ZengWileyInternational Journal of Aerospace Engineering1687-59661687-59742021-01-01202110.1155/2021/40662754066275A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock OffsetsTailai Wen0Gang Ou1Xiaomei Tang2Pengyu Zhang3Pengcheng Wang4College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaCollege of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, ChinaThe satellite clocks carried on the BeiDou navigation System (BDS) are a self-manufactured hydrogen clock and improved rubidium clock, and their on-orbit performance and stabilities are not as efficient as GPS and Galileo satellite clocks caused of the orbital diversity of the BDS and the complexity of the space operating environment. Therefore, the existing BDS clock product cannot guarantee the high accuracy demand for precise point positioning in real-time scenes while the communication link is interrupted. To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. The ultrarapid predicted clock products provided by IGS (IGU-P) and four widely used prediction methods (the linear polynomial, quadratic polynomial, gray system (GM (1,1)), and Autoregressive Integrated Moving Average (ARIMA) model) are selected to compare with the QPLSTM. The results show that the prediction residual is lower than clock products of IGU-P during 6-hour forecasting and the QPLSM shows a greater performance than the mentioned four models. The average prediction accuracy has improved by approximately 79.6, 69.2, 80.4, and 77.1% and 68.3, 52.7, 66.5, and 69.8% during a 30 min and 1-hour forecasting. Thus, the QPLSTM can be considered as a new approach to acquire high-precision satellite clock offset prediction.http://dx.doi.org/10.1155/2021/4066275 |
| spellingShingle | Tailai Wen Gang Ou Xiaomei Tang Pengyu Zhang Pengcheng Wang A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets International Journal of Aerospace Engineering |
| title | A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets |
| title_full | A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets |
| title_fullStr | A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets |
| title_full_unstemmed | A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets |
| title_short | A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets |
| title_sort | novel long short term memory predicted algorithm for bds short term satellite clock offsets |
| url | http://dx.doi.org/10.1155/2021/4066275 |
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