Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias
The prediction of Satellites’ Clock Bias (SCB) plays an important role in optimizing the clock bias parameters in navigation messages, meeting the requirements of real-time dynamic precise point positioning and providing the prior information required for satellite autonomous navigation....
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2021
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oai:doaj.org-article:eba0dd34d9074242b021ed354fc37c5c2021-11-19T00:05:16ZNonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias2169-353610.1109/ACCESS.2021.3053265https://doaj.org/article/eba0dd34d9074242b021ed354fc37c5c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9334968/https://doaj.org/toc/2169-3536The prediction of Satellites’ Clock Bias (SCB) plays an important role in optimizing the clock bias parameters in navigation messages, meeting the requirements of real-time dynamic precise point positioning and providing the prior information required for satellite autonomous navigation. Satellite-borne atomic clocks are often affected by many factors in space, which makes it difficult to describe the clocks’ bias and behavior with fixed model to achieve reliable high-precision prediction. The composition and characteristics of clock bias for satellite-borne atomic clock are described and analyzed, a clock bias prediction algorithm based on Nonlinear autoregressive model with exogenous input (NARX) recurrent neural network is proposed, the advantages of this model in SCB and other time series prediction are introduced in detail. The SCB data from four different clock types are selected for calculation and analysis. The comparative results show that, for both 6h and 24h forecasts, the accuracy and stability of NARX model are significantly better than three commonly used models, especially in the prediction of satellite cesium atomic clock.Yifeng LiangJiangning XuFangneng LiPengfei JiangIEEEarticleSatellite clock bias (SCB)predictionNARXrecurrent neural network (RNN)precise point positioningElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 24416-24424 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
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EN |
| topic |
Satellite clock bias (SCB) prediction NARX recurrent neural network (RNN) precise point positioning Electrical engineering. Electronics. Nuclear engineering TK1-9971 |
| spellingShingle |
Satellite clock bias (SCB) prediction NARX recurrent neural network (RNN) precise point positioning Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yifeng Liang Jiangning Xu Fangneng Li Pengfei Jiang Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias |
| description |
The prediction of Satellites’ Clock Bias (SCB) plays an important role in optimizing the clock bias parameters in navigation messages, meeting the requirements of real-time dynamic precise point positioning and providing the prior information required for satellite autonomous navigation. Satellite-borne atomic clocks are often affected by many factors in space, which makes it difficult to describe the clocks’ bias and behavior with fixed model to achieve reliable high-precision prediction. The composition and characteristics of clock bias for satellite-borne atomic clock are described and analyzed, a clock bias prediction algorithm based on Nonlinear autoregressive model with exogenous input (NARX) recurrent neural network is proposed, the advantages of this model in SCB and other time series prediction are introduced in detail. The SCB data from four different clock types are selected for calculation and analysis. The comparative results show that, for both 6h and 24h forecasts, the accuracy and stability of NARX model are significantly better than three commonly used models, especially in the prediction of satellite cesium atomic clock. |
| format |
article |
| author |
Yifeng Liang Jiangning Xu Fangneng Li Pengfei Jiang |
| author_facet |
Yifeng Liang Jiangning Xu Fangneng Li Pengfei Jiang |
| author_sort |
Yifeng Liang |
| title |
Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias |
| title_short |
Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias |
| title_full |
Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias |
| title_fullStr |
Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias |
| title_full_unstemmed |
Nonlinear Autoregressive Model With Exogenous Input Recurrent Neural Network to Predict Satellites’ Clock Bias |
| title_sort |
nonlinear autoregressive model with exogenous input recurrent neural network to predict satellites’ clock bias |
| publisher |
IEEE |
| publishDate |
2021 |
| url |
https://doaj.org/article/eba0dd34d9074242b021ed354fc37c5c |
| work_keys_str_mv |
AT yifengliang nonlinearautoregressivemodelwithexogenousinputrecurrentneuralnetworktopredictsatellitesx2019clockbias AT jiangningxu nonlinearautoregressivemodelwithexogenousinputrecurrentneuralnetworktopredictsatellitesx2019clockbias AT fangnengli nonlinearautoregressivemodelwithexogenousinputrecurrentneuralnetworktopredictsatellitesx2019clockbias AT pengfeijiang nonlinearautoregressivemodelwithexogenousinputrecurrentneuralnetworktopredictsatellitesx2019clockbias |
| _version_ |
1718420703146934272 |