A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning
Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time appli...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/23/5921 |
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| author | Huan Zhang Yibin Yao Mingxian Hu Chaoqian Xu Xiaoning Su Defu Che Wenjie Peng |
| author_facet | Huan Zhang Yibin Yao Mingxian Hu Chaoqian Xu Xiaoning Su Defu Che Wenjie Peng |
| author_sort | Huan Zhang |
| collection | DOAJ |
| description | Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision tropospheric delay augmentation information is vital to speed up the convergence of PPP. In this research, we estimate the zenith tropospheric delay (ZTD) from 2018 to 2019 by static precise point positioning (PPP) using the fixed position mode; GNSS observations were obtained from the National Geomatics Center of China (NGCC). Firstly, ZTD outliers were detected, and data gaps were interpolated using the K-nearest neighbor algorithm (KNN). Secondly, The ZTD differences between the KNN and periodic model were employed as input datasets to train the long short-term memory (LSTM) neural network. Finally, LSTM forecasted ZTD differences and the ZTD periodic signals were combined to recover the final forecasted ZTD results. In addition, the forecasted ZTD results were applied in static PPP as a prior constraint to reduce PPP convergence time. Numerical results show that the average root-mean-square error (RMSE) of predicting ZTD is about 1 cm. The convergence time of the PPP which was corrected by the LSTM-ZTD predictions is reduced by 13.9, 22.6, and 30.7% in the summer, autumn, and winter, respectively, over GPT2-ZTD corrected PPP and unconstrained conventional PPP for different seasons. |
| first_indexed | 2024-03-09T17:34:00Z |
| format | Article |
| id | doaj.art-97dc0eb7f1a4428ca7bc0e81831152ad |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T17:34:00Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-97dc0eb7f1a4428ca7bc0e81831152ad2023-11-24T12:02:38ZengMDPI AGRemote Sensing2072-42922022-11-011423592110.3390/rs14235921A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point PositioningHuan Zhang0Yibin Yao1Mingxian Hu2Chaoqian Xu3Xiaoning Su4Defu Che5Wenjie Peng6School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaSchool of Resources and Civil Engineering, Northeastern University, Shenyang 110819, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaGlobal navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision tropospheric delay augmentation information is vital to speed up the convergence of PPP. In this research, we estimate the zenith tropospheric delay (ZTD) from 2018 to 2019 by static precise point positioning (PPP) using the fixed position mode; GNSS observations were obtained from the National Geomatics Center of China (NGCC). Firstly, ZTD outliers were detected, and data gaps were interpolated using the K-nearest neighbor algorithm (KNN). Secondly, The ZTD differences between the KNN and periodic model were employed as input datasets to train the long short-term memory (LSTM) neural network. Finally, LSTM forecasted ZTD differences and the ZTD periodic signals were combined to recover the final forecasted ZTD results. In addition, the forecasted ZTD results were applied in static PPP as a prior constraint to reduce PPP convergence time. Numerical results show that the average root-mean-square error (RMSE) of predicting ZTD is about 1 cm. The convergence time of the PPP which was corrected by the LSTM-ZTD predictions is reduced by 13.9, 22.6, and 30.7% in the summer, autumn, and winter, respectively, over GPT2-ZTD corrected PPP and unconstrained conventional PPP for different seasons.https://www.mdpi.com/2072-4292/14/23/5921precise point positionglobal navigation satellite systemzenith tropospheric delayk-nearest neighbor algorithmlong short-term memory neural network |
| spellingShingle | Huan Zhang Yibin Yao Mingxian Hu Chaoqian Xu Xiaoning Su Defu Che Wenjie Peng A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning Remote Sensing precise point position global navigation satellite system zenith tropospheric delay k-nearest neighbor algorithm long short-term memory neural network |
| title | A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning |
| title_full | A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning |
| title_fullStr | A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning |
| title_full_unstemmed | A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning |
| title_short | A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning |
| title_sort | tropospheric zenith delay forecasting model based on a long short term memory neural network and its impact on precise point positioning |
| topic | precise point position global navigation satellite system zenith tropospheric delay k-nearest neighbor algorithm long short-term memory neural network |
| url | https://www.mdpi.com/2072-4292/14/23/5921 |
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