A regional augmented PPP algorithm for offshore considering NWP
In offshore areas with inhomogeneous distribution of reference stations, the low accuracy of regional augmented Zenith Tropospheric Delay (ZTD) products directly impacts regional augmented Precise Point Positioning (PPP) convergence time. Considering the availability of Numerical Weather Prediction...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-01-01
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| Series: | Geo-spatial Information Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10095020.2024.2441515 |
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| author | Ying Xu Xiangdan Meng Jianhui Cui Lin Ma |
| author_facet | Ying Xu Xiangdan Meng Jianhui Cui Lin Ma |
| author_sort | Ying Xu |
| collection | DOAJ |
| description | In offshore areas with inhomogeneous distribution of reference stations, the low accuracy of regional augmented Zenith Tropospheric Delay (ZTD) products directly impacts regional augmented Precise Point Positioning (PPP) convergence time. Considering the availability of Numerical Weather Prediction (NWP) data at sea, we propose a regional augmented PPP algorithm that integrates tropospheric delays derived from NWP virtual grid points and the Continuously Operating Reference Station (CORS) network observations. Land and marine experiments are carried out to verify the effectiveness of this algorithm, evaluating ZTD interpolation accuracy and PPP positioning performance. The land experimental results show that this algorithm achieves similar ZTD accuracy and PPP results compared to traditional augmented PPP with perfect reference stations. The ZTD accuracy is 8.5 mm, and the average positioning accuracy is approximately 3.75 cm. The convergence time is less than 11 min where the data sampling interval is 30 s. Marine experiments indicate that the convergence time of this algorithm is 11–32% shorter than that of ionospheric-free PPP. The convergence time of this algorithm is reduced by 56–59%, and positioning accuracy in the E, N and U directions is augmented by 33.3%, 19.5% and 53.7%, respectively, compare to traditional augmented PPP at sea where the distribution of reference stations is inhomogeneous. Meanwhile, almost all stations can achieve a faster solution convergence by this algorithm. Furthermore, the algorithm is suitable not only for areas that lack reference stations at sea but also for the lack of reference stations on land. |
| format | Article |
| id | doaj-art-cc5a8e6a10614cd6827bdbf8a55216d4 |
| institution | Kabale University |
| issn | 1009-5020 1993-5153 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geo-spatial Information Science |
| spelling | doaj-art-cc5a8e6a10614cd6827bdbf8a55216d42025-02-04T15:09:04ZengTaylor & Francis GroupGeo-spatial Information Science1009-50201993-51532025-01-0111410.1080/10095020.2024.2441515A regional augmented PPP algorithm for offshore considering NWPYing Xu0Xiangdan Meng1Jianhui Cui2Lin Ma3College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, ChinaHebei Institute of Investigation & Design of Water Conservancy & Hydropower Group Limited, Tianjin, ChinaIn offshore areas with inhomogeneous distribution of reference stations, the low accuracy of regional augmented Zenith Tropospheric Delay (ZTD) products directly impacts regional augmented Precise Point Positioning (PPP) convergence time. Considering the availability of Numerical Weather Prediction (NWP) data at sea, we propose a regional augmented PPP algorithm that integrates tropospheric delays derived from NWP virtual grid points and the Continuously Operating Reference Station (CORS) network observations. Land and marine experiments are carried out to verify the effectiveness of this algorithm, evaluating ZTD interpolation accuracy and PPP positioning performance. The land experimental results show that this algorithm achieves similar ZTD accuracy and PPP results compared to traditional augmented PPP with perfect reference stations. The ZTD accuracy is 8.5 mm, and the average positioning accuracy is approximately 3.75 cm. The convergence time is less than 11 min where the data sampling interval is 30 s. Marine experiments indicate that the convergence time of this algorithm is 11–32% shorter than that of ionospheric-free PPP. The convergence time of this algorithm is reduced by 56–59%, and positioning accuracy in the E, N and U directions is augmented by 33.3%, 19.5% and 53.7%, respectively, compare to traditional augmented PPP at sea where the distribution of reference stations is inhomogeneous. Meanwhile, almost all stations can achieve a faster solution convergence by this algorithm. Furthermore, the algorithm is suitable not only for areas that lack reference stations at sea but also for the lack of reference stations on land.https://www.tandfonline.com/doi/10.1080/10095020.2024.2441515Augmented precise point positioning (PPP)offshore positioningzenith tropospheric delay (ZTD)numerical weather prediction (NWP) |
| spellingShingle | Ying Xu Xiangdan Meng Jianhui Cui Lin Ma A regional augmented PPP algorithm for offshore considering NWP Geo-spatial Information Science Augmented precise point positioning (PPP) offshore positioning zenith tropospheric delay (ZTD) numerical weather prediction (NWP) |
| title | A regional augmented PPP algorithm for offshore considering NWP |
| title_full | A regional augmented PPP algorithm for offshore considering NWP |
| title_fullStr | A regional augmented PPP algorithm for offshore considering NWP |
| title_full_unstemmed | A regional augmented PPP algorithm for offshore considering NWP |
| title_short | A regional augmented PPP algorithm for offshore considering NWP |
| title_sort | regional augmented ppp algorithm for offshore considering nwp |
| topic | Augmented precise point positioning (PPP) offshore positioning zenith tropospheric delay (ZTD) numerical weather prediction (NWP) |
| url | https://www.tandfonline.com/doi/10.1080/10095020.2024.2441515 |
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