Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104077
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorGao, Ren_US
dc.creatorLiu, Zen_US
dc.creatorOdolinski, Ren_US
dc.creatorZhang, Ben_US
dc.date.accessioned2024-01-29T02:24:11Z-
dc.date.available2024-01-29T02:24:11Z-
dc.identifier.issn1080-5370en_US
dc.identifier.urihttp://hdl.handle.net/10397/104077-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© The Author(s) 2024en_US
dc.rightsOpen Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.en_US
dc.rightsThe following publication Gao, R., Liu, Z., Odolinski, R., Zhang, B. (2024). Improving GNSS PPP-RTK through global forecast system zenith wet delay augmentation. GPS Solutions 28(2), 66 is available at https://doi.org/10.1007/s10291-023-01608-0.en_US
dc.subjectGPSen_US
dc.subjectPPP-RTKen_US
dc.subjectTropospheric delayen_US
dc.subjectNumerical weather prediction (NWP)en_US
dc.subjectPositioning performanceen_US
dc.titleImproving GNSS PPP‑RTK through global forecast system zenith wet delay augmentationen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume28en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1007/s10291-023-01608-0en_US
dcterms.abstractThe precise point positioning real-time kinematic (PPP-RTK) is a high-precision global navigation satellite system (GNSS) positioning technique that combines the advantages of wide-area coverage in precise point positioning (PPP) and of rapid convergence in real-time kinematic (RTK). However, the PPP-RTK convergence is still limited by the precision of slant ionospheric delays and tropospheric zenith wet delay (ZWD), which affects the PPP-RTK network parameters estimation and user positioning performance. The present study aims to construct a PPP-RTK model augmented with a priori ZWD values derived from the global forecast system (GFS) product (a global numerical weather prediction (NWP) model) to improve the PPP-RTK performance. This study gives a priori ZWD values and conversion based on the GFS products, and the full-rank GFS-augmented undifferenced and uncombined (UDUC) PPP-RTK network model is derived. To verify the performance of GFS-augmented UDUC PPP-RTK, a comprehensive evaluation using 10-day GNSS observation data from three different GNSS station networks in the United States (US), Australia, and Europe is conducted. The results show that with the GFS ZWD a priori information, PPP-RTK performance significantly improves at the initial filtering stage, but this advantage gradually decays over time. Based on 10-day positioning results for all user stations, the GFS ZWD-augmented PPP-RTK approach reduces the average convergence time by 46% from 10.0 to 5.4 min, the three-dimensional root-mean-square (3D-RMS) error by 5.7% from 3.5 to 3.3 cm, and the time to first fix (TTFF) value by 35.8% from 6.7 to 4.3 min, all when compared to the traditional PPP-RTK without GFS ZWD constraints.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationGPS solutions, Apr. 2024, v. 28, no. 2, 66en_US
dcterms.isPartOfGPS solutionsen_US
dcterms.issued2024-04-
dc.identifier.eissn1521-1886en_US
dc.identifier.artn66en_US
dc.description.validate202401 bckwen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_TA-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of Chinaen_US
dc.description.pubStatusPublisheden_US
dc.description.TASpringer Nature (2024)en_US
dc.description.oaCategoryTAen_US
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