ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity
Abstract For global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carri...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-07-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2022SW003114 |
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| _version_ | 1841536497214291968 |
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| author | Xiaomin Luo Junfeng Du João Francisco Galera Monico Chao Xiong Jingbin Liu Xinmei Liang |
| author_facet | Xiaomin Luo Junfeng Du João Francisco Galera Monico Chao Xiong Jingbin Liu Xinmei Liang |
| author_sort | Xiaomin Luo |
| collection | DOAJ |
| description | Abstract For global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio (C/N0) based SIGMA‐ε model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ε model. |
| format | Article |
| id | doaj-art-7ee159f94d3e404b85b6ed2aefe9fdac |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-7ee159f94d3e404b85b6ed2aefe9fdac2025-01-14T16:26:58ZengWileySpace Weather1542-73902022-07-01207n/an/a10.1029/2022SW003114ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm ActivityXiaomin Luo0Junfeng Du1João Francisco Galera Monico2Chao Xiong3Jingbin Liu4Xinmei Liang5School of Geography and Information Engineering China University of Geosciences (Wuhan) Wuhan ChinaSchool of Geography and Information Engineering China University of Geosciences (Wuhan) Wuhan ChinaFaculty of Science and Technology Sao Paulo State University Sao Paulo BrazilDepartment of Space Physics Electronic Information School Wuhan University Wuhan ChinaState Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan ChinaSchool of Geography and Information Engineering China University of Geosciences (Wuhan) Wuhan ChinaAbstract For global navigation satellite system (GNSS), ionospheric disturbances caused by the geomagnetic storm can reduce the accuracy and reliability of precision point positioning (PPP). At present, common stochastic models in GNSS PPP, such as the elevation angle stochastic (EAS) model or carrier‐to‐noise power‐density ratio (C/N0) based SIGMA‐ε model, do not properly consider storm effects on GNSS measurements. To mitigate severe storm effects on GNSS PPP, this study further implements the rate of total electron content index (ROTI) parameter into the EAS model referred to as the EAS‐ROTI model. This model contains two operations. The first one is to adjust variance of GNSS measurements using ROTI observations on EAS model. The second one is to determine the ratio of the priori variance factor between pseudorange and carrier phase measurements during severe storm conditions. The performance of EAS‐ROTI model is verified by using a large number of international GNSS service stations datasets on 17 March and 23 June in 2015. Experimental results indicate that on a global scale, the EAS‐ROTI model improves the PPP accuracy in 3D direction by approximately 12.9%–14.7% compared with the EAS model, and by about 24.8%–45.9% compared with the SIGMA‐ε model.https://doi.org/10.1029/2022SW003114 |
| spellingShingle | Xiaomin Luo Junfeng Du João Francisco Galera Monico Chao Xiong Jingbin Liu Xinmei Liang ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity Space Weather |
| title | ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity |
| title_full | ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity |
| title_fullStr | ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity |
| title_full_unstemmed | ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity |
| title_short | ROTI‐Based Stochastic Model to Improve GNSS Precise Point Positioning Under Severe Geomagnetic Storm Activity |
| title_sort | roti based stochastic model to improve gnss precise point positioning under severe geomagnetic storm activity |
| url | https://doi.org/10.1029/2022SW003114 |
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