Precise point positioning with an adaptive geometry-free cycle slip detection threshold under complex ionospheric conditions

Abstract Global navigation satellite system (GNSS) precise point positioning (PPP) performance experiences significant degradation under complex ionospheric conditions, particularly during geomagnetic storm events. To address this challenge, this study develops an adaptive geometry-free (GF) cycle slip detection threshold model, leveraging GNSS Doppler measurements to reduce the incorrect cycle slip detection, thereby enhancing the accuracy and reliability of GNSS PPP. The proposed threshold model is fitted using Doppler index data extracted from 110 International GNSS Service (IGS) stations on the 11th May 2024 when an extreme geomagnetic storm occurred. 72 IGS stations which are not used in modeling on the same day are employed to validate its performance. The results show that PPP with adaptive GF cycle slip detection threshold can achieve positioning accuracy improvements by 42.4% and 37.2% in horizontal and vertical directions, respectively, compared with traditional strategy. Furthermore, positioning accuracy improvements of 63.1% and 50.1% in horizontal and vertical are obtained with data from another geomagnetic storm event on the 11th October 2024, which further demonstrates the efficiency and uniformity of proposed model. Comparison of positioning accuracy with tight and loose thresholds during the ionospheric quiescence periods also verifies the adaptability of the built model.

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Publication Details

Journal
GPS Solutions
Published
2025-10-23
DOI
https://doi.org/10.1007/s10291-025-01979-6
Citations
1
Primary Topic
Ionosphere and magnetosphere dynamics
Type
article
Field-Weighted Citation Impact
1.15
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article

Precise point positioning with an adaptive geometry-free cycle slip detection threshold under complex ionospheric conditions

Jiahuan Hu, Fan Xu, Luheng Zhou, Long Tang et al.
1 citations
GPS Solutions
Ionosphere and magnetosphere dynamics
1.15
article

Precise point positioning with an adaptive geometry-free cycle slip detection threshold under complex ionospheric conditions

Jiahuan Hu, Fan Xu, Luheng Zhou, Long Tang, Xiaohong Zhang
article en
1 citations

Abstract

Abstract Global navigation satellite system (GNSS) precise point positioning (PPP) performance experiences significant degradation under complex ionospheric conditions, particularly during geomagnetic storm events. To address this challenge, this study develops an adaptive geometry-free (GF) cycle slip detection threshold model, leveraging GNSS Doppler measurements to reduce the incorrect cycle slip detection, thereby enhancing the accuracy and reliability of GNSS PPP. The proposed threshold model is fitted using Doppler index data extracted from 110 International GNSS Service (IGS) stations on the 11th May 2024 when an extreme geomagnetic storm occurred. 72 IGS stations which are not used in modeling on the same day are employed to validate its performance. The results show that PPP with adaptive GF cycle slip detection threshold can achieve positioning accuracy improvements by 42.4% and 37.2% in horizontal and vertical directions, respectively, compared with traditional strategy. Furthermore, positioning accuracy improvements of 63.1% and 50.1% in horizontal and vertical are obtained with data from another geomagnetic storm event on the 11th October 2024, which further demonstrates the efficiency and uniformity of proposed model. Comparison of positioning accuracy with tight and loose thresholds during the ionospheric quiescence periods also verifies the adaptability of the built model.

GPS SolutionsVol. 30(1)
Guangdong University of Technology (CN), Hong Kong Polytechnic University (HK), Wuhan University (CN), Chinese Academy of Surveying and Mapping (CN)
Openalex Percentile: Top 15%
Ionosphere and magnetosphere dynamics
1.15
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Precise point positioning with an adaptive geometry-free cycle slip detection threshold under complex ionospheric conditions — Jiahuan Hu, Fan Xu, et al. · GPS Solutions (2025) | TGRS Research Map | TGRS