Carrier phase-based ionospheric observables using PPP models

Xiang, Yan; Gao, Yang; Shi, Junbo; Xu, Chaoqian

doi:10.1016/j.geog.2017.01.006

Cited by 32 publications

(6 citation statements)

References 8 publications

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“…For GNSS ionospheric data, the carrier‐to‐code leveling (CCL) method (Ciraolo et al., 2007; Tang, 2023; Xiang et al., 2017) was used to extract ionospheric information along a specific line‐of‐sight (LoS) for dual‐frequency GNSS observations collected by ground‐based multi‐GNSS receivers and LEO‐based data, such as the SWARM constellation (Friis‐Christensen et al., 2006), Gravity Recovery and Climate Experiment (GRACE) constellation (Tapley et al., 2004), Meteorological Operational (MetOp) constellation (Clerbaux et al., 2009), Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) constellation (Fong et al., 2009), TerraSAR‐X satellite (Buckreuss et al., 2009), and KomopSat‐5 satellite (Lee, 2010). However, there are system errors between ground‐based GNSS ionospheric data and LEO‐based ionospheric data due to the different observational ranges.…”

Section: Ionospheric Information Extraction Methodsmentioning

confidence: 99%

Global Ionosphere Modeling Based on GNSS, Satellite Altimetry, Radio Occultation, and DORIS Data Considering Ionospheric Variation

Chen,

Ren,

Yang

et al. 2023

JGR Space Physics

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The accuracy of ionospheric models estimated by ground‐based multiple global navigation satellite system ionospheric data over regions with sparse tracking stations is not ideal. To improve the accuracy of the estimated ionospheric model, different types of ionospheric data with different combinations were employed for previous studies. However, the ionospheric observational ranges for different types of ionospheric data are not the same. In this study, the accuracy of ionospheric maps generated by ground‐based ionospheric data (ground‐based strategy) and ground‐based ionospheric data combined with data provided by other geodetic measurements normalized by the single‐layer normalization method (multi‐source strategy) were studied. The results showed that the main differences between the ionospheric models estimated by the two strategies occur for data taken over the ocean, which mainly range from −1 to 0 total electron content unit (TECU). When assessed using Jason‐3 vertical total electron content data, the mean root mean square (RMS) value of the ionospheric model estimated by the multi‐source strategy was 5.03 TECU, which is approximately 15% smaller than that estimated by the ground‐based strategy. The maximum reduction in results using the multisource strategy was approximately 25% over different latitudes compared with that of the ground‐based strategy. Furthermore, the self‐consistency evaluation method was employed for evaluation. The results showed that the RMS of the ionospheric model estimated by the multi‐source strategy was 2.41 TECU, which is 3.60% better than that of the ground‐based strategy. The maximum reduction was 15% on different days.

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Section: Ionospheric Information Extraction Methodsmentioning

confidence: 99%

Global Ionosphere Modeling Based on GNSS, Satellite Altimetry, Radio Occultation, and DORIS Data Considering Ionospheric Variation

Chen,

Ren,

Yang

et al. 2023

JGR Space Physics

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“…To achieve a near real-time ionosphere estimation the levelling method is not quite suitable Xiang et al (2017), instead of that a Hatch-filter smoothing was applied. The smoothed geometry-free code Rs r,I (n) can be calculated as:…”

Section: Ionospheric Tec Measurements From Gnssmentioning

confidence: 99%

Gauss process regression for real-time ionospheric delay estimation from GNSS observations

Lupsic

Takács

2022

Acta Geod Geophys

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The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety of receivers regarding their accuracy and reliability. Low cost, multi-frequency units have been released on the market latterly; however, the number of single-frequency receivers is still significant. Since their measurements are influenced by ionospheric delay, accurate ionosphere models are of utmost importance to reduce the effect. This paper summarizes how Gauss process regression (GPR) can be applied to derive near real-time regional ionosphere models using raw Global Navigation Satellite System (GNSS) observations of permanent stations. While Gauss process is widely used in machine learning, GPR is a nonparametric, Bayesian approach to regression. GPR has several benefits for ionosphere monitoring since it is quite robust and efficient to derive a grid model from data available in irregular set of ionospheric pierce points. The corresponding instrumental delays are estimated by a parallel Kalman filter. The presented algorithm can be applied near real-time, however the results are offline calculated and are compared to two high quality TEC map products. Based on the analysis, the accuracy of the GPR modell is in 2 TECu range. The developed methods could be efficiently applied in the field of autonomous vehicle navigation with meeting both accuracy and integrity requirements.

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“…Montenbruck [27] applied the GPS ionospheric-free SF-PPP in precise orbit determination of low Earth orbiting satellites and demonstrated that accuracy at the 1 m level could be obtained. Third, the ionospheric delays can be parameterized and estimated along with other unknowns [16,32,33], namely ionospheric-unconstrained SF-PPP, which is based on undifferenced and uncombined carrier phase and pseudorange observations. Similar to ionospheric-free SF-PPP, ionospheric-unconstrained SF-PPP is also a rank-deficient model.…”

Section: Introductionmentioning

confidence: 99%

Single-frequency precise point positioning enhanced with multi-GNSS observations and global ionosphere maps

Ning

Han

Zhang³

2018

Meas. Sci. Technol.

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Due to its low-cost, sub-meter-precision, and large number of possible users, precise point positioning with single-frequency receivers (SF-PPP) has attracted a great deal of interest in recent years. One crucial issue in SF-PPP is the handling of ionospheric delays, which cannot be eliminated by combining observations on different frequencies. For validation and evaluation purposes, four SF-PPP models, which are ionospheric-corrected, -free, -unconstrained, and -constrained, were realized with different ionosphere treatments in SF-PPP. To enhance real-time SF-PPP performance, the contribution of multi-Global Navigation Satellite System (GNSS) observations and (predicted) global ionosphere maps (GIMs) was investigated in this study. To demonstrate the performance of the four SF-PPP models, two sets of multi-GNSS observations, which came from 33 Multi-GNSS Experiment (MGEX) ground static and one airborne kinematic observation data, were utilized to perform SF-PPP in simulated kinematic and real kinematic mode, respectively. The results indicated that the ionospheric-corrected and -constrained SF-PPP models performed better than the ionospheric-free and -unconstrained SF-PPP models in positioning convergence, while the ionospheric-unconstrained SF-PPP model showed advantages over the other three SF-PPP models in positioning accuracy after convergence. The positioning accuracy derived from Global Positioning System (GPS) + GLONASS + BeiDou Navigation Satellite System + Galileo ionospheric-constrained (IC)SF-PPP solutions is 9.1, 7.2, and 17.0 cm in the east, north, and the up components, respectively, which is somewhat worse than that of the ionospheric-unconstrained (IU)SF-PPP solutions (7.3, 6.1, and 15.2 cm) due to the low accuracy of GIMs. Compared to the IUSF-PPP solutions, the positioning accuracy during the convergence period (e.g. 1 h) derived from ICSF-PPP solutions is improved by 85.3% from 116.3 to 17.1 cm in the east, by 71.6% from 67.6 to 19.2 cm in the north, and by 72.6% from 194.7 to 53.4 cm in the up, respectively. Overall, the ionospheric-constrained SF-PPP demonstrated good performance in both positioning convergence and accuracy. Enhanced by employing multi-GNSS observations and external ionospheric products (i.e. GIMs), the ionospheric-constrained SF-PPP can achieve the positioning accuracy of about 0.7–1.0 dm in the horizontal component and 0.15–0.25 dm in the vertical component after a certain convergence time, i.e. 1 h in this study.

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Carrier phase-based ionospheric observables using PPP models

Cited by 32 publications

References 8 publications

Global Ionosphere Modeling Based on GNSS, Satellite Altimetry, Radio Occultation, and DORIS Data Considering Ionospheric Variation

Global Ionosphere Modeling Based on GNSS, Satellite Altimetry, Radio Occultation, and DORIS Data Considering Ionospheric Variation

Gauss process regression for real-time ionospheric delay estimation from GNSS observations

Single-frequency precise point positioning enhanced with multi-GNSS observations and global ionosphere maps

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