Three Dual-Frequency Precise Point Positioning Models for the Ionospheric Modeling and Satellite Pseudorange Observable-Specific Signal Bias Estimation

Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) enables the estimation the ionospheric vertical total electron content (VTEC) as well as the by-product of the satellite Pseudorange observable-specific signal bias (OSB). The single-frequency PPP models, with the ionosphere-float and ionosphere-free approaches in ionospheric studies, have recently been discussed by the authors. However, the multi-frequency observations can improve the performances of the ionospheric research compared with the single-frequency approaches. This paper presents three dual-frequency PPP approaches using the BeiDou Navigation Satellite System (BDS) B1I/B3I observations to investigate ionospheric activities. Datasets collected from the globally distributed stations are used to evaluate the performance of the ionospheric modeling with the ionospheric single- and multi-layer mapping functions (MFs), respectively. The characteristics of the estimated ionospheric VTEC and BDS satellite pseudorange OSB are both analyzed. The results indicated that the three dual-frequency PPP models could all be applied to the ionospheric studies, among which the dual-frequency ionosphere-float PPP model exhibits the best performance. The three dual-frequency PPP models all possess the capacity for ionospheric applications in the GNSS community.

High-resolution and high-accuracy global ionosphere maps estimated by GNSS and LEO constellations: simulative and real data experimental results

<p>Global ionospheric total electron content (TEC) map has been employed in many high-precision areas. However, its spatial and temporal resolution is not ideal since the ground-based Global Navigation Satellite Systems (GNSS) stations distributed unevenly. Fortunately, many low earth orbit (LEO) satellite constellations will provide a large number of observations that can be used for ionospheric monitoring in the future. In this contribution, we presented two methods, which are the single-layer normalization (SLN) method and the dual-layer superposition (DLS) method, for ionospheric modeling based on the simulative and real data of GNSS+LEO satellites.</p><p>For simulative data, a constellation with 192 LEO satellites is simulated. And then,  the global ionospheric maps (GIMs) are estimated by all Multi-GNSS and simulative LEO satellite observations. The results showed that the root mean square (RMS) is reduced by approximately 25% and 21% for SLN method and DLS method, respectively. For real data,  20 available scientific LEO satellites, such as Jason-2/3, COSMIC-1/-2, Swarm missions, etc.,  are employed in the ground-based GNSS ionospheric modeling. The results showed that the differences between the ionospheric model estimated by GNSS+LEO and that by GNSS data are mainly over the oceanic region, which may exceed ±20 TECU. The improvement of RMS over the oceanic region is about 15% for the ionospheric model estimated by GNSS+LEO. The RMS of the ionospheric model improved approximately 4.0% compared with that by GNSS data using the dSTEC assessment method.</p>

All Hands on Deck for Ionospheric Modeling

Challenges to studying the ionosphere’s ability to conduct electrical currents undercut scientists’ efforts to improve space weather forecasting models. Let’s tackle them together.

Efficient global ionospheric modeling based on multi-source and massive observation data

<p><strong><span>Abstract</span></strong><span> Ionosphere is an important error source of satellite navigation and a key component of space weather. With the rapid development of multiple Global Navigation Satellite System (GNSS) and other ionospheric research technologies, and the high precision and near real-time requirements for ionospheric products, it is necessary to carry out a research on multi-source data fusion, massive data processing and near-real-time solution of global ionosphere model (GIM); therefore, we modified the traditional ionospheric modeling technology and generate the GIM products (GIM/SHA). In view of the defect of ground-based GNSS data missing in the ocean regions, the method of adding virtual observation stations to the data missing regions in a large range was adopted, which not only enhanced the accuracy of the GIM in the ocean regions, but also avoided the weight determination among different data sources. In terms of near-real-time modeling, the multi-threaded parallel modeling strategy was adopted.</span> <span>Four GNSS (GPS, GLONASS, BEIDOU, Galileo) observation data, eight virtual observation stations and a server with a CPU frequency of 2.1 GHz and 16 threads were utilized. It took less than 30 minutes to construct the GIM by using parallel modeling strategy, which was 10.3 times faster than serial modeling strategy. The accuracy of the GIM/SHA was verified by using the ionospheric products of International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) in the period of day of year (DOY) 200-365, 2019. Compared with the ionospheric products of CODE, ESA/ESOC, JPL, UPC, EMR, CAS and WHU, the vertical total electron content (VTEC) root mean squares (RMSs) were 1.09 TEC units (TECu), 1.51TECu, 2.32TECu, 1.88TECu, 2.24TECu, 1.25TECu and 1.38TECu, respectively. The result shows that the GIM/SHA have comparable accuracy with IGS ionospheric products. Satellite altimetry data was exploited to verify the accuracy of GIM/SHA in ocean regions, and it can be concluded that the accuracy of the GIM in ocean regions can be significantly reinforced by adding virtual observation stations in ocean regions. Multi-system and multi-frequency differential code bias (DCB) products (DCB/SHA) were simultaneously generated. Compared with IGS DCB products, the satellite DCB RMSs of DCB/SHA were 0.16ns for GPS, 0.08ns for GLONASS, 0.17ns for BEIDOU and 0.14ns for Galileo; the GNSS receiver DCB RMSs of DCB/SHA were 0.69ns for GPS, 1.06ns for GLONASS, 0.75 for BEIDOU and 1.03ns for Galileo. It can be proved that the accuracy of DCB/SHA are comparable to IGS DCB products.</span></p><p><strong><span>Keywords</span></strong><span> Multi-GNSS; GIM; Virtual observation station; Near real-time; VTEC; DCB</span></p>

Estimation of GPS Differential Code Biases Based on Independent Reference Station and Recursive Filter

The differential code bias (DCB) of the Global Navigation Satellite Systems (GNSS) receiver should be precisely corrected when conducting ionospheric remote sensing and precise point positioning. The DCBs can usually be estimated by the ground GNSS network based on the parameterization of the global ionosphere together with the global ionospheric map (GIM). In order to reduce the spatial-temporal complexities, various algorithms based on GIM and local ionospheric modeling are conducted, but rely on station selection. In this paper, we present a recursive method to estimate the DCBs of Global Positioning System (GPS) satellites based on a recursive filter and independent reference station selection procedure. The satellite and receiver DCBs are estimated once per local day and aligned with the DCB product provided by the Center for Orbit Determination in Europe (CODE). From the statistical analysis with CODE DCB products, the results show that the accuracy of GPS satellite DCB estimates obtained by the recursive method can reach about 0.10 ns under solar quiet condition. The influence of stations with bad performances on DCB estimation can be reduced through the independent iterative reference selection. The accuracy of local ionospheric modeling based on recursive filter is less than 2 Total Electron Content Unit (TECU) in the monthly median sense. The performance of the recursive method is also evaluated under different solar conditions and the results show that the local ionospheric modeling is sensitive to solar conditions. Moreover, the recursive method has the potential to be implemented in the near real-time DCB estimation and GNSS data quality check.