Global Navigation Satellite System Data Processing Research Articles

Celestial frame tie from simulations using phase referencing to GNSS satellites

Aims. For decades now, researchers have been looking for a way to tie the kinematic and dynamic reference frames. Certain worldwide organizations have looked to using co-location in space, combining various techniques. Given the long list of possible applications of the Global Navigation Satellite System (GNSS), it is worthwhile investigating the connection between the most accurate and stable International Celestial Reference Frame (ICRF) and the Earth-centered Celestial Inertial reference frame (ECI) used in GNSS data processing. Methods. We simulated phase-referencing observations of GNSS satellites and nearby radio source calibrators to realize the connection between the two celestial reference frames. We designed two schemes for observation plans. One scheme is to select the satellite target when it can be observed by the greatest number of stations in order to obtain high-precision positioning. During each scan, we employ four regional networks to simultaneously track four chosen satellites. The alternative scheme is to observe satellite orbits of as many satellites as possible on different daily observations. In addition, to test the two schemes, we used Monte Carlo methods to generate 1000 groups of random errors in the simulation. Results. Finally, we estimate the right ascension and declination offsets (∆α, ∆δ) of GNSS satellites in the ICRF, and then derive frame tie parameters based on those results: three global rotation angles (A1, A2, A3). The celestial angular offset results assessed from the former scheme show that this scheme leads to high precision of namely 1 mas, while the parameters of the frame tie determined from the second scheme can achieve an improved precision of better than 1.3 µas.

A Tropospheric Zenith Delay Forecasting Model Based on a Long Short-Term Memory Neural Network and Its Impact on Precise Point Positioning

Global navigation satellite system (GNSS) signals are affected by refraction when traveling through the troposphere, which result in tropospheric delay. Generally, the tropospheric delay is estimated as an unknown parameter in GNSS data processing. With the increasing demand for GNSS real-time applications, high-precision tropospheric delay augmentation information is vital to speed up the convergence of PPP. In this research, we estimate the zenith tropospheric delay (ZTD) from 2018 to 2019 by static precise point positioning (PPP) using the fixed position mode; GNSS observations were obtained from the National Geomatics Center of China (NGCC). Firstly, ZTD outliers were detected, and data gaps were interpolated using the K-nearest neighbor algorithm (KNN). Secondly, The ZTD differences between the KNN and periodic model were employed as input datasets to train the long short-term memory (LSTM) neural network. Finally, LSTM forecasted ZTD differences and the ZTD periodic signals were combined to recover the final forecasted ZTD results. In addition, the forecasted ZTD results were applied in static PPP as a prior constraint to reduce PPP convergence time. Numerical results show that the average root-mean-square error (RMSE) of predicting ZTD is about 1 cm. The convergence time of the PPP which was corrected by the LSTM-ZTD predictions is reduced by 13.9, 22.6, and 30.7% in the summer, autumn, and winter, respectively, over GPT2-ZTD corrected PPP and unconstrained conventional PPP for different seasons.

The Mechanism for GNSS‐Based Kinematic Positioning Degradation at High‐Latitudes Under the March 2015 Great Storm

AbstractIn this study, we focus on the kinematic precise point positioning (PPP) solutions at high‐latitudes during the March 2015 great geomagnetic storm. We aim to discover the mechanism behind the positioning degradation from the perspective of the impacts of the storm‐induced ionospheric disturbance on the global navigation satellite system (GNSS) data processing. We observed that the phase scintillation dominated the amplitude scintillation at high‐latitudes and the variation pattern of the rate of total electron content index (ROTI) was consistent with that of the phase scintillation during the storm. The kinematic PPP errors at high‐latitudes were almost three times larger than those at the middle‐ and low‐latitude, which were accompanied by large ROTI variations. From the perspective of GNSS data processing, the large positioning errors were also found to be related to the large number of satellites experiencing cycle slips (CSs). Based on the lock time from the ionospheric scintillation monitoring receiver, we found that a large amount of the CSs was falsely detected under the conventional threshold of the CS detector. By increasing such threshold, the kinematic positioning accuracy at high‐latitudes can be improved to obtain similar magnitude as at middle‐ and low‐latitude. The improved positioning accuracy may suggest that the ionospheric disturbance induced by the geomagnetic storm at high‐latitudes has minor effects on triggering the CSs. Therefore, precise positioning can be achieved at high‐latitudes under geomagnetic storms, given that the CS problem is well addressed.

Real-Time BDS-3 Clock Estimation with a Multi-Frequency Uncombined Model including New B1C/B2a Signals

The global system of BDS (BeiDou Navigation Satellite System), i.e., BDS-3, is characterized with a multi-frequency signal broadcasting capability, which was demonstrated as beneficial for GNSS (Global Navigation Satellite System) data processing. However, research on real-time BDS-3 clock estimation with multi-frequency signals is quite limited, especially for the new B1C and B2a signals. In this study, we developed models for BDS-3 multi-frequency real-time data processing, including the uncombined model for clock estimation and the GFIF (Geometry-Free Ionosphere-Free) combined model for IFCB (Inter-Frequency Clock Bias) determination. Based on the models, simulated real-time numerical experiments with about 80 global IGS (International GNSS Service) network stations are conducted for validation and analysis. The results indicate that: (1) the uncombined model with multi-frequency signals can achieve comparable accuracy with the traditional dual-frequency IF model in terms of clock estimation, and the double-differenced clock STDs (Standard Deviations) are generally less than 0.05 ns with post-processed clocks as a reference; (2) unlike the B1C and B1I/B3I signals, the satellite IFCBs generated from multi-frequency clock estimation show apparent temporal variations for B2a and B1I/B3I signals, further investigation with GFIF models confirm the variations mainly result from the errors of receiver antenna corrections. Therefore, we addressed the feasibility of the uncombined model and the importance of accurate antenna information in the multi-frequency data processing.

Real-Time Precise GPS Orbit and Clock Estimation With a Quasi-Orbit-Fixed Solar Radiation Pressure Model

<h3>Abstract</h3> Real-time precise global navigation satellite system (GNSS) orbit and clock products play a key role for real-time GNSS-based applications, both in the scientific and industrial communities. Different from the typical two-step procedure to generate orbit and clock solutions separately, we estimate the real-time orbit and clock products simultaneously using a Kalman filter. For this purpose, we developed a GNSS data processing software that can run in pseudo-real-time mode with RINEX files and is ready to run in real-time mode once given the real-time observation stream. Meanwhile, a quasi-orbit-fixed solar radiation pressure (SRP) model is developed. In order to verify the performance of the software and the new SRP model, several experiments with a global network of 60 tracking stations over a time span of three months were conducted to generate real-time Global Positioning System (GPS) orbit and clock products. Then, the results were assessed in terms of accuracy and efficiency, both critical for real-time precise GNSS applications. Compared to the International GNSS Service (IGS) final orbits, the real-time GPS orbit accuracy was 2.82 cm, 5.45 cm, and 5.47 cm in the radial, along-track, and cross-track components, respectively. The precision of the clock product in terms of standard deviation (STD) value was about 0.1 ns. Moreover, the average execution time per epoch was usually less than 1.0 s, which ensures the high efficiency of the processing.

Analysis of Seismic Deformation from Global Three-Decade GNSS Displacements: Implications for a Three-Dimensional Earth GNSS Velocity Field

With the rapid development of Global Navigation Satellite System (GNSS) technology, the long-term accumulated GNSS observations of global reference stations have provided valuable data for geodesy and geodynamics studies since the 1990s. Acquiring the precise velocity of GNSS stations is very important for the study of global plate movement, crustal deformation, etc. However, the seismic activities nearby some GNSS observation stations may seriously change the station’s motion trajectory. Therefore, our research was motivated to propose a method allowing for station seismic deformation, and apply it to construct an updated global GNSS velocity field. The main contributions of this work included the following. Firstly, we improved the GNSS data processing procedures and seismic data selection strategies to obtain GNSS coordinate time series with mm-level precision (3–5 and 6–8 mm in the horizontal and vertical, respectively) and information of each site impacted by seismic events, which provides necessary input data for further analysis. Secondly, an Integrated Time Series Method (ITSM) concerning the effect of seismic deformation was proposed to model the station’s nonlinear motion accurately. Distinguished with existing studies, all parameters including seismic relaxation time can be simultaneously estimated by ITSM, which improves the accuracy and reliability of GNSS station velocity significantly. Thirdly, to optimize the ITSM-based model, the influences of seismic relaxation time (a. 0.1 × true, b. 10 × true, c. true), parameterization mode (a. Offset + Velocity, b. Offset + Velocity + PSD, c. Offset + Velocity + PSD + Period), and the Post-Seismic Deformation (PSD) model (a. None, b. Exp, c. Log, d. Exp + Log) on results of GNSS time series analyzing were discussed. The results showed that the fitting accuracy of GNSS displacements was better than 5 mm and 10 mm in the horizontal and vertical, respectively. Finally, the global GNSS station velocity field (referred to as GGV2020 hereafter) was refined by ITSM using global GNSS observations and seismic data during 1990–2020. This not only helps interpret plate tectonic motion, establish and maintain a Dynamic Terrestrial Reference Frame (DTRF) but also contributes to better investigating geodynamic processes. GGV2020 results showed that the accuracy of global velocity was better than 1 mm/a, and the averages of Root Mean Square Error (RMSE) were 0.19 mm/a, 0.19 mm/a, and 0.33 mm/a in the north, east, and up direction, respectively. Besides, the RMSE obeys normal distribution. Compared with ITRF2014, there was a difference of about 1–2 mm/a between them due to differences in terms of observation span, processing model, and geodetic technology. Moreover, GGV2020 is expected to enrich and update the existing velocity field products to describe the characteristics of regional crustal movement in more detail, especially in Antarctica.

Analysis of GAMIT/GLOBK in high-precision GNSS data processing for crustal deformation

The advances in satellite navigation and positioning technology and the worldwide establishment of continuous-tracking stations have greatly promoted the development and application of the high-precision Global Navigation Satellite System (GNSS). GAMIT/GLOBK, as a popular high-precision GNSS data-processing software, has been widely used in monitoring crustal deformation, tsunami, iceberg, etc. Based on the basic observations and various geophysical models processed by GAMIT/GLOBK, we analyze the influence of the correction of applied geophysical models, and describe the techniques of parameter estimation and accuracy assessment. In addition, taking the present crustal movement in the Chinese mainland and the MW7.8 earthquake in Nepal as examples, we discuss the applications of GAMIT/GLOBK in crustal deformation monitoring and its future prospect.

IWV retrieval from ground GNSS receivers during NAWDEX

Abstract. A ground-based network of more than 1200 Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) was analysed using GIPSY-OASIS II software package for the documentation of time and space variations of water vapor in atmosphere during the North Atlantic Waveguide and Downstream impact EXperiment (NAWDEX) during fall 2016. The network extends throughout the North Atlantic, from the Caribbeans to Morocco through Greenland. This paper presents the methodology used for GNSS data processing, screening, and conversion of Zenith Tropospheric Delay (ZTD) estimates to Integrated Water Vapor content (IWV) using surface parameters from reanalysis. The retrieved IWV are used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses ERAI and ERA5. ERA5 shows an overall improvement over ERAI in representing the spatial and temporal variability of IWV over the study area. The mean bias is decreased from 0.31±0.63 to 0.19±0.56 kg m−2 (mean ±1σ over all stations) and the standard deviation reduced from 2.17±0.67 to 1.64±0.53 kg m−2 combined with a slight improvement in correlation coefficient from 0.95 to 0.97. At regional scale, both reanalyses show a general wet bias at mid and northern latitudes but a dry bias in the Caribbeans. We hypothesize this results from the different nature of data being assimilated over the tropical oceans. This GNSS IWV data set is intended to be used for a better description of the high impact weather events that occurred during the NAWDEX experiment.

An efficient parallel computing strategy for the processing of large GNSS network datasets

The Global Navigation Satellite System (GNSS) has been an indispensable tool for geodetic surveying and geodynamics research and has rapidly developed over the past few years with abundant ground networks, modern constellations and multiple signal frequencies. However, due to increasing numbers of stations and satellites, the data processing burden has increased significantly. In this contribution, an improved parallel computing method is proposed for processing large GNSS network datasets. First, a parallelization strategy is introduced for the traditional GNSS processing model by analyzing the practicability of parallel integrated processing for GNSS network data. In addition, to maximize the advantages of modern microprocessors and local area network environments, we present the multi-core parallel computing of traditional GNSS data processing methods; then, the product is released as a service-oriented architecture that can be found and invoked through multiple nodes in the Internet. Obviously, this method combines the advantages of multi-core parallelism and network parallelism. Experiments show that the efficiency of the proposed method can be further increased with the accumulation of GNSS data and additional nodes. For example, in a network with 2000 stations, the efficiency of the parallel scheme with four quad-core nodes is at least 8 times faster than that of the traditional serial scheme. All the results demonstrate that the proposed strategy is an efficient and promising approach for processing large GNSS network datasets.

Improving GPS and Galileo precise data processing based on calibration of signal distortion biases

The quality of pseudorange observations plays an important role in Global Navigation Satellite System (GNSS) data processing, especially for satellite clock estimation, differential code bias (DCB) estimation and wide-lane ambiguity resolution. It is validated that receivers with different correlator spaces and front-end designs will bring signal distortion bias (SDB) in pseudorange observations, which will affect GNSS data processing based on inhomogenous receivers, such as receivers from Multi-GNSS EXperiment (MGEX) network. We firstly evaluate the consistency of GPS and Galileo satellite clock and DCB products among different International GNSS Service (IGS) Analysis Centers (ACs). The results show that inconsistency exists in both satellite clocks and DCB products among different ACs. For example, the difference of GPS satellite DCB (C1C-C1W) between two ACs, which is free of ionospheric delay, can reach up to 0.31 ns. To improve GPS and Galileo precise data processing based on inhomogenous receivers, pseudorange biases caused by receiver signal distortion are analyzed and calibrated for 5 receiver brands and 19 receiver models. The maximum pseudorange biases of ionospheric-free combination caused by receiver SDBs can reach up to ±3 ns and ±1 ns for GPS and Galileo, respectively. Then, bias corrections of GPS and Galileo raw pseudorange observations are divided into 11 groups and estimated by each receiver group. Finally, several experiments are carried out for the validation of GPS and Galileo SDB correction models. For example, the GPS and Galileo satellite clock offset differences estimated by different receiver brands are decreased by 86.59% and 50.0% for specific receiver type, respectively. The improvement ratio of satellite DCB accuracy is related to satellite systems and signal types, and the maximum improvement can reach up to 95%. Moreover, the maximum improvement of GPS undifferenced wide-lane ambiguity resolution success rate can reach up to 22% for specific receiver types. All these results prove that the bias corrections proposed could greatly eliminate the effect of receiver SDBs on GPS and Galileo precise data processing. Hence, we suggest the corrections of SDBs should be taken into consideration for IGS data reprocessing.

Kinematic GNSS positioning results compared against Agisoft Metashape and Pix4dmapper results produced in the San Joaquin experimental range in Fresno County, California

Abstract RTKLIB which is an open source Global Navigation Satellite Systems (GNSS) software has gained rapid acceptance among Surveying professionals thanks to recent developments in UAS (Unmanned Aerial System) technology. RTKLIB enables standard and precise point positioning (PPP) in real-time and post-processing modes to be performed. As such, UAS users utilize this software to analyze GNSS data collected by GNSS systems on UAS. By being versatile and free, RTKLIB is commonly used by many; however, it is not the only freely available GNSS software. There are also freely available online GNSS data processing software running on servers. These online GNSS data processing services provide data processing in static, kinematic and rapid static modes. Because UAS collect data in kinematic mode, in this study, kinematic data processing by aforementioned software (CSRS-PPP, GAPS and APPS) is analyzed. The results coming from these software are compared against the results produced by photogrammetric software (Agisoft Metashape and Pix4Dmapper). The aim of this practical project is to produce generalizable knowledge about the performance of these software. It is found out that RTKLIB and CSRS-PPP achieved cm-level precision. Yet, GAPS and APPS achieved dm-level precision both for horizontal and vertical coordinates. This study demonstrates the precision and accuracy expected from these software if they are used for kinematic GNSS data processing.

A Real-Time Cycle Slip Detection and Repair Method Based on Ionospheric Delay Prediction for Undifferenced Triple-Frequency BDS Signals

The detection and repair of cycle slips are key steps in high-accuracy GNSS (Global Navigation Satellite System) data processing using carrier phase observations. BDS (BeiDou Navigation Satellite System) triple-frequency observations provide better combinations for cycle slip detections and repairs compared to dual-frequency observations. Although a number of algorithms have been developed and may correctly detect cycle slips most of the time, the reliability of empirical thresholds methods cannot be guaranteed. In this study, an adaptive threshold is proposed for three sets of triple-frequency Geometry-Free (GF) pseudorange minus phase combinations to improve the cycle slip detection performance and reduce the false alarm rate of the cycle slip detection by combining the predicted epoch-differenced ionospheric delays under active ionospheric conditions. Moreover, in the cycle slip repair, the integral combined cycle slips are determined by directly rounding the estimated float-combined cycle slips, which will lead to a repair error if the between-epoch ionospheric variation is large. In this study, a new rounding method considering the predicted epoch-differenced ionospheric delays is proposed, and it is proven that the new method has a higher success rate for estimating the integer value of a cycle slip than the traditional method. The performance of the newly proposed method is validated by using static BDS triple-frequency observations that contain simulated cycle slips. BDS triple-frequency observations were collected at 30-s sampling intervals under active ionospheric conditions. The results show that this method can successfully detect and repair all slips of more than one cycle. In addition, dynamic BDS data collected with a vehicle-based receiver at a 1-s sampling intervals are processed, and the results show that the proposed method is also effective in the detection and repair of cycle slips in dynamic data.

Streaming of Global Navigation Satellite System Data from the Global System of Navigation

The Big Data phenomenon has driven a revolution in data and has provided competitive advantages in business and science domains through data analysis. By Big Data, we mean the large volumes of information generated at high speeds from various information sources, including social networks, sensors for multiple devices, and satellites. One of the main problems in real applications is the extraction of accurate information from large volumes of unstructured data in the streaming process. Here, we extract information from data obtained from the GLONASS satellite navigation system. The knowledge acquired in the discovery of geolocation of an object has been essential to the satellite systems. However, many of these findings have suffered changes as error vocalizations and many data. The Global Navigation Satellite System (GNSS) combines several existing navigation and geospatial positioning systems, including the Global Positioning System, GLONASS, and Galileo. We focus on GLONASS because it has a constellation with 31 satellites. Our research’s difficulties are: (a) to handle the amount of data that GLONASS produces efficiently and (b) to accelerate data pipeline with parallelization and dynamic access to data because these have only structured one part. This work’s main contribution is the Streaming of GNSS Data from the GLONASS Satellite Navigation System for GNSS data processing and dynamic management of meta-data. We achieve a three-fold improvement in performance when the program is running with 8 and 10 threads.

Web tabanlı CSRS-PPP uygulamasının farklı uydu sistemleri üzerindeki performansı

Küresel Navigasyon Uydu Sistemleri’nden (Global Navigation Satellite Systems, GNSS) elde edilen veriler değerlendirilirken bilimsel/akademik, ticari yazılımlar ve kullanımı gittikçe artan web tabanlı uygulamalar kullanılmaktadır. GNSS veri değerlendirme stratejisi mutlak ve bağıl olarak temelde ikiye ayrılmaktadır. Kullanımı artan web tabanlı servisler temelde mutlak yöntemi kullanan ya da bağıl yöntemi kullananlar olarak ayrılmaktadır. Hassas mutlak nokta konumlama (Precise Point Positioning, PPP) yöntemi tek bir alıcı ile cm mertebesinde konum belirlemeyi mümkün kılmakta ve kullanıcılar açısından oldukça pratik olarak konum belirleme imkânı sunmaktadır. PPP yönteminin gerçek zamanlı uygulamaları da gittikçe yaygınlaşmakta, bu durum kullanıcılar açısından hem zaman hem de maliyet tasarrufu imkânları sunmaktadır. Konum belirleme tarafında bu gelişmeler yaşanırken, ilk ortaya çıktığı günden bu yana askeri amacının yanında insanoğlunun hayatına her alanda giren Küresel Konumlama Sistemi (GPS), diğer sistemlerin de (GLONASS, BeiDou, QZSS, IRNSS vb.) devreye alınması ile oldukça yaygınlaşmıştır. Bugün artık GNSS olarak hayatımızın içinde daha fazla var olmaya devam etmektedir. Çeşitli ülkeler tarafından geliştirilen ve kullanılan uydu sistemlerinin dünya üzerine yayılmış bulunan Uluslararası GNSS Servisi (International GNSS Service, IGS) istasyonlarında veri toplanarak, konum belirleme sürecine dahil edilmesi ile The Multi-GNSS Experiment (MGEX) projesi geliştirilmiş ve belirlenen istasyonlarda, tüm uydu sistemlerinden veri toplanmasına başlanılmıştır. Bu çalışmada, web tabanlı CSRS-PPP uygulaması ile dünya üzerinde dağılmış 5 MGEX istasyonunda toplanan 10 günlük GNSS gözlem verileri değerlendirilmiştir. MGEX projesi kapsamında istasyonlardan gözlem verileri 1 saniyeden 30 saniyeye kadar gözlem aralığında ve çoklu uydu sistemlerinden (GPS, GLONASS, Galileo, QZSS, NAVIC, BeiDou, SBAS) veri toplama imkânı bulunmaktadır. CSRS-PPP uygulamasının değerlendirme stratejisinde kullandığı GPS ve GLONASS (GPS, GLONASS ve GPS+GLONASS) uydu sistemleri ile 1 ile 30 saniye aralıklı toplanmış gözlem verilerinin en az 15 dakikadan 24 saate kadar gözlem aralığında ve farklı iyonosferik koşullardaki performansı değerlendirilmiş ve yorumlanmıştır.

Improved Ultra-Rapid UT1-UTC Determination and Its Preliminary Impact on GNSS Satellite Ultra-Rapid Orbit Determination

Errors in ultra-rapid UT1-UTC primarily affect the overall rotation of spatial datum expressed by GNSS (Global Navigation Satellite System) satellite ultra-rapid orbit. In terms of existing errors of traditional strategy, e.g., piecewise linear functions, for ultra-rapid UT1-UTC determination, and the requirement to improve the accuracy and consistency of ultra-rapid UT1-UTC, the potential to improve the performance of ultra-rapid UT1-UTC determination based on an LS (Least Square) + AR (Autoregressive) combination model is explored. In this contribution, based on the LS+AR combination model and by making joint post-processing/rapid UT1-UTC observation data, we propose a new strategy for ultra-rapid UT1-UTC determination. The performance of the new strategy is subsequently evaluated using data provided by IGS (International GNSS Services), iGMAS (international GNSS Monitoring and Assessment System), and IERS (International Earth Rotation and Reference Systems Service). Compared to the traditional strategy, the numerical results over more than 1 month show that the new strategy improved ultra-rapid UT1-UTC determination by 29–43%. The new strategy can provide a reference for GNSS data processing to improve the performance of ultra-rapid products.

Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation

Global navigation satellite systems (GNSS) are an important tool for positioning, navigation, and timing (PNT) services. The fast and high-precision GNSS data processing relies on reliable integer ambiguity fixing, whose performance depends on phase bias estimation. However, the mathematic model of GNSS phase bias estimation encounters the rank-deficiency problem, making bias estimation a difficult task. Combining the Monte-Carlo-based methods and GNSS data processing procedure can overcome the problem and provide fast-converging bias estimates. The variance reduction of the estimation algorithm has the potential to improve the accuracy of the estimates and is meaningful for precise and efficient PNT services. In this paper, firstly, we present the difficulty in phase bias estimation and introduce the sequential quasi-Monte Carlo (SQMC) method, then develop the SQMC-based GNSS phase bias estimation algorithm, and investigate the effects of the low-discrepancy sequence on variance reduction. Experiments with practical data show that the low-discrepancy sequence in the algorithm can significantly reduce the standard deviation of the estimates and shorten the convergence time of the filtering.

Evaluation of precipitable water vapor variation for east mediterranean using GNSS

The study of climate change is an important field of research. Monitoring of atmospheric variability especially the tropospheric precipitable water vapor (PWV) is a powerful way to investigate climate change. Global navigation satellite systems (GNSS) provide a good tool for studying atmospheric parameters as GNSS signals along its path from the satellites to the ground based receivers suffer a significant delay due to the refractivity of earth’s atmosphere. GNSS signals are not only delayed but also refracted in the neutral atmosphere. The zenith wet delays (ZWD) caused by the troposphere can be estimated during the geodetic processing of GNSS signals. Since the ZWD is tightly correlated to the PWV, GNSS observations can be used to study the changes of PWV. In this study GNSS data from the Egyptian permanent GNSS network (EPGN), International GNSS Service (IGS), EUREF Permanent Network (EPN) and Scripps Orbit and Permanent Array Center (SOPAC), for the years 2013 and 2014, were used. These GNSS stations provided a good geometrical coverage over the respective region. All GNSS data were processed using Bernese V 5.2 software. The needed product from GNSS data processing is the tropospheric zenith total delay (ZTD) over each GNSS site. The ZTD is divided based on physical parameters into zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD). The ZWD is the basic observable used to calculate PWV. The values of the PWV were calculated and its variation over the study area was investigated. The quality of calculated PWV values from GNSS data evaluated against traditional Radiosonde (RS) measurements. The results of GNSS PWV showed good agreement with RS PWV when the PWV values were low, but this agreement became worse at high PWV values as the differences between the two techniques increased. The correlation coefficient between RS PWV and GNSS PWV varied from 0.31 to 0.84. Standard deviation of the differences between RS PWV and GNSS PWV ranged from 2.369 to 5.973 mm. The PWV estimated from GNSS observations had annual cycle, and its cycle in 2013 was different from that in 2014. The PWV differences between the 2 years clarified that the water vapor content over the east Mediterranean in most of 2014 days was higher than that in 2013. Furthermore, PWV variations were noted on both temporal and spatial scales. The highest temporal variation value was 25.41 mm whereas the maximum value of the spatial variation was 19.67 mm. The present study illustrated the importance of using geodetic networks to provide atmospheric information.

Characteristics Analysis of Raw Multi-GNSS Measurement from Xiaomi Mi 8 and Positioning Performance Improvement with L5/E5 Frequency in an Urban Environment

Achieving continuous and high-precision positioning services via smartphone under a Global Navigation Satellite System (GNSS)-degraded environment is urgently demanded by the mass market. In 2018, Xiaomi launched the world’s first dual-frequency GNSS smartphone, Xiaomi Mi 8. The newly added L5/E5 signals are more precise and less prone to distortions from multipath reflections. This paper discusses the characteristics of raw dual-frequency GNSS observations from Xiaomi Mi 8 in urban environments; they are characterized by high pseudorange noise and frequent signal interruption. The traditional dual-frequency ionosphere-free combination is not suitable for Xiaomi Mi 8 raw GNSS data processing, since the noise of the combined measurements is much larger than the influence of the ionospheric delay. Therefore, in order to reasonably utilize the high precision carrier phase observations, a time differenced positioning filter is presented in this paper to deliver continuous and smooth navigation results in urban environments. The filter first estimates the inter-epoch position variation (IEPV) with time differenced uncombined L1/E1 and L5/E5 carrier phase observations and constructs the state equation with IEPV to accurately describe the user’s movement. Secondly, the observation equations are formed with uncombined L1/E1 and L5/E5 pseudorange observations. Then, kinematic experiments in open-sky and GNSS-degraded environments are carried out, and the proposed filter is assessed in terms of the positioning accuracy and solution availability. The result in an open-sky environment shows that, assisted with L5/E5 observations, the root mean square (RMS) of the stand-alone horizontal and vertical positioning errors are about 1.22 m and 1.94 m, respectively, with a 97.8% navigation availability. Encouragingly, even in a GNSS-degraded environment, smooth navigation services with accuracies of 1.61 m and 2.16 m in the horizontal and vertical directions are obtained by using multi-GNSS and L5/E5 observations.

The Performance of Different Mapping Functions and Gradient Models in the Determination of Slant Tropospheric Delay

Global navigation satellite systems (GNSSs) have become an important tool for remotely sensing water vapor in the atmosphere. In GNSS data processing, mapping functions and gradient models are needed to map the zenith tropospheric delay (ZTD) to the slant total tropospheric delay (STD) along a signal path. Therefore, it is essential to investigate the spatial–temporal performance of various mapping functions and gradient models in the determination of STD. In this study, the STDs at nine elevations were first calculated by applying the ray-tracing method to the atmospheric European Reanalysis-Interim (ERA—Interim) dataset. These STDs were then used as the reference to study the accuracy of the STDs that determined the ZTD together with mapping functions and gradient models. The performance of three mapping functions (i.e., Niell mapping function (NMF), global mapping function (GMF), and Vienna mapping function (VMF1)) and three gradient models (i.e., Chen, MacMillan, and Meindl) in six regions (the temperate zone, Qinghai–Tibet Plateau, Equator, Sahara Desert, Amazon Rainforest, and North Pole) in determining slant tropospheric delay was investigated in this study. The results indicate that the three mapping functions have relatively similar performance above a 15° elevation, but below a 15° elevation, VMF1 clearly performed better than the GMF and NMF. The results also show that, if no gradient model is included, the root-mean-square (RMS) of the STD is smaller than 2 mm above the 30° elevation and smaller than 9 mm above the 15° elevation but shows a significant increase below the 15° elevation. For example, in the temperate zone, the RMS increases from approximately 35 mm at the 10° elevation to approximately 160 mm at the 3° elevation. The inclusion of gradient models can significantly improve the accuracy of STDs by 50%. All three gradient models performed similarly at all elevations and in all regions. The bending effect was also investigated, and the results indicate that the tropospheric delay caused by the bending effect is normally below 13 mm above a 15° elevation, but this delay increases dramatically from approximately 40 mm at a 10° elevation to approximately 200 mm at a 5° elevation, and even reaches 500–700 mm at a 3° elevation in most studied regions.

Algorithms for Integrated Processing of Marine Gravimeter Data and GNSS Measurements

Efficiency of using global navigation satellite system (GNSS) measurements for determining gravity anomalies (GA) at sea by solving filtering and smoothing problems based on GNSS and gravimeter data is studied. The GA, ship heaving, errors of GNSS and gravimeter measurements are presented as stochastic processes. The analysis is based on the standard deviations of the GA estimation errors, calculated at different heaving parameters and in different modes of GNSS data processing.