High-precision Global Navigation Satellite System Research Articles

GCMEbox: A MATLAB toolbox for extracting and analyzing common-mode errors from GNSS time series

High-precision global navigation satellite system (GNSS) time series data contain rich tectonic and non-tectonic motion information. However, such data also have common-mode errors (CME) related to spatiotemporal characteristics. CME will produce bias in accurate and reliable GNSS station positions and velocities estimations, ultimately deciphering erroneous geophysical signals. Currently, CME extraction methods can be divided into three categories: regional stack filtering, reference frame filtering, and statistical signal decomposition. The third method is widely used in geodetic data processing, including GNSS time series analysis, global time-varying gravity signal extraction, interferometric synthetic aperture radar (InSAR) time series analysis, etc. The demand for GNSS time series analysis software with interactive, visualization, and multifunctional features has also increased with the increase in GNSS stations. Therefore, we developed gCMEbox based on MATLAB. The gCMEbox’s primary functions include GNSS time series preprocessing, multiple statistical signal decomposition algorithms for extracting CME, GNSS time series analysis, visualization, velocity field construction, and generation of multiple time series post-processing products. Based on GNSS time series data, this study utilized the gCMEbox for data preprocessing, including format conversion, outlier removal, missing data recovery, etc. By applying statistical signal decomposition methods, gCMEbox successfully extracted CME. The primary objective of this study is to share this toolbox with the scientific community, providing a comprehensive tool for GNSS time series analysis and applications.

Effect of WHU/GFZ/CODE satellite attitude quaternion products on the GNSS kinematic PPP during the eclipse season

To ensure high-precision Global Navigation Satellite System (GNSS) applications, many analysis centers (ACs) have begun to provide satellite attitude products, which affect the modeling of phase variations and phase wind-up. Despite several studies having demonstrated the importance of providing attitude products, particularly eclipsing products, the performance of satellite attitude models for different ACs has not been further investigated. This study compared the satellite attitude models provided by Wuhan university (WHU), German Research Centre of Geosciences (GFZ), and Center for Orbit Determination in Europe (CODE), and investigated the impact of satellite attitude products on high-rate GNSS data processing (i.e., 1HZ). The satellite attitudes of most satellite blocks were consistent well with each other, except for the BDS-2 inclined geostationary orbit (IGSO), BDS-3 medium earth orbit (MEO), GPS IIIA, and GLONASS-M satellites. The differences in the yaw angle between the different ACs ranged as 10–180°. Compared to the good consistency of the wum and com products, gbm exhibited significant differences from the former for most constellations. Following the application of the satellite attitude products, the average 3D RMS value of the single-kinematic PPP was improved by 11% and 10% for the 30-s and 1-s sampling rate observations, respectively. Thus, these eclipsing satellites with inaccurate attitude models can be deleted while integrating more satellites into the GNSS data processing.

Small energy benefits of in-wake flying in long-duration migratory flights.

During long-distance migrations, some bird species make use of in-wake flying, which should allow them to profit from the upwash produced by another bird. While indirect evidence supports energy saving as the primary benefit of in-wake flying, measurements are still missing. We equipped migrating northern bald ibises (Geronticus eremita) with high-precision global navigation satellite system data loggers to track their position in the flock. We estimated birds' energy expenditure through different proxies, namely dynamic body acceleration (DBA), heart rate and effective wingbeat frequency. During active flapping flight, DBA estimates dropped off when in-wake compared with when not-in-wake. In addition, effective wingbeat frequency decreased, suggesting an increased use of intermittent gliding flight during in-wake periods. Heart rate varied greatly among individuals, with a clear decrease during gliding-corroborating its energy-saving function. Furthermore, we found consistent proof for decreased heart rate during in-wake flying, by up to 4.2%. Hence, we have shown that flying in the wake of another individual reduces birds' DBA, heart rate and effective wingbeat frequency, which could reflect reduced energy requirement.

High-precision GPS orbit determination by integrating the measurements from regional ground stations and LEO onboard receivers

High-precision Global Navigation Satellite System (GNSS) orbit and clock products are crucial for precise applications. An evenly distributed global network enables continuous tracking for GNSS satellites, while a regional network may result in tracking gaps in the areas where monitoring stations are not deployed. This also means that the orbit determination accuracy based on a regional network is not comparable to that with a global network. Integrating the measurements from regional ground stations and Low Earth Orbit (LEO) satellites onboard receivers is a potential approach for generating GNSS orbit and clock products with centimeter-level accuracy, which is particularly important for BDS and the local commercial providers relying on a regional network. In the integrated Precise Orbit Determination (POD), LEO satellites are used to compensate for the drawback of regional ground stations in the precise orbit and clock determination of GNSS satellites. To validate the role of LEO satellites in the orbit determination with a regional network, 6 International GNSS Service stations around China and 13 LEO satellites from January 20 to 26, 2019, including GRACE-C/D, SWARM-A/B/C, Jason-3, Sentinel-3A/B, and SAT-A/B/C/D/E are selected in this study to perform the integrated POD. The orbit and clock accuracies of GPS and LEO satellites are evaluated by comparison with precise products. The average Root Mean Square (RMS)of GPS orbit errors in the radial (R), along-track (T) and cross-track (N) directions are 2.27 cm, 3.45 cm, and 3.08 cm, respectively, and the clock accuracy is better than 0.15 ns based on a comparison with the final products provided by Center for Orbit Determination in Europe (CODE). The LEO orbit accuracy is better than 2 cm in the R direction, and the position errors are mostly within 4 cm. The results indicate that the integrated POD can generate high-precision orbit and clock products for GPS and LEO satellites based on regional network stations. Finally, the integrated POD products are assessed for Precise Point Positioning (PPP). Simulated kinematic PPP has a comparable performance in terms of the convergence time and positioning accuracy. With more LEO satellites available, the orbit and clock determination accuracy and PPP positioning accuracy can be improved.

Multipath mitigation for GPS/Galileo/BDS-3 precise point positioning with overlap-frequency signals

The multipath effect is a major Global Navigation Satellite System (GNSS) error source due to its environment-dependent characteristic, which complicates its mitigation process for the high-rate determination of displacements. For instance, Sidereal Filtering (SF) and Multipath Hemispherical Map (MHM) require the observations spanning at least one full cycle of satellite orbit repeat period (e.g., ten days for Galileo navigation satellite system (Galileo) to reproduce the satellite geometry against ground stations. As a consequence, the practicability of SF and MHM is limited due to potential station-surrounding changes over a long period. In this study, we used the overlap-frequency signals on Global Positioning System (GPS) L1/L5, Galileo E1/E5a, and BeiDou-3 Navigation Satellite System (BDS-3) B1C/B2a to construct an interoperable MHM (i.e., MHM_GEC) across constellations to mitigate multipath more efficiently. We thus used 31 days of 1-Hz GPS/Galileo/BDS-3 data at 21 stations in Europe to compare this overlap-frequency MHM with those GNSS-specific MHMs (i.e., MHM_G for GPS, MHM_E for Galileo, and MHM_C for BDS-3), as well as SF. It is confirmed that the multipath effects on overlap-frequency signals are of a high spatial consistency across all GNSS. The mean reduction rate of applying MHM_GEC to GPS, Galileo, and BDS-3 carrier-phase residuals is 25%, 31%, and 28.5%, respectively, which are up to 25 percentage points higher than those of MHM_G, MHM_E, and MHM _C. Furthermore, the MHM_GEC constructed using 5 to 6 days of data can improve the positioning precision by 40%, outperforming the MHM_E, MHM_C, and SF using 10 days of data. Therefore, the interoperable MHM_GEC is more efficient in mitigating multipath effects for high-precision GNSS positioning.

Improving atmospheric pressure vertical correction model using Gaussian function

The Zenith Hydrostatic Delay (ZHD) is essential for high-precision Global Navigation Satellite System (GNSS) and Very Long Baseline Interferometry (VLBI) data processing. Accurate estimation of ZHD relies on in situ atmospheric pressure, which is primarily variable in the vertical direction. Current atmospheric pressure is either site-specific or has limited spatial coverage, necessitating vertical corrections for broader applicability. This study introduces a model that uses a Gaussian function for the vertical correction of atmospheric pressure when in situ meteorological observations are unavailable. Validation with the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) reveals an average Bias and RMS for the new model of 0.31 hPa and 2.96 hPa, respectively. This corresponds to improvements of 37.5% and 80.3% in terms of RMS compared to two commonly used models (T0 and Tv models) that require in situ meteorological observations, respectively. Additional validation with radiosonde data shows an average Bias and RMS of 1.85 hPa and 4.87 hPa, corresponding to the improvement of 42.8% and 71.1% in RMS compared with T0 and Tv models, respectively. These accuracies are sufficient for calculating ZHD to an accuracy of 1 mm by performing atmospheric pressure vertical correction. The new model can correct atmospheric pressure from meteorological stations or numerical weather forecasts to different heights of the troposphere.

GNSS carrier phase common-view precision time transfer using the ionospheric-weighted single-differenced model

ABSTRACT High-precision global navigation satellite system (GNSS) time transfer depends on the carrier phase observations, and ionospheric delay affects rapid integer ambiguity resolution. Therefore, this work applies the ionospheric-weighted single-differenced model. Experimental results indicate that for a time-link of 67.9 km, the time transfer accuracy of different single-system is better than 0.03 ns. Compared with single-system, multi-system demonstrates 8% to 14% improvement in accuracy and improvement of no more than 31% in frequency stability. Additionally, compared with the case without ionospheric constraint, imposing ionospheric constraints can improve performance of time transfer, but the gain decreases as the time-link length increases.

High-resolution reconstruction and correction of FY-4A precipitable water vapor in China using back propagation neural network

Precipitable water vapor (PWV) with high spatial and temporal resolution is essential for a deeper understanding of the study area's hydrological cycle and climate change. This study uses the European Center for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) PWV as auxiliary verification data and proposes a reconstruction method based on back propagation neural network (BPNN) downscaling. Combining with high temporal resolution meteorological elements, digital elevation model (DEM), location, and time, reconstructed the Fengyun-4A (FY-4A) PWV data set with high spatial and temporal resolution in China was obtained. Subsequently, using the characteristics of FY-4A PWV with DEM, location, and time, a BPNN-based correction method is proposed to correct the reconstructed FY-4A PWV using the high-precision global navigation satellite system (GNSS) PWV. The results showed that the root mean square error (RMSE), Bias, and Pearson correlation coefficient (R) of the reconstructed model with respect to the FY-4A PWV are 1.32 mm, 0 mm, and 0.99, respectively. Compared with the auxiliary verification data ERA5 PWV, the RMSE, Bias, and R of the reconstructed FY-4A PWV are 0.85 mm, 0.18 mm, and 0.99, respectively. Compared with the GNSS PWV, the RMSE of the corrected FY-4A PWV is 2.44 mm, which is 28.86% lower than that of the reconstructed FY-4A PWV, the Bias is improved from −1.46 mm to 0 mm, and the R is enhanced from 0.98 to 0.99. The reconstruction and correction method of FY-4A PWV with high spatial and temporal resolution proposed in this study dramatically improves the completeness and accuracy of FY-4A PWV in China. It lays the foundation for studying rapid changes in China's hydrological cycle and climate.

Improving GNSS PPP-RTK through global forecast system zenith wet delay augmentation

The precise point positioning real-time kinematic (PPP-RTK) is a high-precision global navigation satellite system (GNSS) positioning technique that combines the advantages of wide-area coverage in precise point positioning (PPP) and of rapid convergence in real-time kinematic (RTK). However, the PPP-RTK convergence is still limited by the precision of slant ionospheric delays and tropospheric zenith wet delay (ZWD), which affects the PPP-RTK network parameters estimation and user positioning performance. The present study aims to construct a PPP-RTK model augmented with a priori ZWD values derived from the global forecast system (GFS) product (a global numerical weather prediction (NWP) model) to improve the PPP-RTK performance. This study gives a priori ZWD values and conversion based on the GFS products, and the full-rank GFS-augmented undifferenced and uncombined (UDUC) PPP-RTK network model is derived. To verify the performance of GFS-augmented UDUC PPP-RTK, a comprehensive evaluation using 10-day GNSS observation data from three different GNSS station networks in the United States (US), Australia, and Europe is conducted. The results show that with the GFS ZWD a priori information, PPP-RTK performance significantly improves at the initial filtering stage, but this advantage gradually decays over time. Based on 10-day positioning results for all user stations, the GFS ZWD-augmented PPP-RTK approach reduces the average convergence time by 46% from 10.0 to 5.4 min, the three-dimensional root-mean-square (3D-RMS) error by 5.7% from 3.5 to 3.3 cm, and the time to first fix (TTFF) value by 35.8% from 6.7 to 4.3 min, all when compared to the traditional PPP-RTK without GFS ZWD constraints.

ConvGRU-MHM: a CNN GRU-enhanced MHM for mitigating GNSS multipath

In high-precision global navigation satellite system (GNSS) short-baseline positioning, multipath is the main source of errors. If the station environment is quasi-static, repeat periods of satellites can be utilized to generate time- or space-dependent multipath models to mitigate multipaths. However, two general problems are associated with the multipath models constructed based on satellite mechanics: (1) an accuracy decrease occurs when the above models are applied to multipath mitigation over a long time-span; (2) when constructing the spatial and temporal grids of the satellite-based spatially dependent multipath model, it is challenging to balance computational efficiency and spatial resolution. We propose a convolutional neural network-gated recurrent unit enhanced multipath hemispherical map (ConvGRU-MHM) in the observational domain to address these problems. The proposed method directly mines the deep features of elevation, azimuth angle, and multipath and the mapping relationship between these to establish a real-time prediction model. The predicted multipath is obtained and returned to the observation equation for multipath mitigation when the real-time position of the satellite is placed in the pre-trained model. We compared the multipath mitigation performance of sidereal filtering and a MHM with that of the ConvGRU-MHM to demonstrate the advantages of the proposed method. The experimental results are as follows: (1) in the short time-span (first 20 d), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction performed better than those of the SF and MHM; and (2) in the long-term time (after 50 d), the mean accuracy improvements of the ConvGRU-MHM in the E/N/U direction are higher than that of the SF and MHM by 10%–20%. As a lightweight model, the ConvGRU-MHM can effectively improve the measurement accuracy of GNSS real-time monitoring in fields, such as deformation monitoring and seismic research.

A global grid model for the estimation of zenith tropospheric delay considering the variations at different altitudes

Abstract. The accuracy of tropospheric delay correction heavily depends on the quality of the tropospheric model, and the zenith tropospheric delay (ZTD) is an important factor affecting the tropospheric delay. Therefore, it is essential to establish a precise ZTD empirical model. The existing ZTD models are constrained by a single fitting function, lack consideration for daily cycle variations, and rely solely on data with one resolution for modeling. To address these limitations, we proposed a global piecewise ZTD empirical grid (GGZTD-P) model. This model considers the daily cycle variation and latitude factor of ZTD, using the sliding window algorithm based on fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis data (ERA5). The ZTD data from 545 radiosonde stations and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) atmospheric reanalysis data are used to validate the accuracy of the GGZTD-P model. The results indicate that the GGZTD-P model outperforms the global pressure and temperature 3 (GPT3) model, exhibiting 26 % and 53 % lower bias and rms, respectively, when using radiosonde stations as reference values. Furthermore, when evaluated using MERRA-2 atmospheric reanalysis data, the GGZTD-P model consistently exhibits superior performance across various latitude regions. It is expected that the application of this new model will provide improved services for high-precision global navigation satellite system (GNSS) positioning and GNSS meteorology.

Multi-GNSS ultra-rapid orbit determination through epoch-parallel processing

High-precision Global Navigation Satellite Systems (GNSS) orbits are critical for real-time clock estimation and precise positioning service; however, the prediction error grows gradually with the increasing prediction session. In this study, we present a new efficient precise orbit determination (POD) strategy referred to as the epoch-parallel processing to reduce the orbit update latency, in which a 24-h processing job is split into several sub-sessions that are processed in parallel and then stacked to solve and recover parameters subsequently. With a delicate handling of parameters crossing different sub-sessions, such as ambiguities, the method is rigorously equivalent to the one-session batch solution, but is much more efficient, halving the time-consuming roughly. Together with paralleling other procedures such as orbit integration and using open multi-processing (openMP), the multi-GNSS POD of 120 satellites using 90 stations can be fulfilled within 30 min. The lower update latency enables users to access orbits closer to the estimation part, that is, 30–60-min prediction with a 30-min update latency, which significantly improves the orbit quality. Compared to the hourly updated orbit, the averaged 1D RMS values of predicted orbit in terms of overlap for GPS, GLONASS, Galileo, and BDS MEO are improved by 39%, 35%, 41%, and 37%, respectively, and that of BDS GEO and IGSO satellites is improved by 47%. We also demonstrate that the boundary discontinuities of half-hourly orbit are within 2 cm for the GPS, GLONASS, and Galileo satellites, and for BDS the values are 2.6, 15.5, and 9.8 cm for MEO, GEO, and IGSO satellites, respectively. This method can also be implemented for any batch-based GNSS processing to improve the efficiency.

Machine learning-based multipath modeling in spatial domain applied to GNSS short baseline processing

Multipath is the main unmodeled error source hindering high-precision Global Navigation Satellite System data processing. Conventional multipath mitigation methods, such as sidereal filtering (SF) and multipath hemispherical map (MHM), have certain disadvantages: They are either not easy to use or not effective enough for multipath mitigation. In this study, we propose a machine learning (ML)-based multipath mitigation method. Multipath modeling was formulated as a regression task, and the multipath errors were fitted with respect to azimuth and elevation in the spatial domain. We collected 30 days of 1 Hz GPS data to validate the proposed method. In total, five short baselines were formed and multipath errors were extracted from the postfit residuals. ML-based multipath models, as well as observation-domain SF and MHM models, were constructed using 5 days of residuals before the target day and later applied for multipath correction. It was found that the XGBoost (XGB) method outperformed SF and MHM. It achieved the highest residual reduction rates, which were 24.9%, 36.2%, 25.5% and 20.4% for GPS P1, P2, L1 and L2 observations, respectively. After applying the XGB-based multipath corrections, kinematic positioning precisions of 1.6 mm, 1.9 mm and 4.5 mm could be achieved in east, north and up components, respectively, corresponding to 20.0%, 17.4% and 16.7% improvements compared to the original solutions. The effectiveness of the ML-based multipath model was further validated using 30 s sampling data and data from a low-cost device. We conclude that the ML-based multipath mitigation method is effective, easy to use, and can be easily extended by adding auxiliary input features, such as signal-to-noise ratio, during model training.

GNSS Receiver Antenna Absolute Field Calibration System Development: Testing and Preliminary Results

For high-precision Global Navigation Satellite Systems (GNSS) positioning based on carrier-phase measurements, knowledge of the GNSS receiver antenna electrical signal reception characteristics, i.e., phase center, is crucial. Numerous studies have led to the understanding of the influence of GNSS receiver antenna phase center corrections (PCCs) on GNSS positioning accuracy and other estimated parameters (e.g., receiver clock estimates, ambiguities, etc.). With the goal of determining the PCC model of GNSS receiver antennas, only a few antenna calibration systems/facilities are in operation or under development worldwide. The International GNSS Service (IGS) publishes type-mean PCC models for almost all geodetic-grade GNSS antennas. However, the type-mean models are not perfect and do not fully reflect the signal reception properties of individual GNSS receiver antennas. Relevant published scientific research has shown that the application of individual PCC models significantly improves the accuracy of GNSS positioning and other estimated parameters. In this article, the new automated GNSS antenna calibration system, recently developed at the Laboratory for Measurements and Measuring Technique (LMMT) of the Faculty of Geodesy of the University of Zagreb in Croatia, is presented. The developed system is an absolute field calibration system based on the utilization of a Mitsubishi MELFA 6-axis industrial robot. During calibration, the robot tilts and rotates the GNSS antenna under test (AUT) around a fixed point within the antenna. The antenna PCC modelling is based on time-differenced double-difference carrier-phase observations. Our preliminary results for the Global Positioning System (GPS) L1 (G01) frequency show a submillimeter repeatability of the estimated PCC model and a submillimeter agreement with the Geo++ GmbH calibration results.

A Relative Field Antenna Calibration Method Designed for Low-Cost GNSS Antennas by Exploiting Triple-Differenced Measurements

Performing the high-precision Global Navigation Satellite System (GNSS) applications with low-cost antennas is an up-and-coming research field. However, the antenna-induced phase biases, i.e., phase center corrections (PCCs), of the low-cost antennas can be up to centimeters and need to be calibrated in advance. The relative field antenna calibration method is easy to conduct, but the classical procedure entails integer ambiguity resolution, which may face the problem of low success rate under the centimeter-level PCCs. In this contribution, we designed a relative field calibration method suitable for the low-cost GNSS antennas. The triple-differencing operations were utilized to eliminate the carrier-phase ambiguities and then construct PCC measurements; the time-differencing interval was set to a relatively long time span, such as one hour, and the reference satellite was selected according to the angular distance it passed over during a time-differencing interval. To reduce the effect of significant triple-differencing noise, a weight setting method based on the area of a spherical quadrilateral was proposed for the spherical harmonics fitting process. The duration of the data collection with respect to GPS and BDS was discussed. The performance of the proposed method was assessed with real GPS and BDS observations and a variety of simulated phase patterns, showing that calibration results could be obtained with millimeter-level accuracy. The impact of cycle slip and elevation mask angle on the calibration results was also analyzed.

Detecting Maritime GPS Spoofing Attacks Based on NMEA Sentence Integrity Monitoring

Today’s maritime transportation relies on global navigation satellite systems (GNSSs) for accurate navigation. The high-precision GNSS receivers on board modern vessels are often considered trustworthy. However, due to technological advances and malicious activities, this assumption is no longer always true. Numerous incidents of tampered GNSS signals have been reported. Furthermore, researchers have demonstrated that manipulations can be carried out even with inexpensive hardware and little expert knowledge, lowering the barrier for malicious attacks with far-reaching consequences. Hence, exclusive trust in GNSS is misplaced, and methods for reliable detection are urgently needed. However, many of the proposed solutions require expensive replacement of existing hardware. In this paper, therefore, we present MAritime Nmea-based Anomaly detection (MANA), a novel low-cost framework for GPS spoofing detection. MANA monitors NMEA-0183 data and advantageously combines several software-based methods. Using simulations supported by real-world experiments that generate an extensive dataset, we investigate our approach and finally evaluate its effectiveness.

A STAP anti-interference technology with zero phase bias in wireless IoT systems based on high-precision positioning

Fog computing has been applied to the data processing for the Internet of Things (IoT) based on distributed high-precision Global Navigation Satellite Systems (GNSS). However, the space-time adaptive processing (STAP) interference suppression technology in the system will cause fog computing data deviation that includes carrier phase bias and pseudocode offset. An unbiased STAP technique is proposed to eliminate these deviations. First, it is analyzed that the carrier phase bias and pseudocode offset are caused by the non-linear phase response of the STAP equivalent filter. Then, a coefficient-constrained method based on practical engineering processing is proposed, which can eliminate these deviations by restricting the tap coefficients to be symmetrically equal around the center-tap. Moreover, by analyzing the coherent integral function of the pseudocode after filtering, the tap structure of STAP is modified to eliminate the group offset of the pseudocode without increasing the computational complexity and hardware resources. Finally, the unbiased performance and anti-interference performance of the system are verified by numerical and real data simulations.

A Data Quality Assessment Approach for High-Precision GNSS Continuously Operating Reference Stations (CORS) with Case Studies in Hong Kong and Canada/USA

Centimeter-level or better positioning accuracy is needed in engineering surveying applications. When employing the Global Navigation Satellite System (GNSS) in engineering surveying, the high-precision real-time kinematic (RTK) positioning method must be used to achieve such positioning accuracy. Currently, precise point positioning (PPP) cannot reliably achieve the positioning accuracy needed for engineering surveying in real time. The high-precision RTK positioning method needs carrier-phase measurements and a reference station/network. Surveyors may not need a GNSS receiver in their organizations/companies to act as the reference station. Continuously Operating Reference Stations (CORS), run by international/national organizations/agencies or private companies such as GNSS receiver manufacturers, let users freely access the raw GNSS measurements or corrections for real-time and post-processing applications. The positioning accuracy of the GNSS rover is affected by the data quality of the reference stations, including virtual reference stations (VRS). The International GNSS Service (IGS) currently provides the number of cycle slips and the L1 and L2 average pseudorange multipath errors per station daily. The US National Geodetic Survey (NGS) provides daily station coordinate residuals. Carrier-phase data quality of the CORS stations is not provided by their organizations/agencies. Nowadays, many CORS stations track multi-GNSS satellites. This paper proposes a multi-GNSS and multi-frequency data quality assessment approach for CORS stations with a focus on carrier-phase data quality. The proposed approach is demonstrated with case studies on IGS/CORS networks in Hong Kong and Canada/USA. In other words, a strategy to obtain non-linear combined carrier-phase multipath errors and noise is proposed in this work. The data quality of a CORS station depends on the site environment, monument type and height, and GNSS receiver/antenna. An account of the data quality at some selected stations is given; the main focus of the paper is on the proposed data quality assessment approach.

A GNSS/LiDAR/IMU Pose Estimation System Based on Collaborative Fusion of Factor Map and Filtering

One of the core issues of mobile measurement is the pose estimation of the carrier. The classic Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) integrated navigation scheme has high positioning accuracy but is vulnerable to Global Navigation Satellite System (GNSS) signal occlusion and multipath effect. Simultaneous Localization and Mapping (SLAM) is not affected by signal occlusion, but there are problems such as error accumulation and scene degradation. The multi-sensor fusion scheme combining the two technologies can effectively expand the scene coverage and improve the positioning accuracy and system robustness. However, the current scheme has some limitations. On the one hand, GNSS plays a less important role in most SLAM systems, only for initialization or as a closed-loop factor and other auxiliary work. On the other hand, in the fusion method, most of the current systems only use filtering or graph optimization, without taking into account the advantages of both. Aiming at pose estimation for mobile carriers, this paper combines the advantages of the global optimization of the factor graph and the local optimization of filtering and proposes a GNSS-IMU-LiDAR Constraint Kalman Filter (abbreviated as GIL-CKF), which has the characteristics of full coverage and effectively improving absolute accuracy and high output frequency. The scheme proposed in this paper consists of three parts. Firstly, an extensible factor map is used to fuse the positioning information from different sources, including GNSS, IMU, LiDAR, and a closed-loop map, to maintain a high-precision SLAM system, and the output is used as Multi-Sensor-Fusion-Odometry (MSFO). Then, the scene is divided according to the GNSS quality factor, and a Scene Optimizer (SO) is designed to filter GNSS pose and MSFO. Finally, the results are input into the Extended Kalman Filter (EKF) together with the original IMU data for further optimization and smoothing. The experimental results show that the integration of high-precision GNSS positioning information with IMU, LiDAR, a closed-loop map, and other information through the factor map can effectively suppress error accumulation and improve the overall accuracy of the SLAM system. The SO based on GNSS indicators can fully retain high-precision GNSS positioning information, effectively play their respective advantages of filtering and graph optimization, and alleviate the conflict between global and local optimization. SO with EKF filtering furthers optimization, can improve the output frequency, and smooth the trajectory. GIL-CKF can effectively improve the accuracy and robustness of pose estimation and has obvious advantages in multi-sensor scene complementarity, partial road section accuracy improvement, and high input frequency.

Improving GNSS positioning performance of Android smart devices by a novel pseudorange correction method

To achieve high-precision global navigation satellite system (GNSS) positioning of smart devices, it is necessary to correct the pseudorange measurements, especially to reduce time-related errors. Traditional pseudorange smoothing methods fail to cope with measurement anomalies and do not consider the time correlation. Therefore, a time-correlated pseudorange correction (TCPC) method is proposed. The Mann-Kendall trend test and breakpoint detection are used to address data anomalies and pseudorange errors are estimated with an adaptive smoothing method. Using the proposed method, the GNSS receivers can improve the pseudorange accuracy and availability for better single-point positioning performance. The positioning errors are reduced by 10%–45% for different GNSS receivers. Moreover, the circular antenna motion mechanism is used to reduce the measurement correlation and enhance observability. Using the TCPC method in the smartphone antenna motion case, the improvement rates of positioning accuracies are 43%–76% and the horizontal positioning accuracy is improved from meter level to decimeter level.