Accuracy Of 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.

Preliminary insights on fast GNSS signal capture using SFT and FFT frequency shift

Global Navigation Satellite Systems (GNSS) are the cornerstone of modern navigation and positioning technology. In this paper, we introduce an innovative approach to GNSS signal acquisition, specifically designed to enhance the performance and speed of the signal lock-on process with real-world applications in mind. Our proposed algorithm leverages advanced mathematical tools, including Fast Fourier Transform (FFT) and Inverse FFT, to streamline the acquisition process. Notably, it eliminates the need for an Intermediate Frequency (IF) down mixer, simplifying hardware and potentially reducing power consumption in GNSS receivers. One key breakthrough is using the FFT's "circle shift" to handle Doppler frequency offsets resulting from relative satellite-receiver motion. This method significantly reduces the computational burden during Doppler frequency search, ensuring a faster and more efficient lock-on to satellite signals. Furthermore, we exploit the sparse nature of GNSS signals in the reverse FFT calculation, utilizing a Sparse Fourier Transform (SFT) for efficient processing. This innovation allows quicker reverse FFT computations, pivotal in the acquisition process. Through extensive simulations, we demonstrate the superior performance of our improved algorithm. Its speed and reliability make it well-suited for many practical GNSS applications, such as vehicle navigation, precision agriculture, surveying, and geolocation services. By enhancing acquisition efficiency, our approach contributes to more accurate, responsive, and energy-efficient GNSS positioning, benefiting users in both civilian and industrial sectors.

GNSS/IMU/map-matching feedback integration with adaptive GNSS accuracy estimation by using low-quality sensors for vehicle localization in urban canyon

In urban canyons, the performance of vehicle navigation systems based on Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU) is notably compromised due to satellite signal obstructions and severe multipath effects. This article proposes a GNSS/IMU/Map-Matching feedback (MMF) integration with the adaptive GNSS accuracy estimation to provide accurate and robust positions for vehicles in urban canyons. This system integrates self-constraints and MMF to control the drift of low- quality IMU. To avoid fatal positioning errors caused by incorrect MMF, motion states are categorized into straight-line driving and corner turning, with different criteria set based on their characteristics to validate MMF. Furthermore, GNSS accuracy is estimated adaptively with MMF. Extensive experiments in Hong Kong’s dense urban canyons demonstrated significantly improved positioning accuracy of 37% and 51% in the north and east directions, respectively, outperforming traditional GNSS/IMU/Map configurations. The results demonstrate the proposed approach’s marked enhancement in urban vehicular navigation.

Navigation performance analysis of Earth–Moon spacecraft using GNSS, INS, and star tracker

Global Navigation Satellite System (GNSS) can provide an approach for spacecraft autonomous navigation in earth–moon space to make up for the insufficiency of earth-based tracking, telemetry, and control systems. However, its weak power and poor observation geometry near the moon causes new problems. After the GNSS signal characteristics and satellite visibility were evaluated in Phasing Orbit and Lunar Transfer Orbit, we proposed an adaptive Kalman filter based on the Carrier-to-Noise ratio (C/N0) and innovation vector to weaken the influence of GNSS accuracy attenuation as much as possible. The experimental results show that the spacecraft position and velocity accuracy are better than 10 m and 0.1 m/s near the Earth, and better than 50 m and approximately 0.2 m/s near the moon use GNSS with the proposed adaptive algorithms. Additionally, because of the deterioration of navigation performance based on the orbit filter during orbital maneuvering, we used accelerometer data to compensate for the dynamic model to maintain navigation performance. The results of the experiment provide a reference for subsequent studies.

Improving Target Geolocation Accuracy with Multi-View Aerial Images in Long-Range Oblique Photography

Target geolocation in long-range oblique photography (LOROP) is a challenging study due to the fact that measurement errors become more evident with increasing shooting distance, significantly affecting the calculation results. This paper introduces a novel high-accuracy target geolocation method based on multi-view observations. Unlike the usual target geolocation methods, which heavily depend on the accuracy of GNSS (Global Navigation Satellite System) and INS (Inertial Navigation System), the proposed method overcomes these limitations and demonstrates an enhanced effectiveness by utilizing multiple aerial images captured at different locations without any additional supplementary information. In order to achieve this goal, camera optimization is performed to minimize the errors measured by GNSS and INS sensors. We first use feature matching between the images to acquire the matched keypoints, which determines the pixel coordinates of the landmarks in different images. A map-building process is then performed to obtain the spatial positions of these landmarks. With the initial guesses of landmarks, bundle adjustment is used to optimize the camera parameters and the spatial positions of the landmarks. After the camera optimization, a geolocation method based on line-of-sight (LOS) is used to calculate the target geolocation based on the optimized camera parameters. The proposed method is validated through simulation and an experiment utilizing unmanned aerial vehicle (UAV) images, demonstrating its efficiency, robustness, and ability to achieve high-accuracy target geolocation.

A comparative analysis of the performance of various GNSS positioning concepts dedicated to precision agriculture

Abstract Automated guidance systems for precision agriculture rely on Global Navigation Satellite Systems (GNSS) and correction services for high accuracy and precision in field operations. This study evaluates the performance of selected GNSS positioning services for precision agriculture in a field experiment. We use three correction services: SF1, SF3, and RTK, which apply varying positioning concepts, i.e., Wide Area Differential GNSS, Precise Point Positioning, and Real-Time Kinematics, respectively. The tractor is autonomously steered along multiple predefined paths located in open-sky areas as well as near the heavy tree cover. The reference route of the vehicle is determined by classical surveying. Tractor trajectories, a SF1 and SF3 corrections, are shifted from predefined straight paths, unlike in the case for RTK. Offsets of up to several decimeters are service- and area-specific, indicating an issue with the stability of the reference frame. Additionally, the varying performance of the correction services implies that environmental conditions limit the precision and accuracy of GNSS positioning in precision agriculture. The pass-to-pass analysis reveals that SF1 improves the declared accuracy, while SF3 is less reliable in obstructed areas. RTK remains a stable source for determining position. Under favorable conditions, the pass-to-pass accuracy at 95% confidence level is better than 11.5 cm, 8.5 cm, and 4.5 cm for SF1, SF3, and RTK, respectively. In the worst-case scenario, the corresponding accuracies are: 25.5 cm, 65.5 cm, and 22.5 cm.

Short-Term Performance Evaluation of Real-Time Kinematic Global Navigation Satellite System Receivers in Unmanned Aircraft Systems

HighlightsAn evaluation of relative GNSS receiver accuracy was performed using a UAS-based GNSS accuracy testing system.RTK receivers displayed minimal mean error and consistent standard deviations across deployment strategies and error directions.Non-RTK receivers exhibited significantly greater mean error and variability across deployment strategies, particularly in elevation error.UAS deployment strategy did not have a meaningful impact on RTK receiver positioning performance.Abstract. Global navigation satellite system (GNSS) receivers commonly integrated into small unmanned aircraft systems (UAS) generally function in standard or differential fix configurations, providing horizontal and vertical accuracies of approximately ±5 meters and ±15 meters, respectively. The accuracy of GNSS positioning is an important factor with widespread implications, affecting domains such as precision agriculture, meteorology, and photogrammetry. In the context of atmospheric observations, spatial accuracy plays a critical role, particularly in applications related to barometric pressure and precipitable water vapor. Similarly, UAS-based photogrammetry applications rely on high geospatial precision for tasks including topographic mapping and environmental monitoring. As such, this study aimed to improve our understanding of GNSS positioning accuracy in UAS-based observations. The main objectives included (1) deploying a UAS-based GNSS accuracy testing system and (2) evaluating the static and dynamic short-term accuracy of L1 and L1/L2 GNSS receivers in RTK and non-RTK fix modes. Results indicated significant differences across receiver configurations and deployment strategies. RTK receivers displayed minimal mean error and consistent standard deviations, while non-RTK receivers exhibited greater mean error and variability, especially in elevation. Though the study did not conclusively confirm a consistent reduction in accuracy due to UAS deployment, findings suggest that RTK receivers substantially enhance accuracy by reducing position measurement error by two orders of magnitude (1–6 cm for RTK; 50–315 cm for non-RTK), thereby mitigating measurement variability attributable to timing or deployment strategy. In conclusion, this research contributed insights into GNSS accuracy for UAS-based observations and underscored the importance of considering receiver configurations and deployment strategies for position measurement during atmospheric and photogrammetric observations. Keywords: Global navigation satellite systems (GNSS), Meteorology, Photogrammetry, Positioning accuracy, Precision agriculture, Real-time kinematic (RTK), Unmanned aircraft systems (UAS).

A Method for Evaluating Global Navigation Satellite System Position Accuracy in Small Unmanned Aircraft Systems

Highlights A novel UAS-based GNSS testing system was developed to assess both horizontal and vertical accuracy components. Recommendations are provided for further standard development related to UAS GNSS testing protocols. Future research can utilize this framework to enhance understanding of UAS GNSS accuracy. Abstract. Current standards for Global Navigation Satellite Systems (GNSS) testing are primarily designed for assessing horizontal positioning accuracy in ground-based applications. However, there is a notable absence of guidelines specifically addressing vertical accuracy assessment and applications that extend beyond ground-based scenarios, including those involving small unmanned aircraft systems (UAS). This effort aimed to develop a UAS-based GNSS testing system, tailored for the evaluation of GNSS receiver performance in dynamic low-altitude scenarios. The main objectives included (1) designing a GNSS testing system for UAS applications and (2) validating the system through field testing. The system development consisted of GNSS receiver configuration, data acquisition system design, processing procedures, and UAS payload and base station component design. The UAS payload featured multiple GNSS receivers operating in different modes, and data were collected using a custom Python script on a Raspberry Pi computer. For in-field validation, dynamic flight testing with vertical profiles was conducted. A robotic tracking total station (TTS) was used as the reference instrument for measuring the location of the UAS payload. The findings underscore the system's capabilities, primarily emphasizing its functionality rather than conducting an extensive assessment of the individual GNSS receiver performance. The UAS-based testing system that was developed was capable of satisfying most of the criteria for existing GNSS test standards. The insights derived from this process offer recommendations for standardizing UAS-based GNSS testing methods, with a particular focus on mission design, battery management, data processing, and environmental adaptability. Keywords: Dynamic Error, Global Navigation Satellite Systems (GNSS), Positioning Accuracy, Tracking Total Station (TTS), Unmanned Aircraft Systems (UAS).

Testing Galileo High-Accuracy Service (HAS) in Marine Operations

Global Navigation Satellite System (GNSS) technology supports all phases of maritime navigation and serves as an integral component of the Automatic Identification System (AIS) and, by extension, Vessel Traffic Service (VTS) systems. However, the accuracy of standalone GNSS is often insufficient for specific operations. To address this limitation, various regional and local-area solutions have been developed, such as Differential GNSS (DGNSS), Satellite Based Augmentation Service (SBAS) and Real Time Kinematic (RTK) techniques. A notable development in this field is the recent introduction of the Galileo High-Accuracy Service (HAS), which saw its initial service declared operational by the European Commission (EC) on 24 January 2023. Galileo HAS provides high-accuracy Precise Point Positioning (PPP) corrections (orbits, clocks and signal biases) for Galileo and GPS, enhancing real-time positioning performance at no additional cost to users. This article presents the results of the first Galileo HAS testing campaign conducted at sea using a buoy-laying vessel temporarily equipped with a Galileo HAS User Terminal. The results presented in this Article include accuracy and position availability performance achieved using the Galileo HAS User Terminal. The article also highlights challenges posed by high-power radio-frequency interference, which likely originated from the Long-Range Identification and Tracking (LRIT) system antenna on board the vessel. Furthermore, the article provides additional assessments for different phases of navigation, demonstrating better performance in slow-motion scenarios, particularly relevant to mooring and pilotage applications. In these scenarios, values for horizontal accuracy reached 0.22 m 95% and 0.13 m 68% after removing interference periods. These results are in line with the expectations outlined in the Galileo HAS Service Definition Document (SDD).

A Probabilistic Method-Based Smartphone GNSS Fault Detection and Exclusion System Utilizing PDR Step Length

A smartphone equipped with a Global Navigation Satellite System (GNSS) module can generate positional information for location-based services. However, GNSS signals are susceptible to fragility, multipath (MP), and Non-Line-Of-Sight (NLOS) interference, which can lead to a degradation in the accuracy of GNSS positioning on smartphones. Due to limitations in the smartphone’s antenna, GNSS signal strength is typically lower. Moreover, in urban areas, where smartphones rely on GNSS, MP and NLOS signals are the primary factors impeding accurate positioning. In this paper, with the goal of enhancing both the accuracy and robustness of smartphone GNSS positioning, we propose two methods. Firstly, an optimized particle filter method employing a Krill Herd Algorithm (KHA) is suggested for the integration of GNSS and Pedestrian Dead Reckoning (PDR). Secondly, a probabilistic approach is presented to identify faulty GNSS measurements using step distance information obtained from the PDR. Experimental tests were conducted using smartphones to evaluate the performance of the proposed method. The results demonstrate that both the KHA and fault detection methods effectively enhance the performance of integrated PDR and GNSS.

Evaluation of receiver calibration based on clock-steering for time–frequency transfer

Calibrating the global navigation satellite system (GNSS) time–frequency transfer system is crucial for accurate GNSS time–frequency transfer. The most commonly used relative calibration method relies on the common-clock to eliminate clock differences. However, it cannot eliminate the changes in clock signal reference point delay (REFDLY). The changes in REFDLY during calibration could not be ignored, especially for ordinary users in an unstable environment. To address this issue, we use the GNSS real-time single-difference time synchronization calibration based on clock-steering, which can detect the change in REFDLY. According to this method, the clock differences and REFDLY are eliminated simultaneously. We installed two GNSS antennas on the roof of the Xinghu Building of Wuhan University and designed a clock synchronization terminal to evaluate the performance of this method. The data were collected on Day of Year 152–156, 167–171, 222–226, 231–235, 244, 258, 260, and 262 in 2022. In accordance with the calibration results, the standard deviation better than 100 ps, and the stability reaches 10−15@10000s . Also, the time variance better than 60 ps, and the uncertainty uCAL better than 0.5 ns. Compared to the common-clock calibration, one of the most significant benefits is the elimination of REFDLY, which leads to decreased uncertainty and better stability over both the short-term and long-term.

Precise GNSS Time Synchronization With Experimental Validation in Vehicular Networks

Time synchronization utilizing the Global Navigation Satellite System (GNSS) is being increasingly investigated for vehicular networks. Due to GNSS signal blockages, the availability and accuracy of GNSS timing solutions in various road settings is a recognized challenge. With the recent improvement of Multi-GNSS technology and the increased capacity of consumer-grade receivers, the application of GNSS in vehicular environments has brightened up. This paper systematically analyzes the required time synchronization of vehicular networks and presents a GNSS-based time synchronization solution. It also experimentally demonstrates the availability and capabilities of GNSS time synchronization using commercial-grade GNSS receivers and off-the-shelf communication devices. Our experiments show that the timing accuracy of an individual vehicular node can be as good as ±2 microseconds, resulting in synchronization accuracy of sub-10 microseconds among nodes. A momentary complete outage of the GNSS time solution due to signal blockage on the road adds clock error, leading to synchronization inaccuracy of up to sub-20 microseconds. This level of inaccuracy still meets the desired requirement for most applications in vehicular communication.

GNSS Surface Occlusion Model Analysis Based on Different Fitting Functions

Accuracy and reliability are determining factors when selecting where to place a global navigation satellite system (GNSS) receiver for landslide monitoring and early warning in real time. Previous studies have revealed that surface occlusion models (SOMs) are beneficial to reduce the multipath error of satellite observation when the satellite line of sight is near the ground and improve the accuracy and reliability of GNSS monitoring. This paper investigates fitting functions to determine appropriate SOMs for GNSS stations located in different spatial environments. Based on their similarity to terrain patterns, Gaussian, Fourier, and sum of sine functions were chosen to build SOMs for the rover sites with different occlusions; the performance of fitting accuracy and application on the positioning were analyzed. The results indicate that the three functions present a similar performance considering the goodness of fit, but the Fourier and sum of sine functions can significantly improve the efficacy of SOMs and enhance performance in positioning for landslide monitoring. Therefore, these two functions can effectively support accurate and reliable GNSS landslide monitoring in real time.

Accurate Maps of Reef-Scale Bathymetry with Synchronized Underwater Cameras and GNSS

We investigate the utility of towed underwater camera systems with tightly coupled Global Navigation Satellite System (GNSS) positions to provide reef-scale bathymetric models with millimeter to centimeter resolutions and accuracies with Structure-from-Motion (SfM) photogrammetry. Successful development of these techniques would allow for detailed assessments of benthic conditions, including the accretion and erosion of reefs and adjacent sediment deposits, without the need for ground control points. We use a multi-camera system towed by a small vessel to map over 70,000 m2 of complex shallow (2–8 m water depth) bedrock reef, boulder fields, and fine (sand and gravel) sediments of Lake Tahoe, California. We find that multiple synchronized cameras increase overall mapping coverage and allow for wider survey line spacing. The accuracy of the techniques was sub-millimeter for local length measurements less than a meter, and the bathymetric reproducibility was found to scale with the accuracy of GNSS (3–5 cm), although this could be improved to sub-centimeter with the inclusion of one or more co-registered, but unsurveyed, control points. For future applications, we provide guidance on conducting field operations, correcting underwater image color, and optimizing the SfM workflows. We conclude that a GNSS-coupled underwater camera array is a promising technique to map shallow reefs at high accuracy and resolution without ground control.

A Novel Optimal Robust Adaptive Scheme for Accurate GNSS RTK/INS Tightly Coupled Integration in Urban Environments

Modern navigation systems are inseparable from an integrated solution consisting of a global navigation satellite system (GNSS) and an inertial navigation system (INS) since they serve as an important cornerstone of national comprehensive positioning, navigation, and timing (PNT) technology and can provide position, velocity, and attitude information at higher accuracy and better reliability. A robust adaptive method utilizes the observation information of both systems to optimize the filtering system, overcoming the shortcomings of the Kalman filter (KF) in complex urban environments. We propose a novel robust adaptive scheme based on a multi-condition decision model suitable for tightly coupled real-time kinematic (RTK)/INS architecture, which can reasonably determine whether the filtering system performs robust estimation (TCRKF) or adaptive filtering (TCAKF), improving the robust estimation method of two factors considering ambiguity variance for RTK-related observations. The performance of the proposed robust adaptive algorithm was evaluated through two sets of real vehicle tests. Compared with the TCAKF and TCRKF algorithms, the new robust adaptive scheme improves the average three-dimensional (3D) position root mean square (RMS) by 31% and 18.88%, respectively. It provides better accuracy and reliability for position, velocity, and attitude simultaneously.

Assessing the influence of differential code bias and satellite geometry on GNSS ambiguity resolution through MANS-PPP software package

Abstract Ambiguity resolution (AR) is essential for quick and accurate Global Navigation Satellite System GNSS location and navigation. In addition to location parameters, there are various additional GNSS characteristics that are relevant for a wide range of applications such as instrumental calibrations, atmospheric sounding, and time transfer. We offer differential code bias and satellite geometry for the GNSS estimable parameters using MANS-PPP software backage. In this research, we used the MANS-PPP software package to execute the processing method and generate the PPP GNSS solution. We demonstrated how differential code bias and satellite geometry can effectively enhance initial time and positioning error for multi-GNSS satellites. PPP Processing observation data in static mode was used by the different DCB files the Chinese Academy of Sciences (CAS), the German Aerospace Centre (DLR), and the Centre for Orbit Determination in Europe (CODE), for the 12 stations from IGS, and we analyzed the impact of errors from the satellite geometry. The results illustration that the correction of DCB significantly improves the PPP ambiguity resolution success rate and quality, which have higher DCB values. The satellite geometry also has a substantial influence on the PPP ambiguity resolution, with a better geometry leading to a higher success rate and quality. Furthermore, the use of multiple GNSS constellations and the optimization of the satellite selection and weighting algorithms can further improve the PPP ambiguity resolution and the resulting positioning accuracy.

Can GNSS Derived Height Replace Levelling Height? - A Case of Low-Land of Nepal

Orthometric height is the generally adopted type of height worldwide and in geomatics community. Precise levelling has been the method of obtaining orthometric height in past for most of the country, so as the Nepal. However, due to wide usage of Global Navigation Satellite System (GNSS), the alternative approach of combining GNSS derived ellipsoidal height with geoid undulation to get GNSS derived orthometric height, has been used extensively. In Nepal, this technique was officially adopted in 2020 for Everest height measurement and understood as the efficient way to comply with levelling height. In this study, GNSS surveying was conducted on 15 stations located at the lowland region of Nepal and orthometric heights were obtained from GNSS and geoid method. When compared GNSS derived orthometric height with precise levelling height, the difference remained within threshold of 5cm for majority of observation stations. However, these differences are not sufficient to support the standards set for the third order levelling by Survey Department (SD). The accuracy of GNSS derived orthometric height can be significantly affected by various environment and existing resources such as existing accuracy of geoid, nature of precise levelling height. Considering the revisit upon these conditions, we expect GNSS-levelling as a strong alternative to time consuming, tedious, and costly precise levelling which is most suitable method of obtaining orthometric height in lowland topography at a precision less than 4 cm.

V-SLAM ENHANCED INS/GNSS FUSION SCHEME FOR LANE LEVEL VEHICULAR NAVIGATION APPLICATIONS IN DYNAMIC ENVIRONMENT

Abstract. With the development of different sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), LiDAR, radar and camera, more localization information is available for autonomous vehicular applications. However, each sensor has its limitations in different circumstances. For example, visual Simultaneous Localization and Mapping (SLAM) easily loses tracking in an open sky area where accurate GNSS measurements can be obtained. Sensors can complement each other by integrated their information in a multi-sensor fusion scheme. In this study, we proposed a visual-SLAM enhanced INS/GNSS localization fusion scheme for a high dynamic environment. Oriented FAST and rotated BRIEF (ORB) SLAM are used to pre-process image sequences from monocular camera, rescaled and refreshed after applying GNSS measurements, and convert to position and velocity information, which can provide updates to the system. The performance of the fusion system was verified through two field tests at different speed ranges (about 30–60 km/s), using a reliable reference system as ground-truth to assess the accuracy of the proposed localization fusion scheme. The results indicated that the proposed system could improve the navigation accuracy compared to INS/GNSS integration scheme and achieve which-lane level or even where-in-lane level.

Using Radio Technical Commission for Maritime Services Corrections in a Consumer-Grade Lane-Level Positioning System for Connected Vehicles

<div>Connected vehicle (CV) technology has the potential to greatly improve the safety, mobility, and environmental sustainability of traffic. Many CV applications require the vehicle position as input, which is primarily provided by global navigation satellite systems (GNSS). Although a large number of those applications (e.g., Intersection Movement Assist) require vehicle positioning to have lane-level accuracy, it has been shown that the type of positioning system typically used by CVs currently cannot provide consistent lane-level accuracy, even under open-sky conditions. In order to address this gap, we have evaluated an enhanced positioning system that adds little, if any, to the cost of the CV. It consists of a single-frequency real-time kinematic (RTK)-capable GNSS receiver onboard the vehicle, which utilizes Radio Technical Commission for Maritime Services (RTCM) differential corrections transmitted over dedicated short-range communications (DSRC) by the roadside infrastructure. Tests on a moving vehicle show that this system could provide lane-level accuracy over 95% of the time in open-sky conditions. These tests also show the DSRC to be an effective means of disseminating RTCM corrections, given the intersection spacings and communication ranges in the test. However, neither RTCM nor the more commonly used space-based augmentation system (SBAS) differential corrections appeared to improve the positioning accuracy of GNSS in urban canyons.</div>

Measurements and Modeling of the Responses of VLF Transmitter Signals to X‐Class Solar Flares at the Great Wall Station in Antarctica

AbstractSolar flares can severely disturb the Earth's ionosphere, leading to changes of atmospheric chemistry and degradation of Global Navigation Satellite System (GNSS) signals. The key to better assess these effects is to more accurately quantify the electron density enhancements induced by solar flares, which is still uncertain. Thanks to the good coverage and quiet electromagnetic environment provided by the Great Wall Station (GWS, 62.22°S, 58.96°W) in Antarctica, our Very‐Low‐Frequency (VLF) receiver has recorded clean VLF signatures produced by three X‐class solar flares from March to May in 2022. These measurements motivate us to revisit the uncertain question related to the ionospheric effects induced by solar flares. Different from previous studies, we model solar flare events from satellite measurements of X‐ray fluxes to ground‐based measurements of VLF transmitter signals by combining a flare irradiance model, an X‐ray propagation model, a D‐region chemistry model, and a VLF propagation model. For eight transmitting frequencies and propagation paths, modeling results can satisfactorily explain our VLF measurements at GWS during the three X‐class flares, especially so for the X1.3 event occurring on 30 March 2022. Moreover, the electron density results that reconcile space‐ and ground‐based measurements suggest that the VLF reflection height can be lowered to ∼66 km by an X1.1 flare, in line with previous studies. The present set of numerical models can be thus utilized in future studies to improve the nowcasting capability of VLF sensing technique for monitoring solar flares, as well as the accuracy of GNSS navigation/positioning during solar flares.