Global Navigation Satellite System Technology Research Articles

Improvement of the Estimation of the Vertical Crustal Motion Rate at GNSS Campaign Stations Based on the Information of GNSS Reference Stations

With the enrichment of GNSS data and the improvement in data processing accuracy, GNSS technology has been widely applied in fields such as crustal deformation. The Crustal Movement Observation Network of China (CMONOC) has provided decades of Global Navigation Satellite System (GNSS) data and related data products for crustal deformation research on the Chinese mainland. The coordinate time series of continuously observed reference stations contain abundant information on crustal movements. In contrast, the coordinate time series of periodically observed campaign stations have limited data, making it difficult to separate or remove instantaneous non-tectonic movements from the time series, as performed with reference stations, to obtain a stable and reliable crustal movement velocity field. To address this issue, this paper proposes a method to improve the estimation of crustal movement velocity at campaign stations using the information of neighboring reference stations. This method constructs a Delaunay triangulation of reference stations and fits the periodic movement of each campaign station using an inverse distance weighted interpolation algorithm based on the reference station information. The crustal movement velocity of the campaign stations is then estimated after removing the periodic movement. This method was verified by its application to the estimation of the vertical motion rate at some reference and campaign stations in Yunnan Province. The results show that the accuracy of vertical motion rate estimation for virtual and real campaign stations improved by an average of 24.4% and 9.6%, respectively, demonstrating the effectiveness of the improved method, which can be applied to estimate crustal movement velocity at campaign stations in other areas.

Assessing application errors on sports fields across varying levels of sprayer technology and operator experience

AbstractThis study aimed to quantify misses, overlaps, and oversprays on sports fields using different sprayer technologies across operator experience levels. Conducted from Winter 2022 to Summer 2023 at Veterans Park and Southwood Park in College Station, TX, identical studies were conducted on softball, baseball, and soccer fields. Employing a crossover design, six treatments were administered to three fields at each location, using manual, Global Navigation Satellite System (GNSS), and GNSS + autosteer sprayer technologies. Operators ranged from a highly experienced former sports field manager with nearly 20 years of experience to four inexperienced undergraduate students. Field boundaries were georeferenced for target area determination and treatment applications, using water and a spray volume of 65 gal per acre. ArcMap calculated percentage target area missed, overlapped, and oversprayed based on the actual versus intended volume sprayed. Data were subjected to analysis of variance, and means were separated using Fisher's protected LSD (α = 0.05). Applications by inexperienced operators using manual spray mode typically resulted in the highest rates of percentage target area missed and overlapped across locations and field types. The use of GNSS and autosteer technologies markedly reduced these errors and, consequently, percentage target area oversprayed. This enhancement in the consistency of applications led to a reduction of up to 4.6% in the total volume applied on the softball, baseball, and soccer fields. Therefore, the findings suggest that investing in GNSS‐equipped sprayers with autosteer not only decreases the dependency on experienced operators but also minimizes errors and reduces total volume applied.

CORS station for synergistic monitoring of multivariate surface parameters in expansive soils

Expansive soils cause frequent surface deformation due to their expansion and contraction, which is a serious engineering hazard, and long-term subsidence monitoring is a prerequisite for preventing and controlling expansive soil disasters. Currently, the conventional monitoring methods for the above issue include Interferometric Synthetic Aperture Radar (InSAR) technology, but InSAR is not suitable for uninterrupted monitoring of surface deformation and has low sensitivity. Meanwhile, it can’t obtain multiple surface environmental parameters around the station. The Global Navigation Satellite System (GNSS), a system that can directly acquire surface deformation, has been widely used in landslide disaster monitoring, and in recent years, this technology has also been applied to the field of expansive soil disaster monitoring. At the same time, GNSS can also provide a constant stream of L-band microwave signals to obtain ground environmental information such as precipitable rainfall and soil moisture around the station. In previous studies of expansive soil hazards, GNSS technology has been mainly used to provide surface deformation information without exploring its potential to invert ground environmental information around stations. This paper proposes a ground-based GNSS remote sensing integrated monitoring system that integrates expanding land surface parameters such as “precipitable rainfall, soil moisture, and three-dimensional deformation” and analyses the ability of ground-based GNSS to be used for integrated monitoring of expanding soil hazards by combining ten years of consecutive observational data from GNSS stations along the coastal area of Houston. The experimental results show that the GNSS is capable of providing highly accurate time-series characterization of deformation, and inelastic subsidence in recent years has resulted in a cumulative permanent elevation loss of 2 cm along the Houston coast. The correlation coefficient between soil moisture extracted by the fifth-generation European reanalysis data (ERA5) and soil moisture inverted by ground-based GNSS is 0.514. At the same time, the GNSS was also able to monitor the zenithal precipitable water vapor (PWV) and soil moisture changes around the GNSS station and further analyze the response relationship among the three parameters, which could comprehensively evaluate the stability of expansive soils, avoiding the unreliability of relying on a single piece of monitoring information to assess the stability of expansive soils. We hope to construct a more comprehensive ground-based GNSS remote sensing monitoring system to better monitor expansive soil hazards.

GIS and GNSS Integration Techniques for Earth Observations

Abstract: The term "GNSS," or global navigation satellite system, refers to any satellite navigation system with global coverage. To determine your current location, GNSS devices and receivers employ geolocation information from satellite navigation systems. Trilateration is the basis for how navigation satellites operate. An object's position on the spheroid is defined by its latitude, longitude, and altitude above mean sea level. Today, the utilisation of Global Navigation Satellite Systems (GNSS) technology is required for a wide range of uses, including engineering, cities, and defence. Geographic Information Systems (GIS) can be integrated with GPS to create powerful tools for mapping and spatial analysis. This integration allows for precise display of geographic characteristics on maps, land use planning, response to emergencies, and management of natural resources. Precision agriculture is one area where GNSS and GIS are integrated, enabling farmers to gather precise position data while operating machinery. GNSS and GIS integration can be done through position-focused and technology-focused integration. Position-focused integration uses GNSS technology to locate items or features on the earth's topography, while technology-focused integration enhances the utility and accuracy of GNSS data using GIS applications and technology. Both forms of integration provide potent solutions for various industries.

Innovative landslide disaster monitoring: unmanned aerial vehicle-deployed GNSS technology

Real-time monitoring technologies of surface deformation represented by the global navigation satellite system (GNSS) are essential for landslides early warning. Monitoring methods such as GNSS require on-site manual installation. Therefore, it becomes nearly impossible to deploy surface monitoring equipment when landslides are located in high mountain valleys that are challenging for personnel to access, and/or in hazardous situations. We propose an intelligent real-time monitoring and early-warning technology that employs an unmanned aerial vehicle (UAV) as a carrier to deploy GNSS equipment. A system was initially designed with five components: an adaptive sampling GNSS receiver, an intelligent cooperative network transmission module, UAV-dropped GNSS equipment, special delivery UAV, and an intelligent monitoring and early warning cloud platform. This new technology was applied to a real landslide in Gansu Province, China, in 2020. The mean absolute error of 30 days in the east, north, and upward directions were 1.2, 1.3, and 2.9 mm/d, respectively, comparing the deformation velocity between the traditional monitoring station and the UAV-dropped monitoring station. This observation facilitated the successful prediction of a landslide hazard on 27 January 2021, allowing for a timely alert to be issued. Collectively, the results of this study established that UAV-deployed GNSS technology can accomplish unmanned deployment of GNSS equipment for landslide monitoring to ensure early warning issuance in inaccessible and/or high-risk areas.

Python toolbox for android GNSS raw data to RINEX conversion

Global navigation satellite system (GNSS) data collected from Android devices have gained increasing importance in various applications, ranging from geospatial positioning to environmental monitoring. However, the lack of standardized tools for converting Android GNSS raw data into receiver independent exchange (RINEX) format poses a significant challenge for researchers and practitioners. In response to this need, we present a comprehensive Python toolbox designed to streamline the conversion process and enhance the usability of Android GNSS data. The proposed toolbox leverages Python’s versatility to provide a user-friendly interface for converting Android GNSS raw data into the widely adopted RINEX format. Key features include robust data parsing algorithms, support for multiple GNSS constellations, and compatibility with diverse Android device configurations. Furthermore, the toolbox’s open-source nature encourages community collaboration and allows for continual improvement and adaptation to emerging GNSS technologies. We anticipate that this Python toolbox will serve as a valuable resource for researchers and practitioners working with Android GNSS data, facilitating standardized data interchange and promoting reproducibility in GNSS-based studies.

Accurate Height Determination in Uneven Terrains with Integration of Global Navigation Satellite System Technology and Geometric Levelling: A Case Study in Lebanon

The technology for determining a point’s coordinates on the earth’s surface using the global navigation satellite system (GNSS) is becoming the norm along with ground-based methods. In this case, determining coordinates does not cause any particular difficulties. However, to identify normal heights using this technology with a given accuracy, special research is required. The fact is that satellite determinations of geodetic heights (h) over an ellipsoid surface differ from ground-based measurements of normal height (HN) over a quasi-geoid surface by a certain value called quasi-geoid height or height anomaly (ζ). In relation to determining heights of a certain territory, the concept of geoid height (N) is usually operated when dealing with a geoid model. In this work, geodetic and normal heights are determined for five control points in three different regions in Lebanon, where measurements are carried out using GNSS technology and geometric levelling. The obtained quasi-geoid heights are compared with geoid heights derived from the global Earth model EGM2008. The results obtained showed that, in the absence of gravimetric data, the combination of global Earth model data, geometric levelling for selected areas, and satellite determinations allows for the creation of a highly accurate altitude network for mountainous areas.

GNSS/5G Joint Position Based on Weighted Robust Iterative Kalman Filter

The Global Navigation Satellite System (GNSS) is widely used for its high accuracy, wide coverage, and strong real-time performance. However, limited by the navigation signal mechanism, satellite signals in urban canyons, bridges, tunnels, and other environments are seriously affected by non-line-of-sight and multipath effects, which greatly reduce positioning accuracy and positioning continuity. In order to meet the positioning requirements of human and vehicle navigation in complex environments, it was necessary to carry out this research on the integration of multiple signal sources. The Fifth Generation (5G) signal possesses key attributes, such as low latency, high bandwidth, and substantial capacity. Simultaneously, 5G Base Stations (BSs), serving as a fundamental mobile communication infrastructure, extend their coverage into areas traditionally challenging for GNSS technology, including indoor environments, tunnels, and urban canyons. Based on the actual needs, this paper proposes a system algorithm based on 5G and GNSS joint positioning, aiming at the situation that the User Equipment (UE) only establishes the connection with the 5G base station with the strongest signal. Considering the inherent nonlinear problem of user position and angle measurements in 5G observation, an angle cosine solution is proposed. Furthermore, enhancements to the Sage–Husa Adaptive Kalman Filter (SHAKF) algorithm are introduced to tackle issues related to observation weight distribution and adaptive updates of observation noise in multi-system joint positioning, particularly when there is a lack of prior information. This paper also introduces dual gross error detection adaptive correction of the forgetting factor based on innovation in the iterative Kalman filter to enhance accuracy and robustness. Finally, a series of simulation experiments and semi-physical experiments were conducted. The numerical results show that compared with the traditional method, the angle cosine method reduces the average number of iterations from 9.17 to 3 with higher accuracy, which greatly improves the efficiency of the algorithm. Meanwhile, compared with the standard Extended Kalman Filter (EKF), the proposed algorithm improved 48.66%, 35.17%, and 38.23% at 1σ/2σ/3σ, respectively.

T-SPP: Improving GNSS Single-Point Positioning Performance Using Transformer-Based Correction

GNSS (global navigation satellite systems) technology enables high-precision single-point positioning (SPP) in open environments. However, the accuracy of GNSS positioning is significantly compromised in complex urban canyons due to signal obstructions and non-line-of-sight propagation errors. To address this challenge, we propose a GNSS displacement estimation algorithm. This method learns nonlinear dependencies between GNSS raw measurements and corresponding position changes, capturing dynamic and layered features in GNSS measurement data for displacement estimation. We introduce a denoising auto-encoder (DAE) to preprocess raw GNSS observations, reducing the impact of noise. The model simultaneously outputs estimated displacement and model confidence. The fusion process dynamically combines positioning results from the SPP algorithm and the D-Tran model, adaptively blending them to achieve accurate and optimal positioning estimation. This approach optimizes the accuracy of estimated positioning results while maintaining confidence in the estimation. Experimental results show a 61% reduction in root mean square error (RMSE) and 100% availability in urban canyon environments compared to traditional single-point positioning techniques.

A Systematic Review and Meta-Analysis of Wearable Satellite System Technology for Linear Sprint Profiling: Technological Innovations and Practical Applications.

Cormier, P, Meylan, C, Agar-Newman, D, Geneau, D, Epp-Stobbe, A, Lenetsky, S, and Klimstra, M. A systematic review and meta-analysis of wearable satellite system technology for linear sprint profiling: technological innovations and practical applications. J Strength Cond Res 38(2): 405-418, 2024-An emerging and promising practice is the use of global navigation satellite system (GNSS) technology to profile team-sports athletes in training and competition. Therefore, the purpose of this narrative systematic review with meta-analysis was to evaluate the literature regarding satellite system sensor usage for sprint modeling and to consolidate the findings to evaluate its validity and reliability. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an electronic search of the databases, PubMed and SPORTDiscus (EBSCO), was conducted. Concurrent validity and reliability studies were considered, and 16 studies were retained for the review from the initial 1,485 studies identified. The effects on outcomes were expressed as standardized mean differences (SMDs, Cohen's d ) for each outcome (i.e., maximal sprint speed [MSS], the acceleration constant [τ], maximal theoretical velocity [ V0 ], relative force [ F0 ], and relative power [P max ]). Effect magnitudes represented the SMD between GNSS-derived and criterion-derived (i.e., radar and laser) and resulted in the following estimates: small for MSS ( d = 0.22, 95% CI 0.01 to 0.42), τ ( d = -0.18, 95% CI -0.60 to 0.23), V0 ( d = 0.14, 95% CI -0.08 to 0.36), relative F0 ( d = 0.15, 95% CI -0.25 to 0.55), and relative P max ( d = 0.21, 95% CI -0.16 to 0.58). No publication bias was identified in meta-analyzed studies and moderator analysis revealed that several factors (sampling rate and sensor manufacturer) influenced the results. Heterogeneity between studies was considered moderate to high. This highlighted the differences between studies in sensor technology differences (i.e., sampling rate, sensor fusion, and satellite network acquisition), processing techniques, criterion technology used, sprint protocols, outcome reporting, and athlete characteristics. These findings may be useful in guiding improvements in sprint modeling using GNSS technology and enable more direct comparisons in future research. Implementation of all-out linear sprint efforts with GNSS technology can be integrated into sport-specific sessions for sprint modeling when robust and consistent data processing protocols are performed, which has important implications for fatigue monitoring, program design, systematic testing, and rehabilitation in individual and team sports.

Diurnal variability of atmospheric water vapour, precipitation and cloud top temperature across the global tropics derived from satellite observations and GNSS technique

The diurnal cycle of convection plays an important role in clouds and water vapour distribution across the global tropics. In this study, we utilize integrated moisture derived from the global navigation satellite system (GNSS), satellite precipitation estimates from TRMM and merged infrared dataset to investigate links between variability in tropospheric moisture, clouds development and precipitation at a diurnal time scale. Over 16 years of observations were analysed to estimate the diurnal cycles for 42 sites that span across the global tropics and assess the GNSS precipitable water vapour suitability in tropical moisture variability studies. Results show that the GNSS technique can be successfully used for studies related to the high-resolution temporal variability of integrated atmospheric moisture and, thus, in the analysis of multi-scale interactions and clouds development. For most of the analysed cases, GNSS integrated humidity shows one daily maximum (regardless of the season), which is related to the afternoon precipitation and cloud top temperature daily minimum. Although diurnal integrated moisture variations typically do not exceed 2 mm (usually below 5% of the daily mean value), in favourable large-scale conditions such relatively small changes can lead to the development of convective clouds and can affect precipitation distribution. Thus, GNSS technology allows reliable monitoring of tropospheric moisture variability at a diurnal time scale, which can be leveraged for improved monitoring and prediction of high-impact weather.

Swish Floats: An Inexpensive Neutrally Buoyant Float to Monitor Dispersion in Coastal Seas

Abstract Neutrally buoyant floats have been widely used to measure flows in the ocean, but deploying them in large numbers can be costly and impractical. This is particularly true near coastlines due to the elevated risk of instrument grounding or vessel collisions, resulting in a lack of subsurface Lagrangian measurements in coastal regions. Here, we describe an inexpensive neutrally buoyant satellite-tracked float (named “Swallow-ish,” or “Swish” floats) that has been designed and tested as a cost-effective strategy to measure subsurface dispersion in coastal areas on time scales up to a month. These autonomous instruments are inexpensive, constructed at a material cost of CAD $300 per unit; lightweight, with a mass of 5.4 kg; isopycnal; and constructed from commercially available components, using recently available global navigation satellite system technology to provide the user with a point-to-point measure of subsurface transport. We describe the float design, ballasting techniques, and the governing equations that determine their behavior. Further, through 29 deployments in two coastal seas, we calculate an uncertainty budget and determine a ballasting error of ±1.6 g, corresponding to a local depth targeting error of 16–30 m, analyze the float resurfacing data to calculate subsurface dispersion coefficients, and examine the float depth records to quantify the local internal wave field. Finally, we evaluate surface dispersion using the postresurfacing trajectories. Our findings indicate that Swish floats offer a cost-effective alternative for Lagrangian measurements of subsurface flows in coastal regions.

Advances in satellite atomic clock technologies for the GNSS

Abstract High-precision time frequency lays a foundation for the normal operation and accurate service of the global navigation satellite system (GNSS). The satellite clock, a payload vital to GNSS operation, has become a hot topic in GNSS technology research. This paper gives a systematic introduction to the operation features and principles of satellite clock, and an overview of its research status and current development. On this basis, several techniques that deserve attention of the satellite clocks are categorized in terms of precise orbit determination and clock estimation, performance assessment and characteristic analysis, and clock offset prediction. On this basis, a comprehensive description and analysis is given separately. In the end, this paper gives some prospects over the development trend of techniques of the satellite clock based on the current research achievements and development of the GNSS.

GNSS blasting vibration monitoring and feature recognition method

Aiming at the problems of the current blasting vibration monitoring, such as unstable low-frequency performance, vulnerability to environmental interference, complex operation, and easily damaged, a new blasting vibration monitoring and feature recognition method of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multiscale permutation entropy (MPE) and singular spectrum analysis (SSA) is proposed based on global navigation satellite system (GNSS). Initially, a new method of GNSS blasting vibration monitoring is proposed according to the blasting environment and blasting characteristics, it can provide a new reference for GNSS technology to be used in blasting vibration monitoring. Then, signal simulation proved that the proposed feature recognition method can effectively remove the low-frequency and high-frequency background noise as well as trend items of blasting vibration signals and accurately extract the dominant frequency of vibration compared with the traditional processing methods. Finally, the proposed method is demonstrated by factory open blasting and tunnel excavation blasting compared with the monitoring results of the conventional blasting recorder, it can accurately recognize the characteristics of blasting initiation time, particle peak velocity (PPV), initiation period, and dominant frequency of vibration. The RMSE of PPVs is 0.086, and other errors are within 5%, which is highly consistent with the monitoring results of blasting recorder. The GNSS blasting vibration monitoring and feature recognition method can improve the low-frequency stability, field adaptability, simple operation, and information richness of blasting monitoring, which can provide a new approach for blasting monitoring; it can also be an important supplement to existing blasting monitoring methods.

Performance analysis of frequency-mixed PPP-RTK using low-cost GNSS chipset with different antenna configurations

Low-cost Global Navigation Satellite System (GNSS) devices offer a cost-effective alternative to traditional GNSS systems, making GNSS technology accessible to a wider range of applications. Nevertheless, low-cost GNSS devices often face the challenges in effectively capturing and tracking satellite signals, which leads to losing the observations at certain frequencies. Moreover, the observation peculiarities of low-cost devices are in contradistinction to those of traditional geodetic GNSS receivers. In this contribution, a low-cost PPP-RTK model that considers the unique characteristics of different types of measurements is developed and its performance is fully evaluated with u-blox F9P receivers equipped with three distinctive antenna configurations: vertical dipole, microstrip patch, and helix antennas. Several static and kinematic experiments in different scenarios are conducted to verify the effectiveness of the proposed method. The results indicate that the mixed-frequency PPP-RTK model outperforms the traditional dual-frequency one with higher positioning accuracy and fixing percentage. Among the three low-cost antennas tested, the vertical dipole antenna demonstrates the best performance under static conditions and shows a comparable performance as geodetic antennas with a positioning accuracy of 0.02 m, 0.01 m and 0.07 m in the east, north, and up components, respectively. Under low-speed kinematic scenarios, the helix antenna outperforms the other two with a positioning accuracy of (0.07 m, 0.07 m, 0.34 m). Furthermore, the helix antenna is also proved to be the best choice for vehicle navigation with an ambiguity fixing rate of over 95% and a positioning accuracy of (0.13 m, 0.14 m, 0.36 m).

A Comprehensive Review of the GNSS with IoT Applications and Their Use Cases with Special Emphasis on Machine Learning and Deep Learning Models

This paper presents a comprehensive review of the Global Navigation Satellite System (GNSS) with Internet of Things (IoT) applications and their use cases with special emphasis on Machine learning (ML) and Deep Learning (DL) models. Various factors like the availability of a huge amount of GNSS data due to the increasing number of interconnected devices having low-cost data storage and low-power processing technologies - which is majorly due to the evolution of IoT - have accelerated the use of machine learning and deep learning based algorithms in the GNSS community. IoT and GNSS technology can track almost any item possible. Smart cities are being developed with the use of GNSS and IoT. This survey paper primarily reviews several machine learning and deep learning algorithms and solutions applied to various GNSS use cases that are especially helpful in providing accurate and seamless navigation solutions in urban areas. Multipath, signal outages with less satellite visibility, and lost communication links are major challenges that hinder the navigation process in crowded areas like cities and dense forests. The advantages and disadvantages of using machine learning techniques are also highlighted along with their potential applications with GNSS and IoT.

THE FEATURES OF AERIAL PHOTOGRAPHY FOR ROAD REPAIR OR RECONSTRUCTION

This study formulates some of the actual problems that arise during engineering and geodetic survey for designing road repair and reconstruction. An analysis of the regulatory requirements currently in force in Ukraine for carrying out this type of work has been carried out, it has also been concluded that at present they do not keep up with the latest technologies. Studied publications describing the use of unmanned aerial vehicles (UAV). Classified navigation satellite receivers are installed on UAVs, for the purpose of determining coordinates. Particular attention is paid to the installation on the carrier of geodetic class navigation equipment using kinematic GNSS (Global Navigation Satellite System) technologies, such as RTK (Real-Time Kinematic) and PPK (Post-Processing Kinematic). These technologies allow us to determine the spatial coordinates of the centers of photos at the time of photographing with centimeter accuracy, which greatly simplifies the adjustment of digital photogrammetric models and increases their accuracy. But the most important advantage is a significant reduction in field surveying, which makes them less expensive and more efficient overall. In the course of the study, it was found that in conditions of limited access to nearby territories, insufficient stability of the results of determining the centers of images can affect the final quality of products. For cases where the maximum achievable accuracy is at the limit of the possibility of photogrammetric technology, the removal of the most critical elements is proposed to be combined by combining aerial photography technologies with ground technologies. The road’s hard surface surveying can be performed by ground geodetic methods (tacheometric or GNSS), and by modern laser scanning. At the same time, the adjacent territory, regarding the shooting of elements of which less stringent requirements are established, can be worked out by aerial photography. In further studies, it is necessary to determine the conditions for achieving the maximum accuracy of UAV photogrammetry. Keywords: aerial photography, photogrammetry, geodetic survey, reconstruction of highways.

Borneo triangulation 1968: a review and the way forward

Triangulation is a classical geodetic practice to establish survey control networks by using measurements of angles and some spare distances. In this way, a series of reference points or simply called ‘trigs’ is spreading over the country. This paper aims to review geodetic survey work that was carried out for the East Malaysia and Brunei from 1918 to 1968 which is covering about 204,119 square kilometres'. However, this classical geodetic datum is not directly compatible with the current surveying trends based on Global Navigation Satellite System technologies. Therefore, in this review covered concepts and history of triangulation systems, datums and the status of transformation plan to modernise geodetic datum for Sarawak.

GNSS Technology for Precise Positioning in CCAM: A Comparative Evaluation of Services

Cooperative, connected, and automated mobility (CCAM) can lead to a significantly improved transport system by increasing safety and efficiency, and reducing emissions. To achieve the goal of fully automated mobility and self-driving vehicles, accurate and reliable positioning is essential. Positioning methods in CCAM often use sensor fusion combining data from multiple sensors with Global navigation satellite system (GNSS) positioning data. In this paper, we focus on the status of GNSS technology by investigating position accuracies and integrities of different state-of-the-art GNSS technologies. We conduct field tests using a self-driving vehicle in Drammen, Norway. Three different types of GNSS positioning services are explored, and a reference trajectory delivered by the vehicle’s navigation system is used to determine the performance of each service. We show that the performance of the GNSS methods alone does not fulfill the requirements needed to obtain fully automated mobility. Moreover, we observe a general decreasing trend in GNSS accuracy for more challenging surroundings.

Real-Time Single-Frequency Precise Point Positioning for Connected Autonomous Vehicles: A Case Study over Brazilian Territory

Intelligent Transportation Systems (ITS) and Connected Autonomous Vehicles (CAV) are extremely active areas of research, which have attracted the attention of users, companies, and development agencies worldwide. The Global Navigation Satellite System (GNSS) is a main enabling technology, as ITS/CAV applications are fundamentally dependent on precise positioning systems. Among the recent GNSS technology advances that have allowed high accuracy to be achieved is Real-Time Precise Point Positioning (RT-PPP). Even though the Real-Time Service (RTS) of the International GNSS Service (IGS) provides various correction streams for RT-PPP deployment, some of them (e.g., those associated with ionospheric delays) still have limited accuracy. This has motivated local organizations and research institutes to deploy their own RT-PPP products, based on a denser regional network of GNSS Continuously Operating Reference Stations (CORS). The purpose of this work is to evaluate the performance of a recently established regional ionosphere RT-PPP product, from the Argentine University of La Plata (UNLP), which serves all of South America, with extensions to the Caribbean and Antarctica Peninsula. The main contributions of the work are: we show that RT-PPP using UNLP products outperforms RT-PPP using products exclusively from IGS-RTS; we benchmark positioning strategies, such as Post-Processed PPP (PP-PPP) and Relative Global Positioning System (RGPS); and, demonstrate the ability of Brazilian RT-PPP users employing Single-Frequency (SF) GPS code measurements and UNLP ionosphere products to achieve the CAV lane-level specification (i.e., 1-meter horizontal positioning accuracy at 95% probability). The article includes results from both static and moving experimental tests.