Performance indicator development addressing mitigation of the space weather impacts on GNSS

The GNSS NavAer project has been under development since 2017, focusing on creating a comprehensive infrastructure dedicated to monitoring and analyzing space weather phenomena. This project represents a significant investment in scientific research and technology, aimed at enhancing our understanding of various atmospheric and environmental processes. The infrastructure established through the GNSS NavAer project is versatile and can be deployed across several scientific disciplines, including Geodesy, Aeronomy, and Space Weather, among others.One of the primary motivations behind the GNSS NavAer project is the increasing reliance on Global Navigation Satellite Systems (GNSS) for navigation, communication, and positioning applications. As these systems become more integral to modern society, understanding the factors that can disrupt their performance is of great insterest. Space weather events, particularly those affecting the ionosphere, have been identified as significant sources of interference that can degrade the accuracy and reliability of GNSS signals.Ionospheric scintillation, a phenomenon characterized by rapid amplitude and phase fluctuations of GNSS signals, poses a particular challenge. This can result from natural irregularities in the ionosphere, often intensified by solar activity. When these irregularities are present, GNSS signals can experience disruptions that may lead to positioning errors, loss of signal lock, and degraded navigation accuracy. Such disruptions can have serious implications for air navigation, autonomous systems, and any application that relies on precise positioning data.In this presentation, we will focus on the specific use of the GNSS NavAer infrastructure for monitoring space weather, with an emphasis on IS. The infrastructure includes a network of strategically placed GNSS receivers that continuously collect real-time data on ionospheric conditions, which are stored in a databank and can be explored via a dedicated toll specially developed for that (ISMR Query Tool). By integrating advanced data processing techniques and algorithms, researchers can analyze these data to identify and characterize scintillation events as they occur.The significance of this monitoring cannot be overstated. By providing timely information on ionospheric conditions, stakeholders can better prepare for and mitigate the impacts of space weather on GNSS applications. For instance, real-time observations can help aviation authorities make informed decisions regarding flight operations, ensuring that aircraft can navigate safely even in conditions that would typically be challenging due to ionospheric disturbances.Furthermore, the GNSS NavAer project facilitates collaborative efforts among scientists and researchers in various fields. By sharing data and findings, the project promotes a more comprehensive understanding of space weather and its impacts. International cooperation is vital, as space weather events often affect multiple regions simultaneously, making a collaborative approach essential for effective monitoring and response strategies.In conclusion, the GNSS NavAer project is a groundbreaking initiative aimed at enhancing our ability to monitor and understand the complexities of space weather. Through its dedicated infrastructure, it addresses the challenges posed by IS and aims to provide solutions that improve the reliability of GNSS applications. The deterioration in positioning and navigation will be presented, together to some trial to reduce such problem.

Recurrence plot analysis of GPS ionospheric delay time series in extreme ionospheric conditions

Currently Global Navigation Satellite Systems (GNSS) are the primary source for the determination of absolute position, velocity and time (PVT) for merchant vessel navigation. On a ship bridge a number of systems like Electronic Chart and Information Systems (ECDIS), automatic track control systems and the Automatic Identification System (AIS) are relying on that PVT provision. Nevertheless it is well known that GNSS performance can strongly degrade due to space weather events, jamming and spoofing. Especially the availability and usage of low cost jammers, so called personal privacy devices (PPD), lead to the question on how a continuous onboard provision of PVT data can be realized in the presence of such a PPD jammer. Generally three possible solutions for that challenge are contemplated: i) the usage of a terrestrial backup system, ii) a jamming resistant GNSS receiver or iii) the integration of GNSS with other onboard self-contained navigation sensors like, e.g. speed log, gyrocompass and inertial sensors (Inertial Measurement Unit - IMU). The paper will focus on option (iii) and addresses the question whether it is possible to bridge real maritime GNSS Jamming attacks by a Kalman Filter (KF) using GNSS, inertial and Doppler velocity log (DVL). The main challenge for the KF here is, that a free inertial integration needs to be performed after a time period with already degraded GNSS signal reception. As KF are known to be optimum estimators only under the assumption of known statistics of the measurements, the question arises, whether the pseudoranges under the influence of jamming still follow a certain statistics. In order answer this question an additional lab experiment was performed and analyzed. Concretely the sensor fusion was realized by using both loosely and tightly coupled architectures which have been implemented using an Unscented Kalman Filter (UKF). The fusion algorithm is formulated in ECEF coordinate frame with quaternion attitude parameterization, where the heading observability is addressed by fusing the baseline measurements from the GNSS compass. In order to evaluate the performance of the proposed sensor fusion scheme under the influence of a real maritime jamming attack we performed an experiment in our civilian maritime jamming testbed in the Baltic, which has been established together with the German Federal Network Agency approximately 6 nmi north of the peninsula Dars. Here a PPD jammer was mounted on the monkey deck of an anchored vessel. A test vessel was maneuvering around the jammer. As a test vessel the multipurpose research and diving vessel Baltic Diver II (length 29 m, beam 6.7 m and draught 2.8 m) was used, which was equipped with three dual frequency GNSS receivers, a fiber-optic gyroscope IMU and a Doppler velocity log. The obtained results confirm that real maritime jamming attacks using PPD Jammers can be bridged by the proposed tightly coupled GNSS-IMU-DVL integration scheme resulting in horizontal position errors of less than 10m only.

Uncrewed aircraft systems (UASs) and structure-from-motion/multi-view stereo (SfM/MVS) photogrammetry are efficient methods for mapping terrain at local geographic scales. Traditionally, indirect georeferencing using ground control points (GCPs) is used to georeference the UAS image locations before further processing in SfM software. However, this is a tedious practice and unsuitable for surveying remote or inaccessible areas. Direct georeferencing is a plausible alternative that requires no GCPs. It relies on global navigation satellite system (GNSS) technology to georeference the UAS image locations. This research combined field experiments and simulation to investigate GNSS-based post-processed kinematic (PPK) as a means to eliminate or reduce reliance on GCPs for shoreline mapping and charting. The study also conducted a brief comparison of real-time network (RTN) and precise point positioning (PPP) performances for the same purpose. Ancillary experiments evaluated the effects of PPK base station distance and GNSS sample rate on the accuracy of derived 3D point clouds and digital elevation models (DEMs). Vertical root mean square errors (RMSEz), scaled to the 95% confidence interval using an assumption of normally-distributed errors, were desired to be within 0.5 m to satisfy National Oceanic and Atmospheric Administration (NOAA) requirements for nautical charting. Simulations used a Monte Carlo approach and empirical tests to examine the influence of GNSS performance on the quality of derived 3D point clouds. RTN and PPK results consistently yielded RMSEz values within 10 cm, thus satisfying NOAA requirements for nautical charting. PPP did not meet the accuracy requirements but showed promising results that prompt further investigation. PPK experiments using higher GNSS sample rates did not always provide the best accuracies. GNSS performance and model accuracies were enhanced when using base stations located within 30 km of the survey site. Results without using GCPs observed a direct relationship between point cloud accuracy and GNSS performance, with R2 values reaching up to 0.97.

Space weather, geomagnetic, and ionospheric conditions have been identified as the single most influential cause of the Global Navigation Satellite System (GNSS) positioning performance degradation and disruption. The random nature of the appearance, development and intensity of GNSS ionospheric events renders them particularly hard to describe with a single global model. Moderate to massive space weather disturbances remains unresolved problem for satellite navigation. Here the problem of modelling Total Electron Content (TEC), an outcome of ionospheric conditions that affects GNSS positioning performance, in moderately disturbed geomagnetic field conditions, is addressed based on the experimental observations of geomagnetic field and their statistical properties. Here the problem of modelling Total Electron Content in moderately disturbed geomagnetic field conditions, as the originator of the ionospheric conditions, is addressed based on the experimental observations of geomagnetic field and their statistical properties. Three statistical learning-based models are developed, and their performance is assessed according to their ability to predict the resulting TEC. Statistical learning-based TEC prediction models are assessed for their performance, and recommendations for their utilizations are given. The research will continue with development of a larger database of observations taken at various observation sites and during differently generated moderate geomagnetic events. This database will allow for development of more robust TEC prediction models using statistical learning methods for self-adaptive mitigation of GNSS ionospheric effects for the core GNSS positioning performance improvement utilized with a wide rage of GNSS applications.

Global Navigation Satellite System (GNSS) plays an increasingly important role in all walks of life. In order to meet the demands of different users, it is crucial to establish corresponding correct mathematical models, especially in the field of precise positioning and navigation. However, due to the spatiotemporal complexity of and limited knowledge on GNSS errors, some residual errors would inevitably remain even after being corrected with differencing and linear combination, empirical model correction, and traditional parameterization. These residual observation errors are referred to as unmodeled errors. In fact, the unmodeled errors have adverse impacts on high-precision GNSS positioning. However, most existing studies mainly focus on handling the part of systematic errors that can be adequately modeled and then simply ignore the part of unmodeled errors that may actually exist. To make a breakthrough in the precision and reliability of GNSS applications currently, this study focuses on the theory and method for processing the GNSS unmodeled errors based on the mathematical model compensation, including resilient functional model adjustment and resilient stochastic model optimization. Specifically, according to the significance and properties of unmodeled errors, the unmodeled-error-ignored model, unmodeled-error-corrected model, unmodeled-error-fixed model, unmodeled-error-float model, and unmodeled-error-weighted model are proposed, where the approaches of significance testing, geometry-free/based/fixed models, hemispherical/hierarchy maps, composite stochastic model, multi-epoch partial parameterization, and inequality and equality constraints are adopted. Ultimately, an unmodeled error processing flow that can adaptively adjust as the external conditions change is proposed, then one can obtain high-precision GNSS positioning solutions.

As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather.

Global navigation satellite system (GNSS) has been widely used in many fields due to their low cost and high positioning accuracy. Because of the open frequency of navigation signals, the low power of navigation signals, and the growing reliance of many modern wireless systems on satellite-based navigation, GNSS performance may be easily affected by interference signals. Monitoring and troubleshooting of interference sources are important means to guarantee the normal use of satellite navigation applications and are an important part of GNSS operation in complex electromagnetic environments; however, traditional angle of arrival (AOA) algorithms cannot efficiently operate with coherent signals, so a decoherence-based orientation scheme is proposed to optimize the AOA algorithm. Furthermore, a joint AOA and time difference of arrival (TDOA) interference localization algorithm is proposed for problems such as the lack of accuracy in a single interference source localization algorithm. Numerical simulation results show that decoherence-based AOA localization can be well applied to various interference signals, and the accuracy of the joint AOA and TDOA interference localization algorithm is higher than that of single-method interference localization. In addition, the physical verification further verifies the usability and reliability of the GNSS interference source positioning algorithm proposed in this paper.

The number and scope of Global Navigation Satellite System (GNSS)-based space applications has grown significantly since the first GNSS space receiver was flown in the early 1980's. The vast majority of GNSS space users operate in Low-Earth Orbit (LEO), where the use of GNSS receivers has become routine. However, the use of GNSS has expanded to other orbit regimes like Geostationary Orbits (GEO) and High Eccentric Orbits (HEO) but has been very limited due to the challenges involved. The major challenges for such types of orbits including much weaker signals, reduced geometric diversity, and limited signal availability. In any case, considering the recent development of multiple GNSS constellations and ongoing upgrades to existing constellations, GNSS signal availability will improve significantly. As a result, this expanded multi-GNSS signal capability will enable improved on-orbit navigation performance and will also allow the development of new mission concepts. High altitude space users will especially benefit from this evolution, which will provide GNSS signals to challenging regimes well beyond Low Earth Orbit. These benefits will only be realised, however, if additional signals are designed to be interoperable, are clearly documented and supported. In order to enhance the overall GNSS performance for spacecraft's in regimes from LEO, GEO to HEO and beyond, all Satellite Navigation constellation providers and regional augmentation system providers are working together through the United Nations International Committee on GNSS (ICG) forum to establish an interoperable GNSS Space Service Volume (SSV) for the benefit of all GNSS space users. This paper provides an overview of the technical work and in particular the simulations, performance analysis and discussions of the outcomes and results obtained by the UN ICG Working Group-B in the context of the GNSS Space Service Volume activities, which were supported by all GNSS service providers.

Safety as the key quality property among RAMS (reliability, availability, maintainability, and safety) demonstrates the most stringent performance in correspondence with the safety requirements and performance standards like EN 50126. Meanwhile, GNSS (Global Navigation Satellite Systems) are penetrating the railway now widely in non-safety related applications as passenger information, fleet management, etc. GNSS also have great potential for safety-related applications in railway such as the train location determination function, which the safety performance needs to be assured through hazard analysis and risk assessment process. The train location determination by satellite-based localization system is elevating the train control to the next level. The European Train Control System (ETCS) has being trying to implementing Level 3, the Chinese Train Control System (CTCS) has been implementing CTCS Level 3 low cost especially for secondary lines, and the U.S. is implementing train control systems under Positive Train Control (PTC) requirements. The train control system needs GNSS to provide more accurate location information of trains, more flexible and condensed trains on tracks with the consistency of still keeping the current safety level or even improve safety. Some researchers are trying to understand the performance of GNSS (GPS / EGNOS / Beidou) for railway applications from the fundamental accuracy level. A satellite-based train localization unit (SaLuT) as the entity to perform the train location determination function is to bring the GNSS accuracy evaluation up to safety integrity according to the safety requirements and standards for risk assessment. One of the key consequential result derived from the train location is the adequate safety margin. The safety margin, which can also be called as “safe braking distance”, is a margin indicated to rail traffic that would allow the train to stop with the application of normal service braking. The safety margin estimation quality and the risk of the safety margin shows the hazard rate for the safety margin estimation function performed by the designed localization unit SaLuT. This paper discusses the safety margin estimation method considering both GNSS accuracy and integrity assessment aspects of SaLuT, in accordance of the settled safety requirements of location determination function. To analyze the hazard of the safety margin estimation, a formal method is applied to model the SaLuT behavior and functions. The formal method based on stochastic Petri net enables the modeling process to include the GNSS receiver collected real data on the test track into it. The safety margin estimation method together with the risk assessment method using the real data can generate quantitative indicators to represent the localization function and safety margin estimation quality. The data used for the analysis is collected in the Qinghai-Tibet railway line from Golmud station to Ganlong station by SaLuT installed on a locomotive along the track. With the stochastic Petri net model and the systematic equation using the real collected data to estimate the safety margin based on the GNSS technologies, the SaLuT can be validated and verified for its hazard rates, which provides information for the safety cases in order to meet the industrial normative requirements.

Space Weather represents a risk for spacecraft and astronauts when in orbit around Earth. Coronal Mass Ejections, Solar Energetic Particles events, Geomagnetic Storms, and other Space Weather phenomena can have a wide range of effects on Spacecraft, from Single Event Effects to total loss of the mission. They can also increase the radiation exposure of astronauts beyond the safe limit. Considering Space Weather and its effects during the design and operations of spacecraft therefore becomes essential. Space Weather is one of the segments that the European Space Agency (ESA) has included in the Space Situational Awareness (SSA) programme. ESA established a list of services that are to be provided during the execution of the programme. In order to better approach the different users, the Space Weather community has been divided into several User Domains, one of which is Spacecraft Operations. In this paper we will discuss how the services that ESA intends to provide to the Spacecraft Operations User Domain address the risks that Space Weather represents. We will discuss the relevant results of the “Provision of Space Weather Segment Precursor Services – Part 1: Definition and Service Consolidation” project (referred to as SN-I).This project has, among its key objectives, the identification of services needed by the community of users interested in Space Weather, and analysing any gaps with the service provision via assets already identified by the Agency. In addition we will discuss the Space Weather-dedicated User Portal and the services provided by it. This Portal will provide access to several ESA owned applications that will be redeployed into a common Data Centre, and to several ’federated’ services that will remain hosted at the provisioning organisations. We will also present the architecture of the system and the services that it can provide to the Space Operations User Domain, as well as the concept of ’federation’ as it has been implemented in SN-I. Finally the paper will discuss general ideas for the future evolution of the provision of services to the Space Operations user domain and community from the consolidated Space Weather data centre.

<p>Knowledge of the ionospheric electron density is essential for a wide range of applications, e.g., telecommunications, satellite positioning and navigation, and Earth observation from space. Therefore, considerable efforts have been concentrated on modeling this ionospheric parameter of interest. Ionospheric electron density is characterized by high complexity and is space−and time−varying, as it is highly dependent on local time, latitude, longitude, season, solar cycle and activity, and geomagnetic conditions. Daytime disturbances cause periodic changes in total electron content (diurnal variation) and additionally, there are multi-day periodicities, seasonal variations, latitudinal variations, or even ionospheric perturbations that cause fluctuations in signal transmission.</p><p>Because of its multiple band frequencies, the current Global Navigation Satellite Systems (GNSS) offer an excellent example of how we can infer ionosphere conditions from its effect on the radiosignals from different GNSS band frequencies. Thus, GNSS techniques provide a way of directly measuring the electron density in the ionosphere. The main advantage of such techniques is the provision of the integrated electron content measurements along the satellite-to-receiver line-of-sight at a large number of sites over a large geographic area.</p><p>Deep learning techniques are essential to reveal accurate ionospheric conditions and create representations at high levels of abstraction. These methods can successfully deal with non-linearity and complexity and are capable of identifying complex data patterns, achieving accurate ionosphere modeling. One application that has recently attracted considerable attention within the geodetic community is the possibility of applying these techniques in order to model the ionosphere delays based on GNSS satellite signals.</p><p>This paper deals with a modeling approach suitable for predicting the ionosphere delay at different locations of the IGS network stations using an adaptive Convolutional Neural Network (CNN). As experimental data we used actual GNSS observations from selected stations of the global IGS network which were participating in the still-ongoing MGEX project that provides various satellite signals from the currently available multiple navigation satellite systems. Slant TEC data (STEC) were obtained using the undifferenced and unconstrained PPP technique. The STEC data were provided by GAMP software and converted to VTEC data values. The proposed CNN uses the following basic information: GNSS signal azimuth and elevation angle, GNSS satellite position (x and y). Then, the adaptive CNN utilizes these data inputs along with the predicted VTEC values of the first CNN for the previous observation epochs. Topics to be discussed in the paper include the design of the CNN network structure, training strategy, data analysis, as well as preliminary testing results of the ionospheric delays predictions as compared with the IGS ionosphere products.   </p>

Due to the low signal power, the Global Navigation Satellite System (GNSS) signal is vulnerable to interference and even cannot be captured or tracked in harsh environments. As an alternative, the Low Earth Orbit (LEO) satellite has been widely used in the navigation field due to the advantages of low cost and strong signals. It is becoming a significant component of the new combined navigation system with GNSS. The combination of an LEO Doppler signal and GNSS observables can improve the positioning accuracy and high-precision positioning convergence time of the GNSS receiver. However, the GNSS signal receiving capability cannot be improved from this data fusion level. We propose a novel assisted structure where GNSS signal acquisition and Doppler tracking are assisted by LEO Doppler positioning. The receiver uses the LEO signal to achieve Doppler positioning firstly. Then, the coarse position with the GNSS navigation messages received from LEO, as well as the estimated clock information, is used to assist in the acquisition and tracking of GNSS. In this way, the GNSS receiver’s sensitivity can get the benefit from this integrated system. The paper presents the structure of the assisted receiver and analyzes the assisted GNSS signal acquisition and carrier tracking performance in detail. Simulation experiments of this assisted structure are carried out to verify its superiority of acquisition and tracking sensitivity in comparison with standalone GNSS receivers. Theoretical analysis and experimental results show that the proposed acquisition method can achieve 90% detection probability at a carrier-to-noise ratio (C/N0) of 15 dB-Hz, which is about 8 dB higher than the conventional acquisition method without assistance; the proposed tracking method can track weak signals of 5 dB-Hz, which is about 4 dB higher than the conventional method. Therefore, this novel LEO-assisted receiver has significantly improved weak signal acquisition and tracking sensitivity.

The Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC‐1 and COSMIC‐2) missions provide global observations of atmospheric and ionospheric conditions using Global Navigation Satellite System (GNSS) radio occultation (RO). While distributing satellites across different orbital inclinations ensures broad spatial coverage, it does not inherently guarantee uniform improvements in electron density estimation across all data assimilation (DA) methods. This study evaluates the impact of various RO constellation configurations on ionospheric space weather nowcasting using ANCHOR, a DA model developed at the Naval Research Laboratory. Observing system simulation experiments (OSSEs) quantify global enhancements in ionospheric state estimation and identify the optimal number of satellites before diminishing returns are observed. These findings are critical for agencies such as National Oceanic and Atmospheric Administration and the Department of Defense, informing the design of future RO missions to improve space weather monitoring and operational forecasting capabilities.

Global Navigation Satellite Systems (GNSS) underpin a number of modern life activities, such as air/marine transport, autonomous vehicles/machinery control and in areas such as construction, agriculture and offshore operations. GNSS signals are sensitive to a number of vulnerabilities, of which a one major shortcoming is related to the perturbations due to the Earth’s ionosphere. In particular, a phenomenon known as ionospheric scintillation, characterised by rapid fluctuations in GNSS signal amplitude and phase, may seriously disrupt satellite tracking and degrade system accuracy, reliability and integrity. The research presented in this paper was carried out under the “Ionosphere Prediction Service” (IPS) project funded by the European Commission (EC) under Horizon 2020, in the frame of the Galileo programme. The main aim of this project is to translate the forecast of the ionosphere into GNSS userdevoted metrics through the design and development of a prototype for an ionospheric prediction service. The statistical models developed to estimate the 3D positioning errors at the high and low latitudes as a function of scintillation levels are presented. The statistical approach of non-linear regression was applied on a long-term data set to develop these models. If scintillation information can be nowcasted, the developed statistical models can be used to nowcast the 3D positioning errors on a regional or a global scale, which can provide GNSS users with the expected error in the 3D position estimation under scintillation.