Headnote
ABSTRACT
Objective: This study presents the development of data panels to monitor the activities of the ionospheric layer in the western region of Paraná.
Theoretical Framework: Since the ionosphere is considered the largest source of systematic error in positioning by GNSS (Global Navigation Satellite System), understanding it is essential for applications in the region, such as controlled machine traffic, dam monitoring, and aerial navigation, among others.
Method: A set of metrics was calculated using GNSS observables from the ITAI (Foz do Iguaçu/PR), MSMN (Mundo Novo/MS), and PRCV (Cascavel/PR) stations of the RBMC (Brazilian Network for Continuous Monitoring of GNSS Systems), from 2017 to 2023 (solar cycle 25).
Results and Discussion: The data panels made it possible to verify that during the peak of solar cycle 25 (2022-2023), there was a significant increase in metrics: ionospheric delay obtained by the carrier phase (If), ionospheric irregularity index (ROTI), and ionospheric gradient (glf), with peaks between August and April.
Research Implications: The analysis of ionospheric gradients also indicates the feasibility of implementing GBAS (Ground-Based Augmentation System) at Foz do Iguaçu International Airport, which will assist in the precision of aircraftapproach and landing.
Originality/Value: The results generally provide a structured understanding of ionospheric irregularities, allowing pattern detection and improvement in monitoring.
Keywords: Global Navigation Satellite System, Ionosphere, Data Panels.
RESUMO
Objetivo: Este estudo apresenta o desenvolvimento de paineis de dados para monitorar as atividades da camada ionosférica na região oeste paranaense.
Referencial Teórico: Considerada a maior fonte de erro sistemático no posicionamento pelo GNSS (Global Navigation Satellite System), a compreensão da ionosfera é imprescindível para aplicações na região, como tráfego controlado de máquinas, monitoramento de barragens, navegação aérea, dentre outros.
Método: Um conjunto de métricas foi calculada, utilizando observáveis GNSS das estações ITAI (Foz do Iguaçu/PR), MSMN (Mundo Novo/MS) e PRCV (Cascavel/PR) da RBMC (Rede Brasileira de Monitoramento Contínuo dos Sistemas GNSS), para o período de 2017 a 2023 (ciclo solar 25).
Resultados e Discussão: A partir dos paineis de dados foi possível verificar que durante o ápice do ciclo solar 25 (2022-2023) houve aumento significativo das métricas: atraso ionosférico obtido pela fase da portadora (If), índice de irregularidades ionosféricas (ROTI) e gradiente ionosférico (glf), com picos entre agosto e abril.
Implicações da Pesquisa: A análise dos gradientes ionosféricos também indica a viabilidade de implantação do GBAS (Ground-Based Augmentation System) no Aeroporto Internacional de Foz do Iguaçu, que auxiliará na aproximação e pouso preciso de aeronaves.
Originalidade/Valor: Os resultados fornecem, em geral, uma compreensão estruturada das irregularidades ionosféricas, permitindo a detecção de padrões e melhorias no monitoramento.
Palavras-chave: Global Navigation Satellite System, Ionosfera, Paineis de Dados.
RESUMEN
Objetivo: Este estudio presenta el desarrollo de paneles de datos para monitorear las actividades de la capa ionosférica en la región oeste de Paraná.
Marco Teórico: Considerada la mayor fuente de error sistemático en el posicionamiento por GNSS (Sistema Global de Navegación por Satélite), comprender la ionosfera es esencial para aplicaciones en la región, como tráfico controlado de máquinas, monitoreo de presas, navegación aérea, entre otras.
Método: Se calculó un conjunto de métricas, utilizando observables GNSS de las estaciones ITAI (Foz do Iguaçu/PR), MSMN (Mundo Novo/MS) y PRCV (Cascavel/PR) de la RBMC (Red Brasileña de Monitoreo Continuo de Sistemas GNSS), para el período de 2017 a 2023 (ciclo solar 25).
Resultados y Discusión: A partir de los paneles de datos fue posible verificar que durante el pico del ciclo solar 25 (2022-2023) hubo un aumento significativo en las métricas: retraso ionosférico obtenido por la fase de la portadora (If), índice de irregularidad ionosférica (ROTI) y gradiente ionosférico (glf), con picos entre agosto y abril.
Implicaciones de la investigación: El análisis de los gradientes ionosféricos también indica la viabilidad de la implementación del GBAS (Ground-Based Augmentation System) en el Aeropuerto Internacional de Foz do Iguaçu, que ayudará en la aproximación y aterrizaje precisos de las aeronaves.
Originalidad/Valor: En general, los resultados proporcionan una comprensión estructurada de las irregularidades ionosféricas, lo que permite la detección de patrones y mejoras en el monitoreo.
Palabras clave: Sistema Global de Navegación por Satélite, Ionosfera, Paneles de Datos.
1 INTRODUCTION
The present study is based on the creation of data panels for monitoring ionospheric activities, fed by metrics calculated from GNSS (Global Navigation Satellite System) data from RBMC (Brazilian Network for Continuous Monitoring of GNSS Systems) stations in the municipalities of Foz do Iguaçu/PR (ITAI), Mundo Novo/MS (MSMN), and Cascavel/PR (PRCV), as shown in Figure 1. The analysis of available metrics, calculated using the Ion_Index script (Perreira; Camargo, 2017) and processed by another cleaning and storage script, aims to investigate and monitor the intensity of ionospheric gradients and irregularities on a given date in the region covered by the present study.
The Western Region of Paraná is characterized by intense agricultural activity, primarily the cultivation of grains (soybeans and corn), as well as the coverage of the reservoir of the Itaipu hydroelectric plant and tourism along the triple border in Foz do Iguaçu (shared by Argentina, Brazil, and Paraguay). Since the ionosphere is considered the largest source of systematic error in positioning by GNSS, understanding ionospheric effects is essential for applications that require accurate coordinates in the region, such as controlled machine traffic, dam monitoring, and aerial navigation.
Thus, the Power BI tool was used to create data panels for statistical analysis and comparison of historical data from the calculated metrics. By analyzing the graphs, it is possible to detect a local maximum at the peak of solar cycle 25 (composed by the years 2022 and 2023), especially between August and April, and, in addition, to find local minima in the period corresponding to the rise of the cycle (comprising the years 2018 to 2021). This finding represents a method for predicting periods of higher ionospheric irregularity incidence, which can cause errors in GPS (Global Positioning System), GLONASS (Globalnaya Navigatsionnaya Sputnikovaya Sistema), Galileo, and BeiDou/Compass.
The graph that relates glf (ionospheric gradient by phase) to the elevation of the satellites is used by DECEA (Airspace Control Department) to assess the feasibility of a GBAS (Ground-Based Augmentation System) in the Western Region of Paraná. To make possible use of GBAS, glf values cannot exceed 425 mm/km from the CONUS Threat Model (DATTA-BARUA et al., 2010). Furthermore, we seek to relate the collected data with relevant metrics, such as ionospheric delay obtained by the carrier phase (If), ionospheric irregularity index (ROTIN), ionospheric gradient obtained by the phase (gIf), receiver instrument trend (DCBr), the distance between the ionospheric position points (IPP - Ionospheric Pierce Point) and ionospheric irregularity index for each satellite (ROTI).
The hypothesis is that analyzing the collected data will enable the identification of patterns, trends, and correlations between the mentioned metrics, providing valuable insights into the characteristics and behavior of ionospheric activities in the Western Region of Paraná. This information can be used to enhance the monitoring and mitigation of adverse effects on GNSS systems from the ionosphere, thereby improving the accuracy and reliability of projected satellite positioning applications.
2 THEORETICAL FRAMEWORK
GNSS is a set of positioning and navigation systems that use constellations of satellites orbiting the Earth to provide time and location information to receivers anywhere on the planet. In addition to GPS, other systems are in operation, including GLONASS from Russia, Galileo from the European Union, and BeiDou/Compass from China (Monico, 2008; Silva et al., 2020).
These GNSS systems work similarly. That is, satellites transmit radio signals that contain precise information about their orbits and the time the signals were transmitted. GNSS receivers, when receiving signals from at least four satellites, are capable of calculating their three-dimensional position with high precision, taking into account the signal control time (Pereira, 2018).
GNSS offers a wide range of mobile applications, from personal navigation on devices to professional applications in areas such as Precision Agriculture, environmental monitoring, structure monitoring, transportation, Geodesy, and many others (Kaplan & Hegarty, 2017; Pereira, 2018).
In Brazil, the RBMC, a network of permanent stations that forms part of the Brazilian Institute of Geography and Statistics (IBGE) 's structure, stands out. These stations are equipped with high-precision GNSS receivers, allowing for continuous and precise positioning monitoring throughout the Brazilian territory. RBMC provides high-quality data for various applications, including monitoring ionospheric activities (IBGE, 2023).
The ionosphere is a layer of the Earth's atmosphere, approximately 50 to 1000 km high, and is composed of ionized particles that interact with GNSS signals. The presence of irregularities in the ionosphere can cause distortions and delays in GNSS signals, resulting in positioning errors at various scales. A rapid and random variation in the intensity of the GNSS signal, also called ionospheric scintillation, can cause loss of tuning in some cases (Pereira, 2018).
Monitoring data from the ionospheric layer can be represented by reports and dashboards, which are essential tools for data visualization and analysis. They allow the presentation of information in a clear and organized way, facilitating the identification of patterns, trends, and correlations. In this sense, Power BI is a business intelligence platform developed by Microsoft that offers advanced data visualization capabilities (Ferrari & Russo, 2016). Power BI enables the creation of interactive reports, graphs, and data panels that facilitate understanding and informed decision-making (Microsoft, 2023). In the context of this work, Power BI is used to visualize metrics calculated from data from RBMC stations related to ionospheric activities, enabling the identification of patterns, trends, and correlations.
This methodological approach to data reporting and visualization allows a more in-depth understanding of ionospheric activities in the Western Region of Paraná, providing valuable insights for monitoring and mitigating the adverse effects of the ionosphere on satellite positioning systems.
3 METHODOLOGY
The ionospheric metrics that feed the platform were calculated using the Ion_Index script (Pereira & Camargo, 2017) implemented in C, which transfers ephemeris data and observation RINEX (Receiver Independent Exchange Format) files to the local machine, unzips them, and performs the processing, generating a text file with a header that contains the necessary metrics, in their respective columns. For the present work, an elevation mask of 30 degrees (to minimize multipath effects of the signals), a height of 350 km for the ionospheric layer, and an interval of 60 seconds for determining the ionospheric gradients were considered. For better understanding, the Ion_Index script is referred to as the collection script.
After collecting the files with the metrics, they were processed by another script, implemented in Python, which treated the metrics, aiming to remove lines containing invalid information. Processing occurs by transforming text files into DataFrames, a two-dimensional labeled data structure from the Pandas library, and searching for possible invalid values, such as character sequences or fields with NaN (Not a Number) values.
Initially, the metrics were calculated with a temporal resolution of 15 seconds, which resulted in files that occupied a significant amount of storage space. To enable pre-processing of the data and implementation of the panels, the temporal resolution was adjusted to a time-based approach. This conversion was performed by another script implemented in Python, which organized all the metrics for each satellite and respective carrier in a one-hour range, calculating the average of the respective values and adding them to a new CSV file (comma-separated values).
The visualization of data collected and processed by the scripts was carried out using Microsoft's proprietary tool, Power BI. Data collected from the three stations used in this study-ITAI (Foz do Iguaçu, PR), MSMN (Mundo Novo, MS), and PRCV (Cascavel, PR)- were added to the tool by importing them as CSV files. After the database was loaded, the relationship between the files was established using the data column, and a quick check of the data source's integrity was performed. No treatment was required within the tool.
This methodological approach enabled us to thoroughly explore the collected data and gain a more in-depth understanding of ionospheric activities in the western region of Paraná, presenting the analysis across different periods (years).
4 RESULTS AND DISCUSSIONS
With a focus on verifying the intensity of irregularities and ionospheric gradients, some visualizations of the calculated metrics were generated, enabling a better understanding of the ionosphere in the Western Region of Paraná.
In the foreground, the metrics If (ionospheric delay obtained by phase), ROTIN (ionospheric irregularity index considering all available satellites), and glf (ionospheric gradient obtained by phase) of all GPS and GLONASS satellites with dates available from 2017 to 2023 (Figure 2) were related. An increase in the values can be seen at the peak of the 25th solar cycle (2022 and 2023), especially between August and April (seasonal variation). Conversely, lower values are observed during the period of solar cycle rise, spanning the years 2018 to 2021.
It is possible to notice a gradient categorized as an outlier on August 3, 2020. The classification of the gradient as an atypical value is justified because it is the only one to reach such a high value (41,782,615.48), deviating from the so-called intact values. Furthermore, the values that make up the graph have undergone normalization to facilitate comparison between them.
To verify whether any positioning system (GPS or GLONASS) had a difference in metric values, thus enabling a possible preference for the one that results in lower delay rates, the If, ROTI, and glf metrics were compared regarding the available satellites.
To facilitate understanding, each graph in this analysis line contains one of the metrics for each available satellite. The graphs are presented in Figures 3 (ITAI), Figure 4 (MSMN), and Figure 5 (PRCV). The values in the graphs represent the maximum value of the given metric for the respective day.
For the PRCV station, some atypical values were found for the glf metric (Figure 5) as they do not represent reality.
To analyze the feasibility of using GBAS in the region, a sequence of graphs was constructed, relating the satellite elevation with the If, ROTI, and glf metrics for the ITAI (Figure 6) and MSMN (Figure 7) stations.
Comparison of satellite elevation with If, ROTI, and gIf for ITAI station.
The analysis of the patterns observed in the graphs related to the satellite's azimuth, If, ROTI, gIf, DCBr, IPP distance, and ROTIN allows for the extraction of relevant insights into ionospheric activities in the Western Region of Paraná. As the results from all stations are similar due to proximity, only reports for ITAI will be presented. In Figure 8, the azimuth is related to If, ROTI, and gIf. It is worth noting that only the maximum daily values of each metric are presented.
This analysis enables the identification of the main directions of occurrence of ionospheric irregularities (also known as bubbles) in the Western Region of Paraná. The highest irregularity values are between 0° and 100° and between 270° and 360°, corresponding to the West-North-East of the Western Region of Paraná.
About DCBr, jumps in values are observed on certain days. The graph of this second analysis is presented in Figure 9 for the ITAI station.
These jumps indicate changes or updates in the station's antenna or receiver. This information is relevant, as the DCBr directly interferes with the quality calculation of the TEC (Total Electron Content). Visualizing the data through line graphs made it possible to identify the days on which such jumps in DCBr values occurred. To gain greater confidence in the analysis, the station update dates were compared to the RBMC Station Information Report, which confirmed the efficiency of the tool. From Figure 9, it can be seen that on July 25, 2018, a change occurred at the station's antenna, as indicated by a jump in the DCBr value. This statement can be confirmed in the RBMC ITAI Station Information Report.
Another analysis carried out was the relationship between the IPP distances and gIf. The graph of this analysis is presented in Figure 10.
The results show that there is a large density of gradient values between approximately 3.5 km and 10 km of distance among the IPPs. For distances smaller than 3 km and greater than 10 km, there are no gradient values. Therefore, to estimate confidence gradients, the distance between the IPPs in space must be approximately in the range above.
Finally, the gIf, ROTI, and ROTIN were listed for all available days. The graph is presented in Figure 11.
The results indicate a correlation between the gradient and the levels of irregularities, suggesting that the greater the gradient, the higher the level of irregularities. This correspondence can be used to validate the gradients obtained.
5 CONCLUSION
The data panels enable the monitoring of ionospheric activities in the Western Region of Paraná using RBMC stations, eliminating the need to purchase specialized geodetic receivers. This technique provided a clear and organized view of ionospheric irregularities and gradients for a specific period and the specified region.
A concentration of high values of the If, ROTIN, and glf metrics at the peak of solar cycle 25, corresponding to the years 2022 and 2023, was observed, along with a seasonal variation in the ionosphere (spanning the months of August to April) for the analyzed region.
Such information helps predict periods with a higher likelihood of ionospheric irregularities, which are a source of errors in positioning using GNSS.
The graphs comparing the glf metric with elevation (Figures 6 and 7) are used by the Airspace Control Department (DECEA) to evaluate the feasibility of adopting GBAS in a given region. For the study region, GBAS proves to be viable, as the metric values do not exceed 425 mm/km. Similar values were noted for metrics on different satellites (Figures 3, 4, and 5).
The dynamics between the orientations of the most intense ionospheric irregularities and satellite azimuth bands indicated the presence of ionospheric bubbles in specific directions. The identification of jumps in DCBr values made it possible to recognize changes or updates in the receivers' instruments. The relationship between the IPP distance and the ionospheric gradient provided information about the quality of the calculated gradients. Finally, the correspondence between the gradients and the irregularity indices validates the gradients obtained.
For future reference, it is recommended to continue monitoring ionospheric activities in the Western Region of Paraná to investigate other characteristics and measures linked to ionospheric activities and to apply these findings to different regions of Brazil.
Sidebar
References
REFERENCES
Datta-Barua, S., Lee, J., Pullen, S., Luo, M., Ene, A., Qiu, D., ... & Enge, P. (2010). Ionospheric threat parameterization for local area global-positioning-system-based aircraft landing systems. Journal of Aircraft, 47(4), 1141-1151.
Da Silva, C. M., Júnior, P. D. T. S., Alves, D. B. M., & Monico, J. F. G. (2020). Galileo: a consolidação do sistema de posicionamento europeu. Revista Brasileira de Geomática, 8(4), 238-255.
Ferrari, A., & Russo, M. (2016). Introducing Microsoft Power BI. Microsoft Press.
IBGE. (2024). Ibge.gov.br. https://www.ibge.gov.br/geociencias/.
Kaplan, E. D., & Hegarty, C. (Eds.). (2017). Understanding GPS/GNSS: principles and applications. Artech House.
Microsoft. Power BI. Recuperado em 22 de agosto de 2023, de<https://learn.microsoft.com/en-us/power-bi/> fundamentals/power-bi-overview.
Monico, J. F. G. (2007). Posicionamento pelo GNSS: descrição, fundamentos e aplicações. Editora Unesp.
Pereira, V. A. S. (2018). Investigação da usabilidade do GBAS no brasil.
Stuani Pereira, V. A., & de Oliveira Camargo, P. (2017). Brazilian active GNSS networks as systems for monitoring the ionosphere. GPS Solutions, 21(3), 1013-1025.
