Global Navigation Satellite System Reference Research Articles

ADVANCED ANALYSIS TOOLS FOR THE EUROPEAN GROUND MOTION SERVICE DATA

Abstract. This paper is focused on Advanced Differential Interferometric SAR (A-DInSAR). In the first part, the paper describes the European Ground Motion Service (EGMS), a new service of the Copernicus Land Monitoring Service. The EGMS provides consistent, regular, standardized, harmonized and reliable information regarding natural and anthropogenic ground motion over Europe. It is derived from A-DInSAR analysis of Copernicus Sentinel-1 data. The EGMS includes three main products: Basic, i.e. basic deformation maps delivered for individual and consistent frames of image stacks; Calibrated, i.e. deformation map integrated into a reference Global Navigation Satellite System network; and Ortho, which includes the East-West and Up-Down deformation, obtained by combining the ascending and descending results. The second part of the paper describes examples of data analysis. The first one is a clustering of PSI results, which highlights the so-called Active Deformation Areas (ADAs). The second one is an automatic classification of the ADAs. The third analysis concerns the differential deformation maps, which are useful to highlight areas where high deformation gradients occur. Such gradients are often associated to damages in buildings and infrastructures.

High energy events as a combined effect of human impact and geoenvironmental factors - the case study based on GNSS data

In the Upper Silesian Coal Basin (USCB) mining region in Poland, coal production has been gradually reduced for many years. Nevertheless, the number of high-energy tremors remains at a similar level or decreases much slower than production. We analyze this problem in the aspect of geological setting and on the base of geodetic data. We explain the paradox by specific interaction between man-induced and natural tectonic stress as seasonal hydrological effects. Anomalous energies released during large seismic events exceeded the predetermined threshold, typical for mining tremors in the area. Further, the authors point out the seasonal occurrence of these events. Temporal variations of distances between continuously operating GNSS (Global Navigation Satellite Systems) reference stations were analyzed, and linear strain, inferred from these geodetic observations, corresponded to high-energy tremors in the area. Consequently, the seismic events usually occurred when the analyzed baseline performance demonstrated significant seasonal increases or decreases of evaluated temporal distribution of strain. The aim of the analysis was to evaluate the relationships between the characteristics of the time series of deformations and the occurrence of seismic tremors of energy E ≥ 3 x 107J occur. Seasonal occurrences of high-energy seismic events as the energy they released suggest the influence of environmental factors.

Interpolation of GNSS Position Time Series Using GBDT, XGBoost, and RF Machine Learning Algorithms and Models Error Analysis

The global navigation satellite system (GNSS) position time series provides essential data for geodynamic and geophysical studies. Interpolation of the GNSS position time series is necessary because missing data will produce inaccurate conclusions made from the studies. The spatio-temporal correlations between GNSS reference stations cannot be considered when using traditional interpolation methods. This paper examines the use of machine learning models to reflect the spatio-temporal correlation among GNSS reference stations. To form the machine learning problem, the time series to be interpolated are treated as output values, and the time series from the remaining GNSS reference stations are used as input data. Specifically, three machine learning algorithms (i.e., the gradient boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), and random forest (RF)) are utilized to perform interpolation with the time series data from five GNSS reference stations in North China. The results of the interpolation of discrete points indicate that the three machine learning models achieve similar interpolation precision in the Up component, which is 45% better than the traditional cubic spline interpolation precision. The results of the interpolation of continuous missing data indicate that seasonal oscillations caused by thermal expansion effects in summer significantly affect the interpolation precision. Meanwhile, we improved the interpolation precision of the three models by adding data from five stations which have high correlation with the initial five GNSS reference stations. The interpolated time series for the North, East, and Up (NEU) are examined by principal component analysis (PCA), and the results show that the GBDT and RF models perform interpolation better than the XGBoost model.

Assimilating GNSS TEC with an LETKF over Yunnan, China

A robust ionospheric model is indispensable for providing the atmospheric delay corrections for global navigation satellite system (GNSS) navigation and positioning and forecasting the space environment. The accuracy of ionospheric models is limited due to the simplified model structures. Complicated spatiotemporal variations in total electron content (TEC) biases between GNSS and international reference ionosphere (IRI) suggest a robust strategy to optimally combine GNSS and IRI TEC for high-precision modeling. In this paper, we propose a novel ionospheric data assimilation method, which is a local ensemble transform Kalman filter (LETKF), to construct an ionospheric model over Yunnan in southwestern China. We used the LETKF method to assimilate the ionospheric TEC extracted from GNSS observations in Yunnan into the IRI-2016 model. The experimental results indicate that the ionospheric data assimilation has a more pronounced improvement effect on the IRI empirical model during periods of geomagnetic quiet than during periods of geomagnetic disturbance. On quiet magnetic days, the skill score (SKS) of the assimilation is 0.60 and the root mean square error (RMSE) values before and after assimilation are 5.08 TECU and 2.02 TECU, respectively. The correlation coefficient after assimilation increases from 0.94 to 0.99. On magnetic storm days, the SKS of the assimilation is 0.42 and the RMSE values before and after assimilation are 5.99 TECU and 3.46 TECU, respectively. The correlation coefficient after assimilation increases from 0.98 to 0.99. The results suggest that the LETKF algorithm can be considered an effective method for ionospheric data assimilation.

Evaluation of error characteristics of derived TEC with IRI-2016

The total electron content (TEC) derived from the Global Navigation Satellite System (GNSS) has been widely used to study the ionosphere. The evaluation of the derivation method is usually based on the fitting residuals, which are the difference between the estimate and observation. However, there are few studies on the statistical characteristics of fitting residuals. The characteristics of fitting residuals and estimation errors are studied with international reference ionosphere (IRI-2016) and GNSS observations. First, the systematic estimation errors caused by mathematical modeling and related assumptions are discussed with IRI-2016. The errors are 1 TECU and 0.5 TECU for slant TEC (STEC) and vertical TEC (vTEC), respectively. The fitting residuals of STEC observations (ΔSTECr) are studied by different noise models, including independent and identically distributed (IID) Gaussian noise, independent and non identical distributed (INID) Gaussian noise and INID Laplace noise. The statistical analysis shows that the distribution of ΔSTECr for the whole observations of all receivers is Gaussian when the noise is IID Gaussian, while it is Laplace for INID Gaussian or Laplace noise. The ΔSTECr reflects the statistical characteristic of noise. The estimation error of STEC (ΔSTEC), which is the difference between the estimate (STECe) and the model STEC (STEC) is discussed with IRI-2016. The distribution of ΔSTEC is neither Gaussian nor Laplace, and STECe is a biased estimate of STEC. The distribution of estimation error of vTEC (ΔvTEC) is neither Gaussian nor Laplace. Finally, the ΔSTECr is studied with GNSS observations. The distribution of ΔSTECr is more consistent with Laplace than Gaussian, indicating that the noise of the GNSS observations should not be modeled as IID Gaussian distribution.

New Methodology for Shoreline Extraction Using Optical and Radar (SAR) Satellite Imagery

Coastal environments are dynamic ecosystems, constantly subject to erosion/accretion processes. Erosional trends have unfortunately been intensifying for decades due to anthropic factors and an accelerated sea level rise might exacerbate the problem. It is crucial to preserve these areas for safeguarding not only coastal ecosystems and cultural heritage, but also the population living there. In this context, monitoring coastal areas is essential and geomatics techniques, especially satellite remote sensing imagery, might prove very advantageous. In this paper, a semi-automatic methodology to extract shorelines from SAR (Synthetic Aperture Radar) Sentinel-1 and optical Sentinel-2 satellite images was developed. An experimental algorithm, called J-Net Dynamic, was tested in two pilot sites. The semi-automatic methodology was validated with GNSS (Global Navigation Satellite System) reference shorelines and demonstrated to be a powerful tool for a robust extraction of the shoreline both from optical and SAR images. The experimental algorithm was able to extract the shoreline closer to the reference with SAR images on the natural beach of Castelldefels and it was demonstrated to be less sensitive to speckle effects than the commonly used Canny Edge Detector. Using the SAR images of the urban beach of Somorrostro, the Canny detector was not able to extract the shoreline, while the new algorithm could do it but with low accuracy because of the noise induced by man-made structures. For further investigation, the Sentinel-2-extracted shorelines were also compared to the ones extracted by a state-of-the-art tool, CoastSat, in the two beaches using both automatic and manual thresholds. The mean errors obtained with J-Net Dynamic were generally higher than the ones from CoastSat using the manual threshold but lower if using the automatic one. The proposed methodology including the J-Net Dynamic algorithm proves to extract the shorelines closer to the reference in most of the cases and offers the great advantage of being able to work with both optical and SAR images. This feature could allow to reduce the time lag between satellite derived shorelines paving the way to an enhanced monitoring and management of coastal areas.

GNSS-R monitoring of soil moisture dynamics in areas of severe drought: example of Dahra in the Sahelian climatic zone (Senegal)

ABSTRACT With population growth, water will increase in the following decades tremendously. The optimization of water allocation for agriculture requires accurate soil moisture (SM) monitoring. Recent Global Navigation Satellite System Reflectometry (GNSS-R) studies take advantage of continuously emitted navigation signals by the Global Navigation Satellite System (GNSS) constellations to retrieve spatiotemporal soil moisture changes for soil with high clay content. It presents the advantage of sensing a whole surface around a reference GNSS antenna. This article focuses on sandy SM monitoring in the driest condition observed in the study field of Dahra, (Senegal). The area consists of 95% sand and in situ volumetric soil moisture (VSM) range from ~3% to ~5% durinf the dry to the rainy season. Unfortunately, the GNSS signals’ waves penetrated deep into the soil during the dry period and strongly reduced the accuracy of GNSS reflectometry (GNSS-R) surface moisture measurements. However, we obtain VSM estimate at low/medium penetration depth. The correlation reaches 0.9 with VSM error lower than 0.16% for the 5–10-cm-depth probes and achieves excellent temporal monitoring to benefit from the antenna heights directly correlated to spatial resolution. The SM measurement models in our research are potentially valuable tools that contribute to the planning of sustainable agriculture, especially in countries often affected by drought.

Multi-GNSS positioning for landslide monitoring: a case study at the Recica landslide

Global Navigation Satellite System (GNSS) positioning has characteristics of simple operation, high efficiency and high precision technique for landslide surface monitoring. In recent years, finalization of modern GNSS systems Galileo and BeiDou has brought a possibility of multi-GNSS positioning. The paper focuses on evaluation of possible benefits of multi-GNSS constellations in landslide monitoring. While simulating observational conditions of selected Recica landslide in the Czech Republic, one-month data from well-established permanent GNSS reference stations were processed. Besides various constellation combinations, differential and Precise Point Positioning techniques, observation data lengths and observation sampling intervals were evaluated. Based on the results, using a combination of GPS and GLONASS, or GPS, GLONASS and Galileo systems can be recommended, together with a static differential technique and observation periods for data collection exceeding eight hours. In the last step, data from GNSS repetitive campaigns realized at the Recica landslide during two years were processed with optimal setup and obtained displacement results were compared to standard geotechnical measurements.

IMPACT OF GNSS SIGNAL OUTAGE ON EOPS USING FORWARD KALMAN FILTER AND SMOOTHING ALGORITHM

Abstract. The global navigation satellite system (GNSS) has been playing the principal role in positioning applications in past decades. Position robustness degrades with a standalone receiver due to GNSS signal outage in mobile mapping systems. The GNSS and inertial measurement unit (IMU) integration is used to solve positioning degradation. This article studies the GNSS/IMU integration processing (i.e., forward Kalman filter (KF) and smoothing algorithm) using a single or a network of nearby GNSS reference stations. In addition, we analyze the impact of simulated GNSS signal outage on exterior orientation parameters (EOPs). The outcomes confirm that the smoothing algorithm works better than the forward KF and improves the accuracy for position and orientation in the case when there is no GNSS signal outage. Also, it improves the position and orientation accuracy by about 95% and 60% when there is a 180 second GNSS signal outage, respectively.

A Method for dSTEC Interpolation: Ionosphere Kernel Estimation Algorithm

Ionospheric structure is important for estimating ionospheric delay for user stations in Global Navigation Satellite System (GNSS). However, most existing parameter estimation methods suffer from challenges due to data inaccuracy and unavailability of limited and sparse scattered data at ground reference stations. The high variability of active low latitude or disturbed ionosphere leads to GNSS signal scintillation. It is critical to capture ionospheric random structure and estimate ionospheric parameter using data of disperse receivers to improve accuracy. This paper proposes a unifying method named Ionosphere Kernel Estimation Algorithm (IKEA) to retrieve the information of ionospheric spatial structure. The proposed model utilities the semi-parametric representation theorem to incorporate prior information and constraints. The multiple kernel technique is adopted firstly to include physical correlations. Additionally, a learning approach is deployed to determine model parameters. The IKEA model has been verified based on simulated and experimental data at active low latitudes from a network of ground GNSS reference stations from all visible GPS and GALILEO satellites. The IKEA model reduces approximately 19.5% and 24.2% of differential Slant Total Electron Content (dSTEC) in the root mean square error with respect to Inverse Distance Weighting (IDW) and the Kriging model during high ionospheric activities. The IKEA architecture has been demonstrated effective to make robust ionospheric estimation, which may be further extended for various GNSS applications and beyond.

Antenna Array-Aided Real-Time Kinematic Using Moving Base Stations

A global navigation satellite systems (GNSS) real-time kinematic (RTK) using multiple base stations is referred as an antenna array-aided RTK. This positioning method more reliably resolves carrier phase integer ambiguities by processing additional measurements provided from multiple GNSS reference stations whose antenna locations are accurately surveyed. In this study, a conventional antenna array-aided RTK is extended to improve RTK performance with multiple moving base stations. Because accurately surveyed antenna coordinates are not available, we propose an antenna array-aided Kalman filter approach to first check the sanity of the fixed integer ambiguities and baselines estimated by moving reference stations and use them as a-priori information in RTK measurement formulations. Our flight test results with three unmanned aerial vehicles confirmed that the proposed Kalman filter formulation outperformed the conventional RTK and a priori antenna array-aided RTK approaches in the aspects of positioning accuracy, time to first fix, and success probability of integer ambiguity fixes.

Assessing the Impact of Positioning Errors in Car-Borne Repeat-Pass SAR Interferometry With a Controlled Rail-Based Experiment

Agile synthetic aperture radar (SAR) platforms such as car-borne and UAV-borne SAR systems require combined inertial navigation systems (INS) and global navigation satellite systems (GNSS) to measure the radar sensor trajectories used for focusing and interferometric processing. Measurement inaccuracies from INS/GNSS systems lead to residual phase errors in the SAR products whose minimisation is crucial to derive accurate topographic and deformation information. In this work, we analyse the impact of residual positioning errors on car-borne repeat-pass SAR interferometry at L-band for different INS/GNSS measurement configurations and for the typical car-borne acquisition geometry. The positioning errors are evaluated both during single SAR acquisitions with long integration times and between different acquisitions as a function of the distance of the radar platform from the GNSS reference stations. We show the reduction of interferometric phase errors achievable by additionally using a GNSS receiver mounted in the vicinity of the SAR platform as compared to remote reference stations of the national network of permanent GNSS receivers. Test results obtained in a controlled setup with a rail-based SAR system equipped with a navigation-grade INS/GNSS system show maximum repeat-pass trajectory errors on the order of 1–2 cm using a local GNSS reference station and up to 10–15 cm using the remote reference stations, leading to azimuth and range phase trends in the interferometric products.

A Hybrid ECOM Model for Solar Radiation Pressure Effect on GPS Reference Orbit Derived by Orbit Fitting Technique

A hybrid ECOM (Empirical CODE Orbit Model) solar radiation pressure (SRP) model, which is termed ECOMC in this work, is proposed for global navigation satellite system (GNSS) orbit modeling. The ECOMC is mainly parameterized by both ECOM1 and ECOM2 models. The GNSS orbit mainly serves as a reference datum not only for its ranging measurement but also for the so-called precise point positioning (PPP) technique. Compared to a complex procedure of orbit determination with real tracking data, the so-called orbit fitting technique simply uses satellite positions from GNSS ephemeris as pseudo-observations to estimate the initial state vector and SRP parameters. The accuracy of the reference orbit is mainly dominated by the SRP, which is usually handled by either ECOM1 or ECOM2. However, the reference orbit derived by ECOM1 produces periodic variations on orbit differences with respect to International GNSS Service (IGS) final orbit for GPS IIR satellites. Such periodic variations are removed from a reference orbit formed using the ECOM2 model, which, however, yields large cross-track orbit errors for the IIR and IIF satellites. Such large errors are attributed to the fact that the ECOM2 intrinsically lacks 1 cycle per revolution (CPR) terms, which stabilize the estimations of the even-order CPR terms in the satellite-Sun direction when the orbit fitting is used. In comparison, a reference orbit constructed with the ECOMC model is free of both the periodic variations from the ECOM1 and the large cross-track orbit errors from the ECOM2. The above improvements from the ECOMC are associated with (1) the even CPR terms removing the periodic variations and (2) the 1 CPR terms compensating for the force mismodeling at ∆u = 90° and 270°, where the ∆u is the argument of the latitude of the satellite with respect to the Sun. The parameter correlation analysis also presents that the direct SRP estimation is sensitive to the 1 and 2 CPR terms in the ECOMC case. In addition, the root-mean-square (RMS) of orbit difference with respect to IGS orbit is improved by ~40%, ~10%, and ~50% in the radial, along-track, and cross-track directions, respectively, when the SRP model is changed from the ECOM2 to the ECOMC. The orbit accuracy is assessed through orbit overlaps at day boundaries. The accuracy improvements of the ECOMC-derived orbit over the ECOM2-derived orbit in the radial, along-track, and cross-track directions are 13.2%, 14.8%, and 42.6% for the IIF satellites and 7.4%, 7.7%, and 35.0% for the IIR satellites. The impact of the reference orbit using the three models on the PPP is assessed. The positioning accuracy derived from the ECOMC is better than that derived from the ECOM1 and ECOM2 by approximately 13% and 20%, respectively. This work may serve as a reference for forming the GNSS reference orbit using the orbit fitting technique with the ECOMC SRP model.

ICESat-2 Marine Bathymetry: Extraction, Refraction Adjustment and Vertical Accuracy as a Function of Depth in Mid-Latitude Temperate Contexts

Nearshore bathymetric data are used in many coastal monitoring applications, but acquisition conditions can be challenging. Shipborne surveys are prone to the risk of grounding in shallow waters, and scheduled airborne surveys often fail to coincide with optimal atmospheric and water conditions. As an alternative, since its launch in 2018, ICESat-2 satellite laser profile altimetry data provide free and readily available data on a 91-day repeat cycle, which may contain incidental bathymetric returns when suitable environmental conditions prevail. In this paper, the vertical accuracy of extracted, refraction-adjusted ICESat-2 nearshore marine bathymetric data is evaluated at four test sites in a Northern hemisphere, temperate latitude location. Multiple ICEsat-2 bathymetric values that occurred in close horizontal proximity to one another were averaged at a spatial scale of 1 m and compared with Multibeam Echosounder bathymetric survey data and Global Navigation Satellite System reference data. Mean absolute errors of less than 0.15 m were observed up to depths of 5 m, with errors of less than 0.24 m (to 6 m), 0.39 m (to 7 m) and 0.52 m (to 10 m). The occurrence of larger bathymetric errors with depth, which increase to 0.54 m at maximum photon depths of 11 m, appears to be primarily related to reduced numbers of geolocated photons with depth. The accuracies achieved up to 6 m suggest that the manual extraction, refraction adjustment and bathymetric filtering steps were effective. Overall, the results suggest that ICESat-2 bathymetric data accuracy may be sufficient to be considered for use in nearshore coastal monitoring applications where shipborne and airborne bathymetric data might otherwise be applied.

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG12 used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG12 index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC.

Development and evaluation of the refined zenith tropospheric delay (ZTD) models

The tropospheric delay is a significant error source in Global Navigation Satellite System (GNSS) positioning and navigation. It is usually projected into zenith direction by using a mapping function. It is particularly important to establish a model that can provide stable and accurate Zenith Tropospheric Delay (ZTD). Because of the regional accuracy difference and poor stability of the traditional ZTD models, this paper proposed two methods to refine the Hopfield and Saastamoinen ZTD models. One is by adding annual and semi-annual periodic terms and the other is based on Back-Propagation Artificial Neutral Network (BP-ANN). Using 5-year data from 2011 to 2015 collected at 67 GNSS reference stations in China and its surrounding regions, the four refined models were constructed. The tropospheric products at these GNSS stations were derived from the site-wise Vienna Mapping Function 1 (VMP1). The spatial analysis, temporal analysis, and residual distribution analysis for all the six models were conducted using the data from 2016 to 2017. The results show that the refined models can effectively improve the accuracy compared with the traditional models. For the Hopfield model, the improvement for the Root Mean Square Error (RMSE) and bias reached 24.5/49.7 and 34.0/52.8 mm, respectively. These values became 8.8/26.7 and 14.7/28.8 mm when the Saastamoinen model was refined using the two methods. This exploration is conducive to GNSS navigation and positioning and GNSS meteorology by providing more accurate tropospheric prior information.

Towards spherical robots for mobile mapping in human made environments

Spherical robots are a format that has not been thoroughly explored for the application of mobile mapping. In contrast to other designs, it provides some unique advantages. Among those is a spherical shell that protects internal sensors and actuators from possible harsh environments, as well as an inherent rotation for locomotion that enables measurements in all directions. Mobile mapping always requires a high-precise pose knowledge to obtain consistent and correct environment maps. This is typically done by a combination of external reference sensors such as Global Navigation Satellite System (GNSS) measurements and inertial measurements or by coarsely estimating the pose using inertial measurement units (IMUs) and post processing the data by registering the different measurements to each other. In indoor environments, the GNSS reference is not an option. Hence many mobile mapping applications turn to the second option. An advantage of indoor environments is that human-made environments usually have a certain structure, such as parallel and perpendicular planes. We propose a registration procedure that exploits this structure by minimizing the distance of measured points to a corresponding plane. Further, we evaluate the procedure on a simulated dataset of an ideal corridor and on an experimentally acquired dataset with different motion profiles. We show that we nearly reproduce the ground truth for the simulated dataset and improve the average point-to-point distance to a reference scan in the experimental dataset. The presented algorithms are required to work completely autonomously.

Study of the Spatiotemporal Characteristics of the Equatorial Ionization Anomaly Using Shipborne Multi-GNSS Data: A Case Analysis (120–150°E, Western Pacific Ocean, 2014–2015)

Ground-based GNSS (Global Navigation Satellite System) reference stations lack the capacity to provide data for ocean regions with sufficient spatial-temporal resolution, limiting the detailed study of the equatorial ionization anomaly (EIA). Thus, this study collected kinematic multi-GNSS data on the ionospheric Total Electron Content (TEC) during two research cruises across the equator in the Western Pacific Ocean in 2014 (31 October–8 November) and 2015 (29 March–6 April). The purpose of the study was to use sufficient spatial–temporal resolution data to conduct a detailed analysis of the diurnal variation of the equatorial ionization anomaly in different seasons. The two-year data collected were used to draw the following conclusions. During the test in 2014, the EIA phenomenon in the Northern and Southern Hemispheres was relatively obvious. The maximum values occurred at local time (LT) 15:00 (~136TECu) and LT22:00 (~107TECu) in the Northern Hemisphere and at LT14:00 (100TECu) and LT22:00 (80TECu) in the Southern Hemisphere. During the test in 2015, the EIA in the Southern Hemisphere reached its maximum level at LT14:00 (~115TECu) and LT20:00 (~85TECu). However, the EIA phenomenon in the Northern Hemisphere was weakened, and a maximum value occurred only at LT 15:00 (~85TECu). The intensity contrast was reversed. The EIA phenomenon manifests a strong hemisphere asymmetry in this region.

Antennas as Precise Sensors for GNSS Reference Stations and High-Performance PNT Applications on Earth and in Space.

Satellite navigation is more and more important in a plethora of very different application fields, ranging from bank transactions to shipping, from autonomous driving to aerial applications, such as commercial avionics as well as unmanned aerial vehicles (UAVs). In very precise positioning, navigation, and timing (PNT) applications, such as in reference stations and precise timing stations, it is important to characterize all errors present in the system in order to account possibly for them or calibrate them out. Antennas play an important role in this respect: they are indeed the “sensor” that capture the signal in space from global navigation satellite systems (GNSS) and thereby strongly contribute to the overall achievable performance. This paper reviews the currently available antenna technologies, targeting specifically reference stations as well as precise GNSS antennas for space applications, and, after introducing performance indicators, summarizes the currently achievable performance. Finally, open research issues are identified, and possible approaches to solve them are discussed.

Computation and Analysis of Spatio-Temporal Variability of Total Electron Content (TEC) of NIGNET CORS over Northern Part of Nigeria

This paper focused on the determination of Spatial and Temporal variability of TEC over parts of Northern Nigeria. It computed and analyzed the year 2011 and 2013 TEC data, obtained from the Nigerian Global Navigation Satellite Systems Reference Network (NIGNET)’s ground-based GPS receivers installed at Six (6) different locations across Northern Nigeria. Results show that, the annual magnitude of TEC of 2013 was higher than that of 2011. The Diurnal analysis indicates TEC variation was minimum at pre-dawn (0-5 TECU) and increased during the day time attaining a maximum in the afternoon and decrease before sunset at all stations. The highest monthly average value was in November (34.49 and 31.39 TECU) and the minimum value in January (13.21 TECU) and September (18.91 TECU) at BKFP Station for 2011 and 2013 respectively. For seasonal variation, the equinox has the highest value of the GPS-TEC, followed by winter and the lowest value in the summer in all the stations for both years. The highest value of GPS-TEC was recorded at equinox in the year 2013 at FUTY (26.66 TECU) and 2011 at BKFP (26.63 TECU) stations respectively. The lowest value, was in summer at CGGT 2011(17.32 TECU) and 2013 (22.58 TECU). The spatial variation shows that TEC varies with latitude more than longitude. Further research is recommended on ionospheric effects and TEC variability during solar minima and solar maxima, as the research focused on ascending phase of solar cycle 24.