Band 18, Heft 3

The ionospheric delay significantly impacts GNSS positioning accuracy. To address this, an Artificial Neural Network (ANN) was developed using the high-quality COSMIC-2 ionospheric profile dataset to predict the Total Electron Content (TEC). ANNs are adept at addressing both linear and nonlinear challenges. For this research, eight distinct ANNs were cultivated. These ANNs were designed with the following inputs Year, Month, Day, Hour, Latitude, and Longitude. Along with solar and geomagnetic parameters such as the F10.7 solar radio flux index, the Sunspot Number (SSN), the Kp index, and the ap index. The goal was to discern the most influential parameters on ionosphere prediction. After pinpointing these key parameters, an enhanced model utilizing a pioneering technique of a secondary ANN was employed with the main ANN to predict TEC values for events in 2023. The study’s findings indicate that solar parameters markedly enhance the model’s accuracy. Notably, the augmented model featuring a prelude secondary network achieved a stellar correlation coefficient of 0.99. Distributionally, 41 % of predictions aligned within the (−1≤ ΔTEC ≤1) TECU spectrum, 28 % nestled within the (1< ΔTEC ≤2) and (−2≤ ΔTEC <−1) TECU ambit, while a substantial 30 % spanned the broader (2< ΔTEC ≤5) and (−5≤ ΔTEC <−2) TECU range. In essence, this research underscores the potential of incorporating solar parameters and advanced neural network techniques to refine ionospheric delay predictions, thus boosting GNSS positioning precision.

The study introduces the concept of regional GPS orbit determination, whereby GPS satellite positions are determined using GPS measurements from locally or regional distributed stations. The importance and characteristics of regional GPS orbit are briefly discussed. The technique used to determine the regional GPS satellite position is coined Inverse Single Point Positioning (ISPP). Code-based pseudorange is used and improved using residual correction model. Two designs of station distribution are selected in this study, which only cover stations in Malaysia and stations situated 8000 km from a reference point in Malaysia. The root-mean-squared-error (RMSE) of ISPP when compared against final ephemeris were 660.65 m and 27.61 m, while the 3D RMSE of positioning were 1.612 m and 1.324 m for the first and second designs, respectively, lower than the accuracy of broadcast ephemeris. Three parameters are identified as factors affecting accuracy of ISPP, namely geometry of station distribution, nature of measurement used, and technique of orbit determination. Further research will be required to fully realize a functional regional GPS orbit.

Following the Regulation of the Head of the Geospatial Information Agency (BIG) No. 13 of 2021, geoid is used as the Vertical Geospatial Reference System in Indonesia. Applications using the geoid as an ideal reference require a much higher accuracy and resolution than the geoid obtained from models derived solely from satellite data. The Indonesian Geospatial Information Agency considers the geoid ideal if it has reached an accuracy of better than 15 cm. Recent studies have combined satellite and other gravimetric data to produce a combined geoid model with increased resolution. Gravimetric data obtained from measurements close to the Earth’s surface, such as airborne and terrestrial gravity data, are particularly attractive because the high-frequency portion of the signal is more apparent and can contribute to the medium to high frequencies of the gravity field. This study models the geoid over Bali Island by combining satellite, airborne and terrestrial gravity data. Calculations were performed using Least Square Collocation (LSC) and Remove-Compute-Restore (RCR) techniques. The gravimetric geoid model was tested against the geometric geoid profile calculated from a GNSS/Levelling survey. The geoid, calculated by combining the GOCO06S satellite gravity model, the GGMplus gravity model and airborne gravity data at an average flight altitude of 4100 m produces a standard deviation of 14.46 cm along the 125 km validation path. After also adding terrestrial gravity data, the standard deviation increased to 16.37 cm. By comparison, the results of the validation of the geoid model from GOCO06S and INAGEOIDV2 with geometric geoids have standard deviation values of 79.56 cm and 16.40 cm, respectively. However, the results of the statistical tests are strongly influenced by the data quality used as validation, in this case, GNSS/Levelling. It is shown that the GNSS/Levelling data over Bali contains significant errors, which have been reduced based on an analysis of geometric vertical deflections. A geometric geoid profile with higher accuracy is required to test the accuracy of the gravimetric geoid models more reliably.

Land Surface Temperature (LST) and Water Vapor Pressure (WVP) contour maps can be produced using cameras aboard satellites, for instance, under the name “Remote Sensing (RS)”. Satellite image observations should be verified before using based on a reliable data. Global Navigation Satellite System Radio Occultation (GNSS-RO) method is observing accurate Earth atmosphere parameters continuously. In the present research, LST and WVP differences between Landsat 8 (LC08), Sentinel-3 (S3), and MODIS (Terra and Aqua) images and GNSS-RO are assessed in Egypt depending on the satellites operating periods and data availability during the years from 2015 to 2020. Statistically, S3 and Terra have insignificant differences with RO temperature with an average bias of 3.48 °C and 1.47 °C, respectively, but LC08 and Aqua have significant differences with it. For WVP, Aqua and LC08 have insignificant differences with an average bias of 0.02 kg/m 2 and 2.31 kg/m 2 , respectively, but S3 and Terra have significant differences with RO observations. When comparing LC08 LST data to other satellites, it was found that there were insignificant differences between LC08 and S3 as well as Terra. However, significant differences were observed when comparing LC08 LST data to Aqua. Additionally, significant differences were noted when comparing LC08 WVP data to other satellites. In response to these differences, improvement models have been developed to enhance the estimation of terrestrial data through remote sensing, particularly for satellites that exhibited significant disparities when compared to reference observations (RO).

This research evaluates the performance of the first and second-generation satellite-based augmentation system (SBAS) GAGAN and BDSBAS services in Thailand. The study initially analyses GNSS observations from 40 local continuously operating reference stations (CORS) over the past 12 months in 2022, providing initial horizontal and vertical accuracies at 2.03 and 3.66 m respectively with the single point positioning (SPP) mode. The positioning accuracies are 2.27 m horizontally and 2.54 m vertically as of GAGAN, while 2.94 m horizontally and 3.90 m vertically as of BDSBAS with the first-generation system. Since the 1st generation SBAS performance is affected by the ionosphere, especially in the equatorial and auroral regions, the ionosphere-free combination is applied in the SPP algorithm as well as the 2nd generation SBAS with the Dual-Frequency Multi-Constellation (DFMC) capable receivers for BDSBAS only. The SPP accuracies are 1.51 m horizontally and 3.26 m vertically, where the BDSBAS results are 2.16 m horizontally and 4.28 m vertically. Demonstrated results show that the positioning accuracy cannot be improved significantly when applying the 1st generation GAGAN and BDSBAS systems and the 2nd generation BDSBAS system in Thailand due to the low number of common satellites available, especially when using the SBAS outside their ground tracking network; therefore, it is expected to apply the GNSS observation and computed satellite error correction from the regional ground tracking network to enhance the performance of the 2nd generation SBAS. The positioning accuracy result could be achieved at sub-metre level, which will greatly benefit high-accuracy applications such as air, land, and sea navigation in the region.

The country of Libya, situated on the Mediterranean fault zone, has a distinctive geodynamic regime due to the interplay between the Eurasian and African plates, which governs its tectonic evolution. In addition to its seismological significance, Libya is characterized by numerous subsidence and slope instabilities in regions with steep terrain. These geological phenomena have significant consequences for the built environment, as they pose an immediate danger to entire towns and essential infrastructure. Furthermore, infrequent weather phenomena, such as intense precipitation and thunderstorms, when coupled with the geological characteristics of some regions and the presence of seismically active terrain, have the potential to trigger landslide and land subsidence, resulting in significant harm to vital infrastructure. The current study utilizes the DInSAR technology to identify distinct subsidence occurrences that were induced by intense precipitation in coastal regions of Libya, specifically in Derna. These areas experienced significant flooding resulting in collapses during September 2023. A total of six pairs of co-event Interferometric Synthetic Aperture Radar (SAR) were utilized to generate displacement maps in the vertical, north-east, and north-west directions for the purpose of analysing the deformations. The aforementioned activities are conducted via Sentinel-1A images, which is freely accessible through the Copernicus program. Additionally, flood-prone zones were defined using Sentinel-1 GRD imagery. The Interferometric processing revealed multiple areas of subsidence. Subsidence rates of up to −14 cm were found in Derna city’s urban cores after flood. The findings suggest that subsidence may have an effect on the flood-proneness of the region of Derna City as Ground subsidence also occurred in the period immediately before the earthquake, at a rate of −14 cm.

The relevance of the problem examined is the need for research to ensure the safety and efficiency of construction and operation of utility networks, in light of the rapid development and growth of urban infrastructure. The purpose of this research is to explore and evaluate the application of topographic-geodetic studies in the context of the design and construction of utilities. The methods used include analytical method, classification method, functional method, statistical method, synthesis method. At the preparatory stage of works the schedule of all stages of project implementation was developed, coordinate and height systems accepted in the territory of the settlement, topographic and geodetic materials, general plans, detailed planning plans, and schemes of settlement development were established. Executive surveys, the territory of Aksukent settlement, and other materials and documents identified in the process of preparatory works were collected and analysed. Using an unmanned aerial vehicle (UAV) data of plan-altitude substantiation and aerial photography were obtained, orthophoto map in WGS84 and MSC coordinate system of the Aksukent settlement territory was obtained. To update the data on existing points of the state geodetic network, complexes of geodetic measurements and calculations were performed and parameters (key) of transformation between WGS84 and WCS coordinate systems were obtained. In the course of the field works, deciphering work was performed based on the obtained orthophoto. The inventory of engineering networks was conducted, which included engineering-geodetic surveys of underground and surface engineering networks (sewerage, water supply, gas supply, heating mains), and engineering-geodetic surveys of overhead and underground cable power lines. The results of the survey of the traces of engineering communications on the territory of Aksukent village were agreed with the balance holders of networks JSC “Turkestan Regional Telecommunications Directorate Kazakhtelecom”, JSC “KazTransGas Aimak”, LLP “Sairam Tazalyk”, LLP “Ontustik Zharyk Transit”. The research is of practical significance, as it contributes to the accurate determination of network parameters, identification of potential problem areas and ensuring effective planning and management of engineering projects.

Analysis of huge data and detection of scintillation events by human visualization is expensive and time consuming process and also unfeasible in real time. In this paper, classical approaches namely Hard, Semi-Hard and Manual annotation rules are used for detection of the scintillations. For this, one week data is acquired from Septentrio PoLaRx5S GNSS scintillation monitoring Receiver corresponding to various constellations. Seven constellations namely GPS, GLONASS, Galileo, SBAS, BeiDou, QZSS and IRNSS-L5 signals during pre-sunset and post sunset hours are considered. The occurrence of scintillations due to pre-sunset and post sunset period by using hard and semi hard detection rules are analysed. It is observed that the occurrence of scintillations is more in post-sunset hours as compared to pre-sunset hours in all constellations. The performances of Semi Hard and Hard detection rules are compared with manual annotation by using confusion matrices statistical parameters namely accuracy, misclassification and precision. Identified scintillation signals coming from the least and worst affected directions. These results would be useful for early detection of scintillation without human inspection of scintillation events.

The Lombok earthquake in August 2018 triggered a sequential rupture with doublet earthquake up to Mw 6.9. This tectonic activity occurred near the main earthquake due to the decay of residual energy from one event to another. This activity is suspected to be a post-seismic deformation process such as afterslip and viscoelastic. In this paper, we conducted a study to determine the deformation pattern. Each of these processes can be investigated by extracting InSAR observational data. Time series from Sentinel-1 SAR is processed using LiCSBAS as data observation and then compare with the model based on exponential and logarithmic functions. The results of combined logarithmic and exponential fitting suggest the Lombok multi-event earthquakes were influenced by seismic activity from dual-releasing residual energy comprises of afterslip and viscoelastic as a dual mechanism with long duration rather than single mechanism.

This investigation estimates a regional Mean Sea Surface (MSS) model, named SY21MSS, over the eastern Mediterranean Sea using satellite altimetry data from nine Exact Repeat Missions (ERM) and two Geodetic Missions (GM). Two interpolation methods, Least Squares Collocation (LSC) and Ordinary Kriging (OK), were employed, and statistical metrics were applied to assess their performance within a 15 km buffer from the coast. The comparison between LSC and OK techniques in the context of regional MSS modeling primarily centers on the covariance functions used by these methods. Furthermore, generalized cross-validation results indicate that OK outperforms LSC in this region. Consequently, the study recommends adopting the Kriging-based model for calculating regional MSS models in this region due to its superior performance. The investigation further explored the disparities between estimated regional MSS models and the global model DTU18MSS, highlighting a pronounced similarity between OK-SY21MSS and DTU18MSS, as evidenced by a lesser standard deviation (SD) difference compared to LSC-SY21MSS. The practical implications of this research underscore the importance of selecting an appropriate interpolation technique based on data characteristics and study area specifics. While both LSC and OK techniques are deemed viable for MSS modeling, the study emphasizes the superior performance of OK, particularly concerning covariance functions. Additionally, the results emphasize caution when applying global models in regions with significant local variations.

In this study, a tailored gravity-field model is developed to fit and recover local terrestrial gravity data by integrating gravity from global gravity-field models, residual gravity derived from topographic data and observed terrestrial gravity over two study sites in Africa (Cameroon and South Africa). During the modelling phase, two-thirds of the terrestrial gravity data is utilised, reserving the remaining one-third for validation purposes. Additionally, an independent validation is conducted by comparing computed quasigeoid models (derived from tailored gravity data) with height anomalies from GPS/levelling data over the two study sites. The accuracy of the tailored gravity model in reproducing observed gravity data is noteworthy, with a ±8.9 mGal accuracy for the study site in South Africa at 2867 test points and a ±10.4 mGal accuracy for the study site in Cameroon at 637 test points. Comparing height anomalies from GPS/levelling with the SATGQG quasigeoid model (developed from tailored gravity data) and the recent CDSM09A quasigeoid model at 11 GPS/levelling data points reveals comparable accuracies of ±0.10 m and ±0.05 m, for SATGQG and CDSM09A, respectively for the site in South Africa. For the Cameroon site, the differences between height anomalies from GPS/levelling and the CTGQG quasigeoid model (developed from tailored gravity data), along with the recent CGM20 quasigeoid model at 38 GPS/levelling data points, show practically equal accuracies of ±0.15 m for CTGQG and ±0.11 m for CGM20. These findings underscore the potential of tailored gravity-field model in developing accurate quasigeoid models, particularly in regions with limited gravity data coverage. This approach holds promise for gravity recovery and precise geoid modelling in developing countries and regions with insufficient coverage of terrestrial gravity data.

In the maritime environment, multipath interference exhibits a significantly pronounced influence, resulting in GNSS system performance degradation. Enhancing system performance involves the identification and elimination of NLOS signals. This study focuses on the analysis of multipath data induced by sea waves, collected off the coast of Kakinada Sea (16.98° N, 82.29° E), to categorize signals as Line-of-Sight (LOS), Non-Line-of-Sight (NLOS) and Multipath (MP). A machine learning (ML) approach is employed to identify the presence of LOS, NLOS and MP signals in a coastal environment, both before and after the advancement of tidal waves. In the proposed approach, ML algorithms are trained using 3 key parameters namely elevation angle, signal strength and pseudorange residuals. This study involves the implementation of 14 prominent supervised classification algorithms and their accuracies and computational times are compared. The results due to GPS (L1) and IRNSS (L5 and S1) are considered. Decision Tree and its ensemble function AdaBoost, exhibited exceptional performance of accuracy (99.99 %) and computational time (0.45 s).

Sri Lanka is still in need of a well-defined local geoid model. This geoidal void has made present-day Global Navigation Satellite System (GNSS) surveys heavily dependent on Global Geopotential Models (GGMs) for height determination. Further, in many instances, the accuracy of GGMs have shown drawbacks in elevation determination over Sri Lanka. Therefore, the study focused on developing a hybrid geoid model (HGM) for Sri Lanka by integrating the available GGMs. Five high-resolution (2190°) GGMs; EGM2008, EIGEN-6C4, GECO, XGM2019e-2159, and SGG-UGM2 were employed to extract GGM-derived geoid undulation for 21 Fundamental Benchmarks (FBMs). The residuals (geoid height deviation) were calculated relative to the observed geoid undulation using GNSS/leveling on the FBMs. The data set was clustered based on topography, and residuals were adjusted using weighted least squares adjustment (LSA). The uneven distribution of the FBMs promotes topography-based clustering. EIGEN-6C4 is found to be the robust GGM for Sri Lanka to develop a hybrid approach, with a 0.001 m RMS value of estimated residuals in LSA. The resulting HGM was interpolated at 1 arc-second grid resolution (30 m × 30 m) using the Inverse Distance Weighted Interpolation. Regression lines were generated for the interpolated HGM with respect to the interpolated observed geoid undulation for 9 transects along the parallel passing through Mount Pedro and for the 16 transects along the meridian. The coefficient of determination on both lines is 0.999. HGM generated by EIGEN-6C4 has shown reliable RMS gradient and intercept values of 8.860078 × 10 −9 and 0.0039239, respectively, in first-order polynomial fitting.

This study presents an Android-based cooperative positioning (CP) architecture to improve the GNSS positioning performance on mobile devices. SBAS (Satellite Based Augmentation System) augmentation increases positioning accuracies significantly by sharing corrections between SBAS-enabled and non-capable devices via wireless connection or using a central server. The Indian GAGAN (GPS Aided GEO Augmented Navigation) is employed and assessed in the experiments. If GAGAN corrections are applied, all three chosen mobile devices showed a positioning accuracy improvement of around 95 %. The average 2D RSME was reduced from 75.23 to 1.35 m for the single-frequency GNSS smartphone Xiaomi Redmi Note 8 and from 33.25 to 1.62 m for the dual-frequency Google Pixel 4. As expected, the third GIS mapping device, Stonex S70 tablet, showed the highest performance, achieving sub-meter positioning accuracies. Thus, the experiment has proven the suitability of GAGAN augmentation for mobile devices, providing positive insight for further development of the CP architecture.

The accuracy of the results of satellite measurements is influenced by many factors. One of them is the multipath phenomenon resulting from reflections of the satellite signal, mainly from objects in the vicinity of the GNSS antenna. The multipath remains a domain source of ranging errors in satellite positioning. Despite the groups of multipath mitigation methods, it is impossible to totally eliminate the influence of this error on the measurement results. This error has two main effects in the case of carrier phase differential positioning. First, the multipath increases the initial search space for correct ambiguities. Secondly, the accuracy of the vector solution between the reference station and the rover receiver is affected. The authors of this article analyzed the presence of multipath in the Polish network of permanent GNSS stations ASG-EUPOS. Data from the year 2021 were used for the analysis. Two computational strategies were adopted to determine the multipath. The pseudorange multipath observable combination (MP) for L1, L2 and L5 signals was used for code measurements. In the case of the carrier phase, multipath analyses of double-differenced L1, L2, and L5 carriers between neighbouring stations were performed. Based on the research, the average multipath level for the Polish GNSS reference stations network was determined. Stations where the levels of particular combinations exceed the assumed values were successively determined. Finally, multipath models in the form of sidereal maps were created. Based on these models, six stations were identified and recommended for further analysis concerning the impact of multipath on GNSS measurements.