Ahead of Publication / Just Accepted

Radio Frequency Interference (RFI) makes it very difficult for Global Navigation Satellite Systems (GNSS) signals to work properly and remains safe. This makes positioning less accurate and reliable. This study looks at the current methods for finding and reducing RFI, with a focus on the IRNSS S-band and L5-band, because it covers the whole world. In this, how the chirp interference affects signal tracking by measuring the Carrier-to-Noise Ratio ( C / N 0 ) using in-phase and quadrature-phase correlator outputs under both statistical and estimated settings. The narrowband-wideband power ratio (NWPR) approach boosts effective C / N 0 by 11.37 dB-Hz for the S-band signal and 12.48 dB-Hz for the L5-band signal, and compares it to the statistical C / N 0 value and the Signal to Noise variance estimator C / N 0 .

The analysis of long-term sea level signal fluctuations and their systematic forecasting showcase the value and implications not only for oceanographic, climate monitoring and related studies but also, on socio-economic aspects of life. Such changes affect significantly the stability in natural processes functioning as well as the usage of nearshore infrastructures; and therefore, interrelate to human activities in coastal areas. This study contributes to the forecasting of sea level variation employing emerging Deep Learning techniques in the form of Recurrent Neural Networks, particularly Long Short-Term Memory (LSTM). Several combinations of data were tested exhaustively to identify the most suitable setup and examine the accuracy depending on available datasets. Univariate, multivariate and multivariate to univariate LSTMs were formed and hyperparameter tuning took place exhibiting RMSE values around 4–5 cm. Also, the effect of seasonality and trend to LSTM forecast was investigated thoroughly leading to performance improvement after eliminating it from the network. The combination of the above networks introduced the “fill-in and predict” technique leading to RMSE slightly over 1 cm for the multivariate to univariate LSTM network. The forecast results were validated against more than two years of historical data.

In the modern era, deriving accurate geoid models is crucial for various engineering and geoscience applications. The geoid facilitates the transformation of ellipsoidal heights from Global Navigation Satellite System (GNSS) measurements to orthometric heights and aids in determining geostrophic ocean surface currents. This study assesses different spectral methods, including two-dimensional Fast Fourier Transform (FFT), multiband spherical FFT, and one-dimensional FFT, for evaluating the Stokes’ function in gedsaata and Stokes’ kernel. The geoid modeling process is divided into three major stages: gravity data assessment, regularization of scattered gravity datasets onto the topographical surface, and geoid computation under the Remove-Compute-Restore (RCR) mode with Residual Terrain Model (RTM) reduction. Results indicate that the multiband spherical FFT method outperforms others, leading to the adoption of the Institut Teknologi Bandung-Central Java 2024 (ITBCJ24) geoid model, which achieves a root mean squared error (RMSE) of 0.068 m relative to GNSS-leveling observations and a relative accuracy of 4.409 ppm. The study also highlights the significance of gravity data coverage and distribution by comparing geoid models derived from terrestrial, airborne, and altimetric-derived gravity observations against the model relying solely on terrestrial gravity observations. Although the latter performs similarly at validation points, prominent discrepancies in geoidal height outside the validation region highlight the importance of uniform and dense gravity data coverage. Finally, the ITBCJ24 geoid model is compared to existing geoid models in Indonesia, showcasing its superiority with RMSE values of 0.117 m and 0.166 m for Indonesia’s regional geoid model of INAGEOID2020 and Earth Geopotential Model 2008 (EGM2008) model, respectively.

Soil Moisture Content (SMC) is a crucial variable influencing Earth’s environmental processes, including the water cycle, energy balance, and carbon cycle. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a powerful tool for monitoring SMC. The Cyclone Global Navigation Satellite System (CYGNSS), a mission consisting of eight microsatellites launched by NASA in 2016, utilizes GNSS-R to provide high-temporal-resolution soil moisture estimates. This study conducts a comparative analysis of CYGNSS-derived SMC data against SMAP, SMOS, CEDA, and ERA5 datasets over Egypt and Africa from 2017 to 2021. Using tolerance and interpolation techniques, the analysis revealed strong correlations between CYGNSS and the other datasets, with overall correlation values of 0.78 (SMAP), 0.64 (SMOS), 0.74 (CEDA), and 0.80 (ERA5). The corresponding RMSE values were 0.022, 0.018, 0.027, and 0.035 cm 3 /cm 3 , respectively. Interpolation results showed correlations of 0.76, 0.47, 0.54, and 0.39, with RMSE values of 0.03, 0.065, 0.045, and 0.076 cm 3 /cm 3 , respectively. Additionally, the study analyzes the spatiotemporal distribution of soil moisture across Egypt and Africa, revealing regional variations and trends over the study period. These findings demonstrate CYGNSS’s effectiveness in capturing soil moisture variations, particularly in Egypt.

Land subsidence is commonly observed along the coastal regions of Java Island, particularly in the northern part of Central Java. This phenomenon often contributes to the intensification of coastal flooding. Subsidence rates are influenced by multiple factors, which might also relate to tectonic movements associated with active fault structures. In this study, we investigate the contribution of active fault-induced deformation to land subsidence in northern coastal cities of Central Java. We modeled the surface displacements resulting from active faults using the elastic half-space approach. We analyzed geodetic data from Continuously Operating Reference Stations (CORS) situated along the northern coast, covering the period from 2015 to 2019. The analysis revealed notable land subsidence at Pekalongan and Tegal, with annual rates of −122.54 mm/year and −11.04 mm/year, respectively. Statistical testing of annual displacement rates indicated that fault-induced surface movement was significant only at Semarang City. Conversely, the displacement observed at CORS with pronounced subsidence was not statistically significant. These findings suggest that active fault-related displacements do not play a dominant role in driving land subsidence in the study area. Instead, the magnitude of their influence appears to depend on the proximity of the faults to observation sites and the relative level of earthquake potential of the fault.

Although there are several factors could affect the accuracy performance of Global Geopotential Models (GGMs), the current research investigates if the order of a model represents the key aspect influencing its accuracy. Fourteen GGMs, with variable orders, have been selected to be tested over 1,100 Global Navigation Satellite Systems (GNSS)/Levelling stations in Egypt. The performance of global models has been analyzed against that of a national geoid model. Additionally, the performance of GGMs over variable topography in Egypt has been investigated too. Based on the available data and the attained findings, it has been found that the low-order GGMs produced mean geoid undulations very far from that of the national model. Over the available checkpoints, it has been recognized that the accuracy of high-order GGMs is less than ± 0.276 m. However, a particular medium-order model produced a better performance with a standard deviation equals ± 0.193 m. Other medium-order models resulted in accuracy varies between ± 0.238 m and ± 0.371 m. In addition, the models produced slightly better performance over high-elevation topography in Egypt. Furthermore, statistical analysis demonstrates that the correlation between model’s order and accuracy equals −0.38 which indicates that such an association is weak. The regression coefficient of determination has been estimated as 0.14 which concludes that the regression is feeble since only 14 % of the accuracy variations are affected by the model’s order. Such remarks highlight that there is no significant association between the order and accuracy of GGMs over GNSS/Levelling stations in Egypt.

Since 2008, Ecuador has established a Permanent Geodetic Network (REGME), to support geodetic research in the country. The REGME network plays a critical role in monitoring tectonic activity and crustal deformation throughout Ecuador. However, no comprehensive quality analysis has been conducted for REGME stations. The network has provided data from 70 continuous GPS stations across Ecuador, covering a period of 16 years. Shell scripts were developed on UNIX to manage and process REGME raw data. The data were converted from HATANAKA to RINEX format using CRX2RNX, and the sampling rate was standardized to 30 s using TEQC software. Only GPS signals were selected due to variations in the stations’ capabilities to track different signals. This study establishes a crucial foundation for future research on crustal deformation and geophysical phenomena in the region, offering valuable insights that will enhance both local and global geodetic efforts.

In the last 6 months, 5 eruptions have been reported at the location of Iceland’s volcanoes. In this study using the electron density and temperature data measured by the LP sensor onboard the Swarm satellites (A, B, C), the volcanic ionospheric anomalies around the location and time of the Sundhnúkur volcano have been investigated. Most parameters measured by the three Swarm satellites show striking anomalies on 34 and 8 days before the eruption. Also, in this study, GPS-TEC data were used as a volcanic precursor, and median-interquartile method was implemented to detect hidden and highly nonlinear volcano-ionospheric anomalies. This precursor detected clear anomalies in the time interval of 8 days before the eruption. Also, the values of AOD (Aerosol Optical Depth) parameter indicate abnormal behavior on 9 days prior to the event. To reduce the uncertainty of the detected volcanic anomalies, a confutation analysis was also performed, and no clear anomalies were observed in the same spatial and temporal interval studied, but in 2019. The observed anomalies are consistent with geological and seismic reports. For more detailed analysis, we separately investigated the tracks of the satellites crossing the volcano’s buffer zone. Observed anomalies in the time series analysis were acknowledged. Therefore, the results of this study emphasizes that Swarm satellite data, along with other satellite and field data, can be used to predict, detect and track volcanic activities and it is shown that by comparison with other lithospheric and atmospheric precursors, uncertainty in eruption prediction can be reduced.

Ground subsidence, the gradual sinking of the earth’s surface, poses a significant challenge globally, affecting regions like Midvaal, South Africa. This study employed Persistent Scatterer (PS) and Small Baseline Subset (SBAS) InSAR time-series analysis, continuous Global Navigation Satellite Systems (cGNSS) validation, and Random Forest machine learning to monitor and model ground subsidence using Sentinel-1 data (2019–2021). SBAS InSAR showed higher ground subsidence rates than PS InSAR, with cGNSS validation revealing InSAR limitations. Factor analysis identified NDVI, NDWI, lithology, and elevation as key drivers. The Random Forest model generated a susceptibility map, classifying areas into five vulnerability levels. Results indicate that Midvaal experiences significant ground subsidence, likely due to groundwater extraction for agricultural activities and mining, impacting approximately 3.4 % of the study area (118.85 sq. km). This study provides critical insights for sustainable land management and infrastructure planning in ground subsidence-prone regions.

The advancement of multi-mission satellite altimeters has greatly enhanced the conventional approach of using a tide gauge station to establish a localised hydrographic datum. However, there are still several limitations in continually depending on a small number of sparsely distributed tide gauge stations and wide satellite altimeter track missions, even with its continuous monitoring of ocean data at local and worldwide coverage. Such limitation has left a region farther away from the satellite altimeter track and tide gauge stations without any hydrographic data. Hence, this study aims to develop Malaysia’s Continuous Hydrographic Datum (MyCHD) by combining the satellite altimeter, tide gauge station, and global hydrodynamic model (GHM). In addition, the reliability of MyCHD was also assessed to determine the rate of improvement by incorporating GHM as additional hydrographic data. The research methodology involves collecting data from the Department of Survey and Mapping Malaysia (DSMM) tide gauge stations along Malaysia’s coastline, as well as satellite altimetry data from TOPEX, Jason-1, Jason-2, and GEOSAT Follow-On (GFO) via the Radar Altimeter Database System (RADS). Additionally, Indian Ocean GHM data from Oregon State University (OSU) was also utilised. The tide gauge, satellite altimetry, and GHM datasets encompass 26 years of tidal observations, spanning from 1993 to 2018. All hydrographic data were processed using harmonic analysis in Unified Tidal Analysis and Prediction (UTide) within MATLAB to establish the hydrographic datum. The derived Lowest and Highest Astronomical Tide (LAT and HAT) from tide gauge, satellite altimetry, and GHM data were referred to the Mean Sea Level (MSL) for compatibility in data integration; these were designated as LAT MSL  and HAT MSL , respectively. Then, using ArcGIS software, the LAT MSL  and HAT MSL were interpolated using Inverse Distance Weighting (IDW). In contrast to the integration of tide gauge and satellite altimeter, the statistical assessment showed that the integration of tide gauge, satellite altimeter, and GHM has a better agreement with the DSMM tide gauges, with a Root Mean Square Error (RMSE) of ± 0.671 m for LAT MSL and ± 0.370 m for HAT MSL . In percentage terms, incorporating GHM data with tide gauge and satellite altimeter in establishing MyCHD has significantly improved its reliability by 17 % for LAT MSL and 30 % for HAT MSL respectively. In conclusion, the hydrographic datum is now available at any coordinate along Malaysia’s coast with the establishment of MyCHD (LAT MSL and HAT MSL ).

Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) is a versatile remote sensing technique that utilizes reflected GNSS signals for environmental monitoring. By analyzing interference patterns between direct and reflected GNSS signals, GNSS-IR enables the estimation of surface characteristics such as soil moisture, water level, and snow depth. This study investigates snow accumulation, soil moisture, and water level measurements using GNSS-IR over extended periods, providing insights into climate-related trends. Data from GNSS stations in both the northern and southern hemispheres were analyzed. Snow depth was monitored at two stations over a decade, revealing fluctuations in accumulation and highlighting potential impacts of climate change. Soil moisture was analyzed at a grass-covered site, with comparisons before and after vegetation correction. Water levels were measured using GNSS-IR data near a coastal stream, capturing clear tidal signatures. Results show consistent reflector height variations corresponding to snow depth changes, soil moisture dynamics, and water level oscillations (including tides), demonstrating the robustness of GNSS-IR for environmental monitoring of snow, soil moisture, and sea level.

The Persistent Scatterers Interferometry (PSI) method enables displacement estimation with millimeter accuracy. However, the uncertain positioning of Point Scatterers (PS) makes it difficult to associate them with real objects in space and hampers the interpretation of the results. This article proposes a methodology to enhance the accuracy of PS positions. The methodology successfully establishes links between PS and real objects by associating them with the most likely candidate points extracted from Airborne Laser Scanning (ALS) point clouds. The selection process for suitable candidates is based on ALS analysis of return number, classification, and geometric features determined by neighborhood analysis. The linking process involves determining global transformation parameters for PSs using the Iterative Closest Point (ICP) algorithm. Then, the nearest neighbor search within the error ellipsoid of the PS positions is performed. Tests conducted demonstrated that this method allows for linking more than 80 % and 65 % of the PS derived from Sentinel-1 and TerraSAR-X mission data, respectively, in both ascending and descending geometries. To validate the obtained results, in addition to the quantitative assessment, a qualitative analysis is performed based on a developed 3D visualization module showing all stages of the proposed methodology.

Monitoring Precipitable Water Vapor (PWV) with Global Navigation Satellite System (GNSS) receivers is becoming a more viable and effective solution for meteorological applications such as rainfall prediction, climate change, and weather forecasting etc. This study investigates the statistical relationship between GNSS-derived Precipitable Water Vapor (PWV) and rainfall variability across five Indian cities, Bangalore, Hyderabad, Guwahati, Thiruvananthapuram, and Madurai, representing diverse geographic and climatic zones. GNSS-derived PWV data from the GPS Aided Geo Augmented Navigation (GAGAN) network and rainfall data from the Indian Meteorological Department (IMD) were considered from March 2013 to February 2014. Data has been analyzed based on seasonal trends and the dynamic interaction between atmospheric moisture and precipitation. The results represented significant spatial and temporal differences in rainfall patterns, with Thiruvananthapuram recording the highest monthly rainfall (143.90 mm in October 2013) due to dual monsoon exposure, and Madurai the lowest (88.5 mm in October 2013) owing to its rain-shadow location. Guwahati shows strong orographic influence and early monsoon peaks (121.30 mm in June 2013), while inland cities like Bangalore and Hyderabad experience delayed rainfall peaks during post-monsoon periods. Average monthly rainfall and PWV values were found to be closely linked to geographical factors such as elevation, topography, and proximity to coastlines. A clear correlation was observed between PWV and subsequent rainfall events; sharp increases in PWV exceeding 30 mm daily or rising at rates above 0.5 mm/h often preceded heavy precipitation.

Accurate estimation of GNSS station velocities requires a precise characterization of stochastic noise in coordinate time series. This study evaluates stochastic models for estimating site velocities using weekly network and daily PPP GNSS solutions from 74 stations in the stable South American mid-plate. The functional model incorporated seasonal components, while the stochastic model was based on noise variance estimates. The Non-Negative Least Squares Variance Component Estimation method was applied to estimate noise amplitudes, classifying noise as a combination of white and colored components. Additionally, the spectral index was refined using Maximum Likelihood Estimation. Results show that most time series are best described by a combination of white and flicker noise, with differences in spectral properties between weekly and daily solutions. The impact of these models on velocity uncertainties was assessed, showing that neglecting an appropriate noise model can lead to overestimated uncertainties. These findings contribute to improving GNSS-based velocity estimations for geodynamic studies.

Given the multitude of global gravity field model (GGMs) at our disposal, evaluating them against independent data is indispensable for delineating their quality and ascertaining their accuracy in Nigeria regions. In this study, we evaluated five combined GGMs (specifically SGG-UGM-2, XGM2019e_2159, GECO, EIGEN-6C4, and EGM2008) over Nigeria. Our evaluation is conducted using geoid undulations (mainly in FCT-Abuja) and free-air gravity anomalies (covering the whole country, Nigeria). The results showed that the differences between observed and GGMs implied geoid undulations at 20 GNSS/levelling points over FCT-Abuja (small area) revealed that XGM2019e_2159 fit better with standard deviation (SD) of 11.9 cm over the study area compared to other four evaluated combined GGMs. On the other hand, SD of the differences between observed and GGMs implied free-air gravity anomalies over Nigeria showed that XGM2019e_2159 performs slightly better than the other four evaluated combined GGMs with an SD of 7.04 mGal. These results further indicate a good prospect for the development of a precise gravimetric geoid model over Nigeria using XGM2019e_2159 model. However, more GNSS/levelling stations will be required for fittings of the gravimetric geoid model to the national vertical datum of Nigeria.

This study utilizes data from the FORMOSAT-3/Constellation Observing System for Meteorology, Ionosphere, and Climate radio occultation (COSMIC-RO) observations and ground-based Scintillation Network Decision Aid (SCINDA) GNSS recordings at four longitudinal stations situated in the vicinity of the magnetic equator for probing the characteristics of the ionospheric F and E region irregularities in the equatorial region. We utilize the amplitude scintillation index (S4) derived from these datasets to understand the morphology of nighttime and daytime plasma irregularities in the equatorial ionosphere. By combining ground-based data with a limb-viewing geometry from space, valuable complementary information has been obtained on the nighttime/daytime scintillation occurrences and their morphological associations with the equatorial irregularities in the F (equatorial plasma bubbles, EPB) and E (sporadic E layer; Es) region ionosphere. The occurrence of amplitude scintillation retrieved from COSMIC RO data was calculated to analyze its diurnal and seasonal variations. By conducting a statistical analysis on a dataset in 2013, we observed that the occurrence of ionospheric scintillation, as measured by both techniques, displayed similar variations. In this study, it is observed that scintillation in daytime is associated with Es and is predominantly seen over the Asian sector whilst nighttime scintillation associated with plasma bubbles is most frequently observed in the African sector. These results emphasize the need for RO profiles from active missions to serve as effective tools for monitoring scintillation events both regionally and globally.

The integration of multiple sensors, such as cameras and LiDARs, is increasingly employed in vehicle navigation and 3D environmental mapping. Accurate multi-sensor data fusion relies heavily on the precise estimation of extrinsic parameters, which define the geometric transformations between sensors. However, when low-cost sensors are used, systematic errors in their intrinsic characteristics often introduce inaccuracies in subsequent applications. To address this challenge, we investigate the impact of intrinsic calibration of a low-cost LiDAR on the accuracy of its extrinsic calibration parameters. Our study evaluates a generalized intrinsic calibration approach that assumes a single scale factor and bias for the LiDAR’s laser rays, in contrast to a detailed model where individual scale factors and biases are estimated for each ray. While the generalized approach exhibits noticeable deviations from the systematic parameters of some rays, it has a substantial positive impact on the estimated extrinsic parameters of the LiDAR, leading to improved mounting accuracy. This improvement highlights the critical role of intrinsic calibration in achieving reliable extrinsic calibration for multi-sensor systems. To ensure robust extrinsic calibration, we decouple the intrinsic and extrinsic calibration processes, thereby providing rectified input data for extrinsic calibration. Furthermore, we present an extrinsic calibration approach for a multi-sensor system comprising an inertial measurement unit, two cameras, and a LiDAR, where the cameras and LiDAR share only a narrow overlapping field of view, resulting in minimal shared information. By employing a high-accuracy reference instrument, we address the limited overlap issue and validate both the extrinsic calibration results and the impact of the LiDAR’s intrinsic calibration. Our findings demonstrate that untreated LiDAR intrinsic errors lead to significant deviations in the extrinsic calibration parameters, underscoring that intrinsic calibration is not merely a preliminary step but a vital factor in ensuring accurate extrinsic calibration in multi-sensor systems.

The determination of accurate geoid, ellipsoidal and orthometric heights is essential for applications related to surveying engineering, geodetic and geoscience research. The International Association of Geodesy (IAG) defined the International Height Reference System (IHRS) as the conventional gravity field-related global height system. The realization of the IHRS to establish an International Height Reference Frame (IHRF) and its temporal variations, is an ongoing geodetic task. The objective of this research is to assess temporal variations of geoid, ellipsoidal and orthometric heights at proposed IHRF sites using space geodetic data, namely Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO), Global Navigation Satellite Systems (GNSS), Very Long Baseline Interferometry (VLBI), Satellite Laser Ranging (SLR), Doppler Orbitography and Radio-positioning Integrated by Satellite (DORIS) and hydrological model. GRACE/GRACE-FO-based and GLDAS-based Global Geopotential Models were utilized to determine temporal variations of geoid height (Δ N ) and time-dependent changes of ellipsoidal height (Δ h ) at proposed IHRF sites. Moreover, Δ h at proposed IHRF sites were also obtained from GNSS, VLBI, SLR and DORIS solutions. Temporal variations of orthometric height (Δ H ) at proposed IHRF sites were determined by combining Δ N and Δ h . Results reveal that Δ N , Δ h , and Δ H at proposed IHRF sites are not negligible as they can reach several centimetres in terms of seasonal variations and at the and 1 cm level annually. These Δ N , Δ h , and Δ H would be required for the modernization and unification of international/national vertical datums in addition to contemporary requirements for precise height measurements.

Terrestrial Laser Scanning (TLS) is increasingly used in geomonitoring for 3D displacement analysis. However, assessing the sensitivity of the implemented monitoring strategy, which is crucial for correctly interpreting observations and detecting deformations, is challenging due to the impact of complex spatial correlations and other influencing factors. Traditional methods for sensitivity analysis often assume uncorrelated measurements, leading to biased and overestimated sensitivity. This can lead to suboptimal choices of monitoring strategy, false expectations, and errors in displacement detection. This study introduces a new method for quantifying the uncertainty of spatially aggregated (averaged) TLS-based displacement estimates in geomonitoring by empirically locally sampling such aggregated values (ELSA). The method implicitly accounts for the mentioned spatial correlations and their local variations, therefore, providing a more realistic sensitivity quantification. Validation using real-world datasets and simulated displacements demonstrates the method’s ability to provide a realistic uncertainty estimate and, subsequently, a good sensitivity estimate, realized herein by means of the Minimal Detectable Bias (MDB). Finally, we investigated several data preprocessing steps and demonstrated their effectiveness in enhancing both sensitivity and the quality of uncertainty estimates.

Ionospheric scintillation causes a serious threat to the accuracy, integrity, and reliability of Global Navigation Satellite System (GNSS) receivers, leading to significant amplitude fading, and rapid phase fluctuations. The GNSS receivers with traditional carrier-tracking loops may sometimes fail to retain signal integrity during high scintillation scenarios, leading to degraded positioning performance or even loss of lock and can seriously affect the dynamic systems and safety of life-critical applications. We present an Autoregressive Adaptive Kalman Filter (AR-KF) based tracking approach to enhance the robustness of the GNSS receivers under scintillation conditions. The adaptive KF algorithm in the proposed framework dynamically adjusts the time-varying characteristics of the parameters in its tracking loop by leveraging insights gained from a sophisticated and high rate software defined radio-frequency (RF) system collocated by a specialized ionospheric monitoring GNSS receiver whereas the succeeding AR algorithm plays a crucial role by intelligently predicting and refining the carrier phase estimates, thereby mitigating the distortions and enhancing the GNSS tracking loop resiliences. The efficacy of the proposed filtering algorithm is evaluated using real-time GNSS signals affected by weak, moderate, and strong scintillation conditions. Experimental results demonstrate that the combined approach in this work can ensure relatively robust navigation and timing performance by effectively tracking even in severe scintillation conditions. The analysis revealed minimal intervention of the algorithm during weak scintillation but intelligently improving the performance in moderate, and strong scintillation scenarios. The findings from this study suggest the effectiveness of integrating adaptive KF and AR architecture through advanced signal processing methodologies into GNSS receivers, offering a consistent solution for navigational applications during strong scintillation conditions.

Global Navigation Satellite Systems (GNSS) offer a modern, efficient alternative to traditional leveling methods, with advantages in accuracy, cost-effectiveness, and adaptability to diverse terrains. This study examines GNSS leveling techniques using Thailand’s Continuously Operating Reference Station (CORS) network. A multi-baseline network approach is proposed to enhance redundancy, enabling weighted least squares adjustment for both absolute and relative methods. The performance of the multi-baseline approach is compared to the single-baseline method to assess its benefits. Results indicate that the multi-baseline absolute method achieves an accuracy of approximately 4 cm, surpassing the single-baseline absolute method and demonstrating a robustness to orthometric height errors caused by reference station anomalies. On the other hand, the relative method shows degraded performance for both single and multi-baseline approaches due to biases in antenna eccentricity, which necessitate reducing the ellipsoidal height at the marker point to the Permanent Bench Mark (PBM) level.