Global Navigation Satellite System Data Research Articles

Immature characteristics of the East Anatolian Fault Zone from SAR, GNSS and strong motion data of the 2023 Türkiye–Syria earthquake doublet

On February 6, 2023, an Mw 7.9 earthquake occurred in the western section of the East Anatolia Fault Zone (EAFZ). It was subsequently followed by an Mw 7.7 earthquake on the northern branch of the EAFZ, known as the Sürgü Fault Zone. Coseismic deformation fields were derived for these earthquakes using joint evaluation of near-field strong motion data, Global Navigation Satellite System data, and Synthetic Aperture Radar datasets. The coseismic slip distribution model was determined through the joint kinematic finite fault inversion. The Mw 7.9 earthquake was a left-lateral strike-slip event, predominantly occurring at depths up to 20 km. The earthquake displayed three distinct asperities that correlate well with bends and stepovers along the EAFZ. The Mw 7.7 earthquake also exhibited left-lateral strike-slip characteristics, with a major asperity along the Çardak Fault featuring a maximum slip of approximately 9.5 m at depths between 0 and 24 km. The occurrence of this unanticipated large Mw 7.9 catastrophic seismic event on a fault with low-intermediate structural maturity is noteworthy. In the vicinity of immature faults with multiple jogs, stress tends to accumulate at barrier locations. When the accumulated stress near several adjacent barriers reaches a certain threshold, it may result in the transformation of multiple barriers into asperities, triggering cascading ruptures.

The Prospect of Typhoon Landfall Observation Utilizing L-band InSAR

The cyclonic phenomenon, particularly typhoons within the 100° E to 180° E longitude region, bring significant devastation through intense rainfall and strong winds, causing secondary effects like landslides and floods upon landfall. This results in infrastructural damage and loss of life, ranking storm-related disasters, including typhoons, second only to earthquakes in casualties, emphasizing the need for a robust observation system. While higher income countries benefit from established radar and meteorological stations for continuous monitoring, this study highlights the critical necessity for an additional monitoring system in lower income countries. We investigate the applicability of Interferometric Synthetic Aperture Radar (InSAR) during typhoon landfall, particularly in densely vegetated areas where longer wavelengths like L-band (for instance, the Advanced Land Observing Satellite (ALOS/PALSAR) and ALOS-2/PALSAR-2) are suitable. By analyzing typhoon occurrences and confirming landfall using weather radar from 2007 to 2011 (ALOS/PALSAR observation period) and 2014 to 2023 (ALOS-2/PALSAR-2 observation period), our study successfully identifies the landfall of the Jebi typhoon on September 4, 2018. InSAR processing reveals significant phase gradients linked to dense water vapor during landfall, corroborated by comparisons with sea-level Zenith Wet Delay (ZWD) from Global Navigation Satellite System (GNSS) data and hourly precipitation rates from the Automated Meteorological Data Acquisition System (AMeDAS). The concurrence of InSAR results with ZWD values and rainfall data from meteorological stations validates the potential of L-band InSAR in observing typhoon landfalls.

An Ellipsoidal Plate Motion Model of the Indo‐Australian Tectonic Plate

AbstractWe present a class of “ellipsoidal rotation matrices” which can be used to characterize tectonic plate motion; where geocentric Cartesian coordinates travel along paths tangential to the ellipsoid. We contrast them with conventional Euler pole plate motion models which are more closely aligned with spherical coordinate systems and inherently induce a change in geodetic ellipsoidal height. We demonstrate the use of each in the Indo‐Australian tectonic plate setting, which is known to move approximately 7 cm/year in a north‐northeast direction. Geocentric Datum of Australia 2020 (GDA2020) coordinates are “plate‐fixed” static coordinates obtained using a conventional Euler pole plate motion model to align time dependent coordinates with the 2014 realization of the International Terrestrial Reference Frame at the epoch 2020.0. We show that this Euler pole plate motion model can introduce ellipsoidal height velocities of up to −0.2 mm/year. This is small but systematic, so pertinent for consideration with high accuracy vertical land motion studies using GDA2020 coordinates and velocities. We further investigate the comparative statistical accuracy of conventional Euler pole and the ellipsoidal models with respect to characterizing plate motion captured in high quality Global Navigation Satellite System data.

GNSS and Sentinel-1 InSAR Integrated Long-Term Subsidence Monitoring in Quetta and Mastung Districts, Balochistan, Pakistan

Land subsidence (LS) is a global phenomenon that has affected several urban centres around the world such as Jakarta (Indonesia), Mexico City (Mexico), Xi’an (China), and Iron County (US). It has mainly been attributed to anthropogenic activities such as groundwater exploitation, especially in unconsolidated aquifer systems rich in highly compressible clay and silt. The platy clay minerals rearrange into horizontal stacks after dewatering, leading to a volume change due to overburden. In this study, land subsidence is investigated in the Quetta and Mastung districts, Balochistan, Pakistan, by employing Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite System (GNSS), and groundwater level (GWL) variations. This study represents the first attempt in Pakistan to measure the long-term land subsidence by fusing GNSS and InSAR data for improved validity. InSAR data from the Sentinel-1 satellite in the Ascending (195 scenes) and Descending (183 scenes) tracks were used to analyse LS from December 2015 to December 2022. High-accuracy Trimble NetRS GNSS receivers were used in five locations from October 2006 to December 2022. An average subsidence ranging from 3.2 cm/y to 16 cm/y was recorded in the valley mainly due to the GWL decline and clay-rich sediments, which are prone to compaction due to dewatering. An accumulative LS of 2 m was recorded by the permanent GNSS station in central Quetta from October 2008 to January 2023 (14.2 years). An acceleration in the subsidence from 12 cm/y to 16.6 cm/y after 2016 was recorded by the continuous GNSS. Additionally, the InSAR and GNSS values were compared for validation, resulting in a good correlation between both techniques. A GWL decline ranging from 1.7 m to 6 m was recorded by the piezometers in Quetta during the period 1987–2022. Large- and small-scale fissures were observed in the study area during the surveys. These fissures are responsible for damage to the city’s infrastructure and aquifer contamination. The subsidence profile also agrees with the subsurface lithology. Our assessment concludes that Quetta may be the fastest-sinking metropolitan city in Pakistan. The overexploitation of groundwater and the population explosion may be the main contributing factors for the land subsidence.

Land subsidence monitoring using InSAR technique in the southwestern region of Bangladesh

Deltas like the Ganges-Brahmaputra Delta (GBD) experience significant land subsidence due to overlaying load, consolidation, sediment compaction, regional tectonics, and human activity. The southwestern region of Bangladesh, marked by its low-lying floodplain with compressible sediments, experiences significant land subsidence. However, accurate assessments for these areas are very scarce, often focusing on the point-based measurements. This study employed Interferometric Synthetic Aperture Radar analysis, spanning from 2014 to 2022, to effectively measure land subsidence, and visualize it spatially. In the study area, subsidence rates were found to be ranging from 3 to 20 mm/year, with most areas experiencing 3-10 mm/year. While some areas show higher rates of 10-13 mm/year, very few exceeded 14 mm/year. The average subsidence rate was 5.98 mm/year, demonstrating significant spatial variability, with higher rates in Gopalganj, Patuakhali, Bhola, Barishal, and other areas. The findings were validated based on existing Global navigation satellite system (GNSS) data points, affirming their reliability. Despite minor discrepancies with other measurements at specific points, the overall results align well with existing data. This comprehensive analysis provides a detailed assessment of land subsidence in the southwestern region of Bangladesh, highlighting significant spatial variability and offering valuable insights for future coastal development and risk mitigation strategies.

Three‐dimensionalized feature‐based LiDAR‐visual odometry for online mapping of unpaved road surfaces

AbstractAutomated maintenance and motion planning for unpaved roads are research areas of great interest in the field robotics. Constructing such systems necessitates the development of surface maps for unpaved roads. However, the lack of distinctive features on unpaved roads degrades the performance of light detection and ranging (LiDAR)‐based mapping. To address this problem, this paper proposes three‐dimensionalized feature‐based LiDAR‐visual odometry (TFB odometry) for the online mapping of unpaved road surfaces. TFB odometry introduces a novel interpolation concept to directly estimate the three‐dimensional coordinates of the image features using LiDAR. Furthermore, LiDAR intensity‐weighted motion estimation is proposed to effectively mitigate the effects of dust, which significantly impact the performance of LiDAR. Finally, TFB odometry includes pose graph optimization to efficiently fuse global navigation satellite system data and poses estimated from motion estimation. Through field experiments on unpaved roads, TFB odometry demonstrated successful online full mapping and outperformed other simultaneous localization and mapping methods. Additionally, it demonstrated remarkable performance in accurately mapping road surface anomalies, even in dusty regions.

A method for correcting InSAR interferogram errors using GNSS data and the K-means algorithm

Correcting interferometric synthetic aperture radar (InSAR) interferograms using Global Navigation Satellite System (GNSS) data can effectively improve their accuracy. However, most of the existing correction methods utilize the difference between GNSS and InSAR data for surface fitting; these methods can effectively correct overall long-wavelength errors, but they are insufficient for multiple medium-wavelength errors in localized areas. Based on this, we propose a method for correcting InSAR interferograms using GNSS data and the K-means spatial clustering algorithm, which is capable of obtaining correction information with high accuracy, thus improving the overall and localized area error correction effects and contributing to obtaining high-precision InSAR deformation time series. In an application involving the Central Valley of Southern California (CVSC), the experimental results show that the proposed correction method can effectively compensate for the deficiency of surface fitting in capturing error details and suppress the effect of low-quality interferograms. At the nine GNSS validation sites that are not included in the modeling process, the errors in the ascending track 137A and descending track 144D are mostly less than 15 mm, and the average root mean square error values are 11.8 mm and 8.0 mm, respectively. Overall, the correction method not only realizes effective interferogram error correction, but also has the advantages of high accuracy, high efficiency, ease of promotion, and can effectively address large-scale and high-precision deformation monitoring scenarios.Graphical

Determination of total electron content in the ionosphere over the territory of the Republic of Belarus based on global navigation satellite systems data

We present the results of experimental studies of electron content in the ionosphere over the territory of the Republic of Belarus based on data from global navigation satellite systems. The results of measurements of the precise positioning system of the Republic of Belarus and navigation data of GPS satellites in RINEX format were used as input data. Expressions for calculation of the total electron content using the two-frequency method and a combination of measurements by phase and code delays are given. Algorithms for eliminating cycle slip and determining differential code biases are used. Examples of calculating the vertical electron content over the Republic of Belarus at different moments of time are demonstrated. The obtained results are reasonable to use in monitoring of the ionosphere in order to provide reliable operation of radio systems, detection of ionospheric anomalies of natural and artificial origin, as well as forecasting of natural phenomena on their basis.

A statistical method for the attribution of change‐points in segmented Integrated Water Vapor difference time series

AbstractMany segmentation or change‐point detection methods for homogenizing climate time series compare candidate station data with reference data to eliminate common climate signals and more efficiently detect spurious, non‐climatic changes. One drawback is that it is difficult to decide whether the detected change‐point is due to the candidate series or to the reference. A so‐called attribution procedure is typically applied in a post‐processing step for each detected change‐point. This article describes a new statistical method for the attribution of change‐points detected in Global Navigation Satellite System (GNSS) minus reanalysis series of integrated water vapour. It requires at least one nearby station with similar GNSS and reanalysis data. Six series of differences are formed from the four base series (BS) and are tested for a significant jump at the time of the change‐point detected in the candidate station. The six test results are analysed with a statistical predictive rule to attribute the change‐point to one, or several, of the four BS. Original aspects of our method are: (1) the significance test, which is based on a generalized linear regression approach, taking both heteroscedasticity and autocorrelation into account; (2) the predictive rule, which uses a machine learning method and is constructed from the test results obtained with the real data by using a resampling strategy. Four popular machine learning methods have been compared using cross‐validation and the best one was applied to a real data set (49 main stations with 114 change‐points). The results depend on the choice of the test significance level and the aggregation method combining the prediction results when several nearby stations are available. We find that 62% of the change‐points are attributed to GNSS, 19% to the reanalysis, and 10% are due to coincident detections.

Versatile Modeling Of Deformation (VMOD) Inversion Framework: Application to 20 Years of Observations at Westdahl Volcano and Fisher Caldera, Alaska, US

AbstractWe developed an open source, extensible Python‐based framework, that we call the Versatile Modeling Of Deformation (VMOD), for forward and inverse modeling of crustal deformation sources. VMOD abstracts from specific source model implementations, data types and inversion methods. We implement the most common geodetic source models which can be combined to model and analyze multi‐source deformation. VMOD supports Global Navigation Satellite System (GNSS), InSAR, electronic distance measurement, Leveling and tilt data. To infer source characteristics from observations, VMOD implements non‐linear least squares and Markov Chain Monte‐Carlo Bayesian inversions, including joint inversions using different sources of data. VMOD's structure allows for easy integration of new geodetic models, data types, and inversion strategies. We benchmark the forward models against other published results and the inversion approaches against other implementations. We apply VMOD to analyze deformation at Unimak Island, Alaska, observed with continuous and campaign GNSS, and ascending and descending InSAR time series generated from Sentinel‐1 satellite radar acquisitions. These data show an inflation pattern at Westdahl volcano and subsidence at Fisher Caldera. We use VMOD to test a range of source models by jointly inverting the GNSS and InSAR data sets. Our final model simultaneously constrains the parameters of two sources. Our results reveal a depressurizing spheroid under Fisher Caldera ∼4–6 km deep, contracting at a rate of ∼2–3 Mm3/yr, and a pressurizing spherical source underneath Westdahl volcano ∼6–8 km deep, inflating at ∼5 Mm3/yr. This and past applications of VMOD to volcanic unrest benefit from an extensible framework which supports jointly inversions of data sets for parameters of easily composable multi‐source models.

Assessment of ERA5 derived zenith tropospheric delay data over East African region

The Global Navigation Satellite Systems (GNSS) provide valuable data for computing accurate and reliable Zenith Tropospheric Delay (ZTD) products used in GNSS precise positioning, climate, and meteorological studies. However, inconsistent GNSS data streaming in the East African region, caused by poor internet connectivity, equipment failure, and electrical power outages, results in data gaps that pose an impact on the applications of the ZTD products. To ensure continuous ZTD data availability, alternative approaches are necessary to fill the gaps. This paper evaluates the accuracy and feasibility of computing ZTD over East Africa using the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Re-Analysis (ERA5) model. The ERA5 ZTD is compared with ZTD data from 13 GNSS stations for the years 2013 to 2020 from the Nevada Geodetic Laboratory (NGL) products. Seven statistical evaluation metrics, including mean bias (MnB), root mean squared error (RMSE), Pearson correlation coefficient (R), coefficient of determination (R2), refined index of agreement (IA), Nash–Sutcliffe coefficient of efficiency (NSE), and Taylor skill score (TSS) were used to determine the agreement between ERA5 and GNSS tropospheric products. The results indicate that ERA5ZTD products match well with GNSS ZTD products, with an average MnB of −0.80 mm, RMSE of 8.94 mm, R of 0.976, R2 of 0.952, IA of 0.986, TSS of 0.975, and NSE of 0.945. The high accuracy and stability of ERA5 data in computing ZTD make it an excellent alternative source of ZTD data to augment GNSS ZTD products for positioning and meteorological applications in the East African region.

Integrated PSInSAR and GNSS for 3D Displacement in the Wudongde Area

The major limitation of persistent scatterer interferometric synthetic aperture radar (PSInSAR) is that it detects only one- or two-dimensional displacements, such as those in the line of sight (LOS) and azimuth directions, by repeat-pass SAR observations. Three-dimensional (3D) displacement reflects the actual sliding surface and failure mechanism of a slope. To transform LOS deformation into a reliable 3D displacement, a new approach for obtaining the 3D displacement is proposed herein based on the slope deformation (Dslope). First, the deformation value calculated using the Global Navigation Satellite System (GNSS) as a constraint is used to eliminate the residual deformation of PSInSAR. Then, Dslope is obtained from the relationship between DLOS and the slope angle extracted from the digital elevation model (DEM). Finally, according to the geometric relationship between Dslope and DLOS, a novel approach for calculating 3D displacement is proposed. When comparing the 3D displacement extracted by the proposed method and that from GNSS data in Jinpingzi landslide, the root-mean-square error (RMSE) values were ±2.0 mm, ±2.8 mm, and ±2.6 mm in the vertical, north, and east directions, respectively. The proposed method shows high accuracy in 3D displacement calculation, which can help to determine the failure mechanism of a landslide. This method can be widely used in landslide monitoring in wide areas.

Python toolbox for android GNSS raw data to RINEX conversion

Global navigation satellite system (GNSS) data collected from Android devices have gained increasing importance in various applications, ranging from geospatial positioning to environmental monitoring. However, the lack of standardized tools for converting Android GNSS raw data into receiver independent exchange (RINEX) format poses a significant challenge for researchers and practitioners. In response to this need, we present a comprehensive Python toolbox designed to streamline the conversion process and enhance the usability of Android GNSS data. The proposed toolbox leverages Python’s versatility to provide a user-friendly interface for converting Android GNSS raw data into the widely adopted RINEX format. Key features include robust data parsing algorithms, support for multiple GNSS constellations, and compatibility with diverse Android device configurations. Furthermore, the toolbox’s open-source nature encourages community collaboration and allows for continual improvement and adaptation to emerging GNSS technologies. We anticipate that this Python toolbox will serve as a valuable resource for researchers and practitioners working with Android GNSS data, facilitating standardized data interchange and promoting reproducibility in GNSS-based studies.

Analysis of GNSS-ZTD retrieval using dual-frequency raw observations

The Global Navigation Satellite System (GNSS) is promising technology for zenith tropospheric delay (ZTD) retrieval. With the emergence of new multi-GNSS frequencies and maturity of precise point positioning ambiguity resolution (PPP-AR) technology, opportunities and challenges have arisen in high-precision GNSS-ZTD retrieval. However, certain strategies have usually been adopted, such as the traditional dual-frequency combination of American global positioning system (GPS) technology, for ZTD retrieval, but the influence of AR on ZTD retrieval has been ignored. To address this issue, in this study, the GNSS-ZTD retrieval performance of all existing dual-frequency combinations was first comprehensively evaluated by raw observations. Then, the influences of AR and system combinations were analysed with popular dual-frequency combinations. Finally, La Niña occurred during the GNSS data period considered in this article, and the effect on GNSS-ZTD retrieval was examined. The results of this study could not only provide a reference for GNSS-ZTD retrieval but also provide insight into the relationship between La Niña occurrence and GNSS-ZTD retrieval performance.

New Concept of Smart UAS-GCP: A Tool for Precise Positioning in Remote-Sensing Applications

Today, ground control points (GCPs) represent indispensable tools for products’ georeferencing in all the techniques concerning remote sensing (RS), particularly in monitoring activities from unmanned aircraft system (UAS) platforms. This work introduces an innovative tool, smart GCPs, which combines different georeferencing procedures, offering a range of advantages. It can serve three fundamental purposes concurrently: (1) as a drone takeoff platform; (2) as a base station, allowing the acquisition of raw global navigation satellite system (GNSS) data for post-processed kinematic (PPK) surveys or by providing real-time GNSS corrections for precision positioning; (3) as a rover in the network real-time kinematic (NRTK) mode, establishing its position in real time with centimetric precision. The prototype has undergone testing in a dedicated study area, yielding good results for all three geodetic correction techniques: PPK, RTK, and GCP, achieving centimeter-level accuracy. Nowadays, this versatile prototype represents a unique external instrument, which is also easily transportable and able to connect to the GNSS RING network, obtaining real-time positioning corrections for a wide range of applications that require precise positioning. This capability is essential for environmental applications that require a multitemporal UAS-based study. When the real-time RING data are accessible to the scientific community operating in RS surveying, this work could be a helpful guide for researchers approaching such investigations.

High temporal resolution quasi-global landscape soil freeze–thaw map from spaceborne GNSS-R technology and SMAP radiometer measurements

Landscape freeze–thaw (F/T) state parameters are an integral part of research on terrestrial hydrological processes, vegetation growth dynamics, and terrestrial–atmospheric trace gas exchange. Therefore, the development of large-scale, continuous, and rapid F/T observation records is essential. This study proposes a scheme to effectively retrieve quasi-global daily soil F/T states from spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations. This method takes into account the GNSS-R surface reflectivity and its influence factor. Specifically, the method employed surface reflectivity derived from Cyclone Global Navigation Satellite System (CYGNSS) data from 2018 to 2020, along with vegetation opacity data from Soil Moisture Active Passive (SMAP) as input layers in random forests for F/T retrieval. This approach provides F/T results on a quasi-global scale, does not rely on continuous F/T observations, and employs the least ancillary data. To validate the retrieval results, SMAP F/T data over the same period and temperature data from more than 100 ground stations were also used as reference. Results showed that the F/T retrieval accuracy of CYGNSS was 95.5% and 82.0% with respect to the SMAP data and data from independent in situ stations, respectively. Given the high temporal coverage of the CYGNSS F/T results, the temporal coverage improvement is 23.3% compared with the actual SMAP observations on a daily scale. Furthermore, as gap-filling data complemented SMAP, CYGNSS data can effectively improve the SMAP F/T sample interval (accuracy improvement of 26.0%). More importantly, the F/T accuracy of the CYGNSS and SMAP combination based on monthly performance is 85.1%, i.e., 20.5% better than that of the existing SMAP daily F/T product. Overall, the quasi-global daily F/T results obtained from CYGNSS and SMAP measurements can complement existing databases with well-defined accuracy.

Multidimensional visualization analysis based on large-scale GNSS data

Abstract With the deployment of global navigation systems such as GPS, GLONASS, GALILEO, and BeiDou, and the construction of the global observation network, global navigation satellite system (GNSS) data have greatly expanded. Traditional relational databases and file systems can no longer efficiently store and process GNSS data. In addition, multidimensional, in-depth visual analysis methods for such data are lacking. In this paper, we provide a solution and implement a GNSS data visualization system based on MapV and cesium running on the Hadoop platform, which can effectively solve the problems of the lack of storage and computing resources for these massive multisource heterogeneous data. The proposed system provides multidimensional presentations to show how the space and ground segments of the entire system are organized by analyzing GNSS data.

GPS/BDS triple-frequency cycle slip detection and repair based on moving window global search method

Cycle slip detection and repair are crucial steps in achieving high accuracy in Global Navigation Satellite System (GNSS) data processing. The use of Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS) triple frequency observations allows for more accurate detection and repair of cycle slips compared to single or dual frequency. This study presents a moving window global search method by selecting three sets of combined coefficients to construct geometry-free (GF) models to minimize the influence of the ionosphere, using a moving window to update the standard deviation of cycle slip estimation, applying the "3 σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upsigma$$\\end{document}" criterion to constrain the range, and utilizing a global search method to detect and repair triple-frequency cycle slips. Through five sets of 1 Hz GNSS data experiments, the results demonstrate the effectiveness of this method in determining the position and size of triple-frequency cycle slips while avoiding multi-value problems. The detection success rate for GPS ranges from 98.0 to 100.0%, while BDS ranges from 92.0 to 100.0%. On average, GPS achieves a detection rate of 99.2%, and BDS reaches 96.7%, which is 0.8% and 1.8% higher than the direct rounding method, respectively. Compared to existing methods, it is also effective for the vast majority of small cycle slips within 2 cycles.

INS aided spoofing detection of high dynamic GNSS receiver for launch vehicle applications: A loosely coupled approach

Launch vehicle applications require highly accurate and reliable navigation systems that can withstand the high dynamic conditions of the launch and flight phases. GNSS-aided INS (Inertial Navigation System) is the most popular solution for launch vehicle navigation. However, the accuracy of Global Navigation Satellite System (GNSS) receivers can be compromised by malicious spoofing attacks, which can result in significant safety risks and mission failure. This paper presents a method for identifying and mitigating the spoofing attacks on Global Navigation Satellite System (GNSS) receivers using the independent and simultaneous measurements of the Inertial Navigation System (INS). A state space approach is attempted in this paper with an extended Kalman Filter for identifying the spoofed signals. The well-established method of integrating INS and GNSS data for navigation accuracy improvement is taken as the baseline of this algorithm. The proposed method is an extension of a loosely coupled integration algorithm already established for fusing INS and GNSS data. The extended Kalman filter estimated position and velocity of the receiver is used along with the satellite position and velocity computed from ephemeris to find out the range and range rate of each of the satellites to the receiver. The difference between the estimated range and range rate against the measured range and range rate is considered as filter residual. The filter residuals are taken as the parameter to identify the spoofing. The threshold of filter residuals for range and range rates is the critical parameter that decides the false isolation and missing detection. The threshold is decided based on the environment in which the receiver is operated, the dynamics expected, and the noise model. The algorithms envelopment and simulation studies are done. The simulation tests are completed using a GNSS constellation simulator with spoofed signals. The INS and GNSS error model, states selection, Kalman filter design, algorithm details, simulation tests, simulation results, advantages and disadvantages of the new algorithm, and future work are covered in this paper.

Assessment of the three representative empirical models for zenith tropospheric delay (ZTD) using the CMONOC data

The precise correction of atmospheric zenith tropospheric delay (ZTD) is significant for the Global Navigation Satellite System (GNSS) performance regarding positioning accuracy and convergence time. In the past decades, many empirical ZTD models based on whether the gridded or scattered ZTD products have been proposed and widely used in the GNSS positioning applications. But there is no comprehensive evaluation of these models for the whole China region, which features complicated topography and climate. In this study, we completely assess the typical empirical models, the IGGtropSH model (gridded, non-meteorology), the SHAtropE model (scattered, non-meteorology), and the GPT3 model (gridded, meteorology) using the Crustal Movement Observation Network of China (CMONOC) network. In general, the results show that the three models share consistent performance with RMSE/bias of 37.45/1.63, 37.13/2.20, and 38.27/1.34 mm for the GPT3, SHAtropE and IGGtropSH model, respectively. However, the models had a distinct performance regarding geographical distribution, elevation, seasonal variations, and daily variation. In the southeastern region of China, RMSE values are around 50 mm, which are much higher than that in the western region, approximately 20 mm. The SHAtropE model exhibits better performance for areas with large variations in elevation. The GPT3 model and the IGGtropSH model are more stable across different months, and the SHAtropE model based on the GNSS data exhibits superior performance across various UTC epochs.