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Accurate estimation of atmospheric delays and reliable ambiguity resolution (AR) are major challenges in implementing network real-time kinematic (NRTK) positioning technology. Previous studies on NRTK positioning have focused on using a double-differenced (DD) model, which restricts the flexibility of interpolated atmospheric delays and lacks a rigorous strategy for GLONASS AR due to the frequency-division multiple access (FDMA) regime. In this contribution, the ionosphere-weighted single-differenced RTK (IW-SD-RTK) model is proposed to obtain more accurate and flexible interpolation of SD atmospheric delays. Then, the influence of the short-term variation in the receiver hardware biases on the estimation of SD ionospheric delays is analyzed by comparing the common clock (CC) and decoupled clock (DC) IW-SD-RTK models, where the receiver hardware biases are treated as constant in the CC-IW-SD-RTK model and as white noise in the DC-IW-SD-RTK model. Furthermore, to improve the compatibility and interoperability of code division multiple access (CDMA) and FDMA in NRTK positioning, a novel integer-estimable (IE) FDMA model is employed for GLONASS AR. The results demonstrate that the DC-IW-SD-RTK model obtains more accurate and stable ionospheric delays than the CC-IW-SD-RTK model, and the DC-IW-SD-RTK model also outperforms the CC-IW-SD-RTK model in NRTK user positioning performance. Additionally, compared to standalone GPS, the incorporation of GPS and GLONASS observations improves the ADOP by approximately 42%, 45%, and 49% and the user 3-dimensional positioning precision by approximately 10%, 34%, and 19% for small-, medium-, and large-scale networks, respectively. The mean time to first fix (TTFF) is also improved by 36%. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between geomagnetic activities and ionospheric scintillations, focusing on how solar and geomagnetic parameters influence ionospheric disturbances across varying time frames and latitudes. Utilizing indices such as Kp, Dst, sunspot numbers, and the F10.7 solar flux, we elucidate the dynamics influencing ionospheric conditions, which are vital for the reliability of satellite communications, particularly in low-latitude regions. Our analysis demonstrates a clear correlation between periods of high solar activity and increased geomagnetic disturbances, leading to heightened ionospheric scintillations, such as occurred during the solar maximum of 2015. In contrast, 2020—a solar minimum period—exhibited fewer disturbances, highlighting the impact of solar activity levels on ionospheric conditions. Innovatively employing ConvGRU networks, this research advances the modeling and prediction of ionospheric scintillations by integrating deep learning techniques suited to the spatiotemporal complexities of ionospheric data. The ConvGRU model effectively captures both temporal sequences and spatial distributions, offering enhanced accuracy in depicting ionospheric scintillation patterns crucial for satellite-based navigation and communication systems. Ground-based GNSS data from 121 stations across South America, collected during 2015 and 2020, provide a robust dataset for our analysis. The study highlights the influence of the solar cycle on ionospheric scintillations, with the years of maximum and minimum solar activity showing significant differences in scintillation intensity and frequency. Our evaluation of the ConvGRU models using statistical parameters demonstrated their potential for reliable ionospheric scintillation prediction. The research underscores the necessity of integrating adaptive mechanisms within models to effectively handle the dynamic nature of ionospheric disturbances influenced by external geomagnetic and solar factors. This study enhances the understanding of ionospheric scintillations and significantly advances predictive modeling capabilities using advanced machine learning techniques. The potential establishment of real-time alert systems for ionospheric disturbances could significantly benefit civilian applications, enhancing the operational efficiency of technologies reliant on accurate ionospheric information. [ABSTRACT FROM AUTHOR]

The regional ionospheric corrections have become one of the critical parts of Precise Point Positioning (PPP)-based Real-Time-Kinematic (RTK) services to achieve fast positioning convergence. Several methods for regional ionospheric corrections supporting PPP-RTK have been developed and implemented over recent years, but little attention is given to the theoretical foundation of existing ionospheric correction methods and their performance comparison to find an optimal method in some sense. The optimality criterion of such methods should not only be based on the precision of the ionospheric correction itself, but also on its broadcasting strategies, and implementation aspects. This contribution studies ionospheric correction generation methods within the best linear unbiased predictor (BLUP) framework. Comparing the accuracy performances of the methods, we demonstrate that the Kriging method with trend, as a special case of BLUP, is the most appropriate method for large-scale networks (above 500 km). A strategy for the evaluation of the uncertainty of the grid-interpolated ionospheric corrections is also developed. In contrast to other empirical methods, this new method is rigorous in the sense that it avoids the underestimation of the uncertainty of predicted ionospheric corrections, especially when reference stations are close to a grid point. [ABSTRACT FROM AUTHOR]

Modelling of the movements at the interface of the Amur and Okhotsk plates within the Sakhalin Island was performed using repeated satellite measurements on the Sakhalin Island and the nearest continental zone for the period of 2016–2021, as well as previously published data. When modelling fault-block kinematics, well-known relations were used to calculate reverse movements for buried rectangular dislocations in an elastic medium, which were implemented in the TDEFNODE software package. In the process of modelling the movements, the measured horizontal components of GNSS (Global Navigation Satellite System) velocities, the boundary and the mutual kinematics of the Amur and Okhotsk plates relative to the North American Plate according to the NNR-MORVEL56 model were used as the input data. This approach revealed persistent deviations in the direction of the simulated displacements of the Earth's surface from the observed ones, which can be explained by the discrepancy between the a priori specified kinematics of the blocks and the observed movements. To eliminate the systematic discrepancy, it was necessary to allow the possibility of updating the mutual kinematics of the blocks. The repeated calculations, with the same input data but in a problem formulation that allowed refinement of block kinematics, led to suppression of systematic discrepancies between the model and measured displacements while retaining the random scatter. The movement parameters of the Amur and Okhotsk plates, which were refined during the modelling, show typical slight differences from the movement parameters of the corresponding large lithospheric plates (the Eurasian and North American plates), from which they are separated into independent blocks in modern constructions. The calculated locking coefficients in the Sakhalin segment of the interplate boundary reach maximal values at depths of 20–30 km. The obtained locking pattern at the plate interface is compared with the sources of the largest earthquakes in the last 30 years in the considered area, viz., Neftegorsk (May 27, 1995) and Uglegorsk (April 4, 2000) earthquakes, which are found to be associated with zones of maximal locking and a high locking gradient, both in the dip and strike directions. [ABSTRACT FROM AUTHOR]

Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas). [ABSTRACT FROM AUTHOR]

Forest environment and topographic obstacles tend to reduce the positioning performance of precise point positioning (PPP) with ambiguity resolution (AR) and may even prevent radio signals from reaching the global navigation satellite systems (GNSS) antenna. In this study, we investigated the positioning performance of PPP-AR in a forest environment in terms of the crown closure ratios, session duration (1-, 2-, 3-, and 6-h), and different satellite constellations [i.e., the global positioning system (GPS)-only and GPS+GLONASS combined satellites]. For this purpose, three GNSS receivers were used to make measurements at three test points in areas with crown closure ratios of 0%, 38%, and 87%. The data were evaluated using the PRIDE PPP-AR software and Canadian Spatial Reference System-PPP (CSRS-PPP). The experiments revealed that the inclusion of the GLONASS observations in the GPS-only solutions did not obviously improve the positioning error and accuracy with closure ratios of 0% and 38%. However, the improvements became more dramatic when the closure ratio increased to 87%. Furthermore, in the horizontal components, an accuracy of 10 cm can be achieved with at least a 2-h session, whereas for the up component, this level of accuracy can only be achieved with a 3-h session. While the PRIDE PPP-AR was able to achieve a 3D positioning performance of 1 cm with the combined GPS+GLONASS satellites, this accuracy level remained at 8 cm in CSRS-PPP. [ABSTRACT FROM AUTHOR]

To accommodate the presence of bounded biases in mixed-integer models, Khodabandeh (2022) extended integer estimation theory by introducing a new admissible integer estimator. The estimator follows the principle of integer least squares estimation and is computed via the integer search method of BEAT. In this contribution, we present the probability distributions of a class of estimators to which the proposed bias-constrained integer least squares estimation belongs. Some important interferometric measuring systems, whose estimation problems can be covered by BEAT, are identified. To show the proposed estimator at work, we apply BEAT to the problem of GLONASS single-differenced (SD) ambiguity resolution. Numerical results of several short-baseline datasets are presented to illustrate why one can achieve more accurate positioning solutions when considering between-receiver SD ambiguity resolution for the cases where carrier phase data are captured on frequency-varying signals with bounded SD receiver phase delays. [ABSTRACT FROM AUTHOR]

Sustainable forest management systems require operational measures to preserve the functional design of forest roads. Frequent road data collection and analysis are essential to support target-oriented and efficient maintenance planning and operations. This study demonstrates an automated solution for monitoring forest road surface deterioration using consumer-grade optical sensors. A YOLOv5 model with StrongSORT tracking was adapted and trained to detect and track potholes in the videos captured by vehicle-mounted cameras. For model training, datasets recorded in diverse geographical regions under different weather conditions were used. The model shows a detection and tracking performance of up to a precision and recall level of 0.79 and 0.58, respectively, with 0.70 mean average precision at an intersection over union (IoU) of at least 0.5. We applied the trained model to a forest road in southern Norway, recorded with a Global Navigation Satellite System (GNSS)−fitted dashcam. GNSS-delivered geographical coordinates at 10 Hz rate were used to geolocate the detected potholes. The geolocation performance over this exemple road stretch of 1 km exhibited a root mean square deviation of about 9.7 m compared to OpenStreetMap. Finally, an exemple road deterioration map was compiled, which can be used for scheduling road maintenance operations. [ABSTRACT FROM AUTHOR]

Currently, it remains a challenge to effectively monitor areas experiencing intense precipitation and the associated atmospheric conditions on a global scale. This challenge arises due to the limitations on both active and passive remote sensing methods. Apart from the lack of observations in remote areas, the quality of some observations deteriorates when heavy precipitation is present, making it difficult to obtain highly accurate measurements of the thermodynamic parameters driving these weather events. However, there is a promising solution in the form of the Global Navigation Satellite System (GNSS) Polarimetric Radio Occultation (PRO) technique. This approach provides a way to assess the large-scale bulk-hydrometeor characteristics of regions with heavy precipitation and the meteorological conditions associated with them. PRO offers vertical profiles of atmospheric variables, including temperature, pressure, water vapor pressure, and information about hydrometeors, all in a single fine-vertical resolution observation. To continue validating the PRO technique, we make use of polarimetric weather data from Next Generation Weather Radars (NEXRAD), focusing on comparing specific differential phase shift ( K d p ) structures to PRO observable differential phase shift ( Δ Φ ). We have seen that PAZ and NEXRAD exhibit a good agreement on the vertical structure of the observable Δ Φ and that their combination could be useful for enhancing our understanding of the microphysics underlying heavy precipitation events. [ABSTRACT FROM AUTHOR]

This article explores the enhancement of Global Navigation Satellite Systems (GNSS) tropospheric tomography by integrating remote sensing data and employing various vertical constraints. Wet refractivity modeling, critical for understanding atmospheric dynamics, has shown promising advancements. Leveraging tropospheric data from the Ocean and Land Color Instrument (OLCI), this research addresses the issue of empty voxels that impede GNSS-based tomography due to satellite and receiver geometries. Incorporating tropospheric data from remote sensing sensors mitigates empty voxels, enhancing retrieval accuracy for tropospheric water vapor. This study evaluates various vertical constraint functions in tropospheric tomography, presenting eight tomography schemes that utilize GNSS and OLCI data, highlighting their capacity to fill empty voxels without relying on empirical horizontal constraints. Results highlight the superiority of using OLCI observations in accuracy. Validation against radiosonde measurements and Weather Research and Forecasting model outputs affirms the reliability of this approach. Integrating OLCI observations with GNSS data reduces the average root mean square error by approximately 27%, with the Gaussian function exhibiting superior vertical constraint performance.

Состояние ионосферы является важным параметром характеризующим динамику Солнечно – Земных связей. Полное электронное содержание ионосферы (ПЭС) является одним из основных параметров её диагностики. Он измеряется по задержке радиосигналов со спутников системы ГНСС на пути до приёмников. Солнечное влияние на ионосферу имеет глобальный характер. Влияние Земли, в силу сложного рельефа поверхности и погодных условий (например грозы), во многом имеет локальную составляющую, меняющуюся во времени и в пространстве. При изучении такого рода возмущений из полного спектра вариаций ПЭС нужно, корректным способом, исключать глобальные, то есть, приходится изучать отличие местных задержек радиосигналов от расчётных глобальных. Это возможно делать в режиме «on — line», используя спутниковые часы. В работе описывается метод измерения локальных вариаций ПЭС в зоне установки «Ковёр» БНО ИЯИ РАН (43.3N, 42.7E) используя стандартные 6-и канальные спутниковые одночастотные часы GPS170PCI. На материале информации с 29.07.2019 по 06.09.2019 производится статистическая оценка коэффициентов связи измеряемого времени исполнения команды, для получения точного времени от часов GPS170PCI, с вариациями ПЭС, определёнными по глобальным картам ионосферы. Выводы сравниваются с результатом калибровки при эффекте от магнитной бури. Отмечается наличие значительных вариаций, коррелирующих с мюонной интенсивностью, регистрируемой на уровне земли. В событии 02.09.2019 имело место троекратное локальное возрастание ПЭС ( 15 мин), начало которого совпало с подземным электрическим разрядом. Форма возмущения ПЭС коррелирует с вариацией электрического тока, протекавшего в реке, рядом с установкой. Анализируются причинно– следственные связи обнаруженных эффектов.

PPP-RTK corrections, aiding GNSS users to achieve single-receiver integer ambiguity-resolved parameter solutions, are often estimated in a recursive manner by a provider. Such recursive, multi-epoch, estimation of the corrections relies on a set of S -basis parameters that are chosen by the provider so as to make the underlying measurement setup solvable. As a consequence, the provider can only estimate estimable forms of the corrections rather than the original corrections themselves. It is the goal of the present contribution to address the consequence of the corrections’ dependency on the provider’s S -basis, showcasing the characteristics of their multi-epoch solutions, thereby identifying potential pitfalls which the PPP-RTK user should avoid when evaluating such solutions. To this end, we develop a simulation platform that allows one to have full control over the properties of PPP-RTK corrections and demonstrate various misleading temporal behaviors that exist when interpreting the multi-epoch solutions of their estimable forms. The roles of the correction latency and time correlation in the multi-epoch user positioning performance are quantified, while the deviation of the user-reported positioning precision description from its user-actual counterpart is measured under a misspecified user stochastic model. [ABSTRACT FROM AUTHOR]

In previous studies on multiple hypothesis solution separations (MHSS) advanced autonomous integrity monitoring (ARAIM) in phase-based GNSS precise point positioning (PPP)-real-time kinematic (RTK), the risk of wrong (or incorrect) ambiguity fixing is generally considered to be negligible after ambiguity validation. However, this unrealistic assumption can introduce risks to PPP-RTK positioning solutions that have strict requirements for accuracy and integrity. In this study, a new integrity monitoring method considering the risks of wrong ambiguity fixing is proposed. The proposed method extends the scope of ARAIM to accommodate a type of pseudo-measurements for integer ambiguity fixing. Then, wrong ambiguity fixing can be treated as a type of fault in the MHSS ARAIM scheme. We test the proposed integrity monitoring method with both simulated and real-world PPP-RTK data sets, demonstrating that this method is conservative and can properly bound the positioning errors. Additionally, we analyze the effect of the probability of wrong ambiguity fixing on PPP-RTK positioning protection levels (PLs). The numerical results show that when the probability of wrong ambiguity fixing is smaller than the magnitude of the integrity budget, the effects on PLs are usually very small. On the contrary, due to the impact of wrong integer ambiguity fixing risks, it can increase the PLs levels of ambiguity-fixed solutions, which however will still be smaller than the PLs of ambiguity-float solutions. [ABSTRACT FROM AUTHOR]

Low Earth Orbit (LEO) constellations have ecently gained tremendous attention in the navigational field due to their arger constellation size, faster geometry variations, and higher signal power evels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric ayers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their ower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects epeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and eflecting the electron content inside the ionosphere. The IAMM model's primary advantage is its ability to be used both online and offline without needing eal-time input parameters, thus making it easy to store in a device's memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS eceivers and Android smartphones. The model's behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant eduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context. [ABSTRACT FROM AUTHOR]

With the continued development of multiple Global Navigation Satellite Systems (GNSS) and the emergence of various frequencies, UnDifferenced and UnCombined (UDUC) data processing has become an increasingly attractive option. In this contribution, we provide an overview of the current status of UDUC GNSS data processing activities in China. These activities encompass the formulation of Precise Point Positioning (PPP) models and PPP-Real-Time Kinematic (PPP-RTK) models for processing single-station and multi-station GNSS data, respectively. Regarding single-station data processing, we discuss the advancements in PPP models, particularly the extension from a single system to multiple systems, and from dual frequencies to single and multiple frequencies. Additionally, we introduce the modified PPP model, which accounts for the time variation of receiver code biases, a departure from the conventional PPP model that typically assumes these biases to be time-constant. In the realm of multi-station PPP-RTK data processing, we introduce the ionosphere-weighted PPP-RTK model, which enhances the model strength by considering the spatial correlation of ionospheric delays. We also review the phase-only PPP-RTK model, designed to mitigate the impact of unmodelled code-related errors. Furthermore, we explore GLONASS PPP-RTK, achieved through the application of the integer-estimable model. For large-scale network data processing, we introduce the all-in-view PPP-RTK model, which alleviates the strict common-view requirement at all receivers. Moreover, we present the decentralized PPP-RTK data processing strategy, designed to improve computational efficiency. Overall, this work highlights the various advancements in UDUC GNSS data processing, providing insights into the state-of-the-art techniques employed in China to achieve precise GNSS applications.

This study investigates the relationship between geomagnetic activities and ionospheric scintillations, focusing on how solar and geomagnetic parameters influence ionospheric disturbances across varying time frames and latitudes. Utilizing indices such as Kp, Dst, sunspot numbers, and the F10.7 solar flux, we elucidate the dynamics influencing ionospheric conditions, which are vital for the reliability of satellite communications, particularly in low-latitude regions. Our analysis demonstrates a clear correlation between periods of high solar activity and increased geomagnetic disturbances, leading to heightened ionospheric scintillations, such as occurred during the solar maximum of 2015. In contrast, 2020—a solar minimum period—exhibited fewer disturbances, highlighting the impact of solar activity levels on ionospheric conditions. Innovatively employing ConvGRU networks, this research advances the modeling and prediction of ionospheric scintillations by integrating deep learning techniques suited to the spatiotemporal complexities of ionospheric data. The ConvGRU model effectively captures both temporal sequences and spatial distributions, offering enhanced accuracy in depicting ionospheric scintillation patterns crucial for satellite-based navigation and communication systems. Ground-based GNSS data from 121 stations across South America, collected during 2015 and 2020, provide a robust dataset for our analysis. The study highlights the influence of the solar cycle on ionospheric scintillations, with the years of maximum and minimum solar activity showing significant differences in scintillation intensity and frequency. Our evaluation of the ConvGRU models using statistical parameters demonstrated their potential for reliable ionospheric scintillation prediction. The research underscores the necessity of integrating adaptive mechanisms within models to effectively handle the dynamic nature of ionospheric disturbances influenced by external geomagnetic and solar factors. This study enhances the understanding of ionospheric scintillations and significantly advances predictive modeling capabilities using advanced machine learning techniques. The potential establishment of real-time alert systems for ionospheric disturbances could significantly benefit civilian applications, enhancing the operational efficiency of technologies reliant on accurate ionospheric information.

Global Navigation Satellite Systems (GNSS) has been the primary positioning solution for Unmanned Aerial Vehicles (UAVs) due to their worldwide coverage, high precision and lightweight receivers. However, GNSS is prone to electromagnetic interference and malicious assaults, including jamming or spoofing because of its low signal-to-noise ratio (SNR). To ensure the continuity and protection of UAV operations, using redundant navigation systems is essential. In recent years, the phased array radio system (PARS) has established itself as a local navigation solution. PARS is robust towards malicious assaults because of an much higher SNR than GNSS regarding directed and encrypted transmission. An essential factor of PARS is that the orientation of the radio antenna at a ground station needs to be precisely determined to obtain the correct positioning of UAVs. This paper presents a method for extending a previously proposed calibration algorithm to estimate the ground antenna orientation with an inertial navigation system (INS) aided by redundant positioning sensors (GNSS, PARS or barometer) using a multiplicative extended Kalman filter (MEKF) so that the calibration can be activated during flights whenever GNSS is available. In other words, the proposed navigation system is essentially an aided-INS which switches between two modes depending on the availability of GNSS: calibration and GNSS aiding mode when GNSS is available (Mode 1) and PARS and barometer aiding mode when GNSS is unavailable (Mode 2). Considering that the navigation system needs to include the effect of Earth’s curvature for a long-distance flight, PARS horizontal measurement and the barometer measurement were treated independently, and the navigation equations were propagated in Earth Centred Earth Fixed (ECEF) frame. The independent treatment of barometer measurement, and the propagation in ECEF frame were also beneficial when using multiple ground antennas to have a common reference point and reference frame. The proposed method was validated using data (Inertial Measurement Unit (IMU), GNSS, PARS, Pixhawk autopilot (including barometer) measurements) collected during a field test. In the validation, GNSS was made available at the middle of the flight and the calibration mode was activated for 200s. The proposed navigation system successfully estimated the precise orientation of multiple ground antennas and the navigation solutions were verified using GNSS and Pixhawk autopilot solutions as ground truth. [ABSTRACT FROM AUTHOR]

The landslide monitoring method that uses the Sentinel 1A Interferogram Synthetic Aperture Radar (InSAR) through the Stanford Method for Persistent Scatterers (StaMPS) method is a complimentary but complex procedure without exact guidelines. Hence, this paper delivered a parametric test by examining the optimal settings of the Sentinel 1A Persistent Scatterer (PS)- and Small Baseline Subset (SBAS)-InSAR using the StaMPS compared to the Global Navigation Satellite Systems (GNSS) in landslide cases. This study first revealed parameters with the suggested values, such as amplitude dispersion used to describe amplitude stability, ranging from 0.47 to 0.48 for PS and equal to or more than 0.6 for SBAS in WuWanZai, Ali Mt. The study further examined the suggested values for other factors, including the following: unwrap grid size to re-estimate the size of the grid; unwrap gold n win as the Goldstein filtering window to reduce the noise; and unwrap time win as the smoothing window (in days) for estimating phase noise distributions between neighboring pixels. Furthermore, the study substantiated the recommended settings in the Woda and Shadong landslide cases with the GNSS, inferring that the SBAS has adequate feasibility in practical landslide monitoring. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) is an essential parameter in numerical weather prediction and climate research. Existing global empirical PWV models rely on a single coefficient for vertical adjustment and lack geographical differentiation. Therefore, this study developed the global PWV vertical adjustment model (GPWV-H) by considering the time-varying lapse rate using the fifth-generation European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5) from 2012 to 2017. The performance of the GPWV-H model in vertical adjustment is evaluated using multi-source PWV data and compared with the conventional empirical model (EPWV-H). The numerical results are as follows: (1) The bias and root mean square (RMS) of the GPWV-H model are − 0.10/ − 0.35 mm and 1.43/1.07 mm, respectively, when ERA5 and radiosonde PWV profiles were used as reference which are 9.3 and 5.9% (in RMS) lower than EPWV-H model; (2) The GPWV-H model improved by 15.1–17.1 and 0.8–1.6% compared to the non-adjustment and the EPWV-H model, respectively, when interpolating Second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) with various grid resolutions to radiosonde stations. These results indicate that the GPWV-H model outperforms the EPWV-H model regarding global PWV interpolation accuracy and stability and has a promising application tendency in global real-time and high-precision water vapor monitoring. [ABSTRACT FROM AUTHOR]

Global Positioning System (GPS) Precise Point Positioning (PPP) with correct fixing ambiguity resolution (AR) can reach cm‐mm level positioning accuracy. However, this accuracy can be degraded by the geomagnetic storm effects. To comprehensively investigate the ambiguity resolved percentage (ARP) of GPS kinematic PPP, referred to as PPP‐ARP, under different intensities of geomagnetic storms, based on the Natural Resources Canada's Canadian Spatial Reference System (CSRS) PPP, this study for the first time gives the correlation between the PPP‐ARP and storm intensity using 67 storms occurred in the past 5 years of 2018–2022. Experimental results indicate that the PPP‐ARP decreases gradually as the increase of geomagnetic storm intensity. Under quiet and low geomagnetic conditions (Dstmin > −50 nT), the PPP‐ARP of global GNSS stations can achieve more than 96%, while these during strong storms (Dstmin ≤ −100 nT) are generally lower than 90.0%, especially for the PPP‐ARP of some stations located at low latitudes which are lower than 40.0%. The mechanism of PPP‐ARP decrease under geomagnetic storms is mainly due to the cycle slips and even loss of lock of GNSS signals caused by the storms induced ionospheric disturbances and scintillations. In addition, different from many previous studies, we found that the CSRS‐PPP with AR can achieve good positioning accuracy (3D RMS <0.2 m) even under strong geomagnetic storms. Plain Language Summary: Global Positioning System (GPS) Precise Point Positioning (PPP) can achieve positioning accuracy of centimeters or even millimeters after resolving the integer ambiguities. However, during strong geomagnetic storms, the positioning accuracy of GPS kinematic PPP will significantly decrease. Under different geomagnetic storm intensities, we perform the experiments on the ambiguity resolved percentage (ARP) of GPS kinematic PPP (PPP‐ARP) using the Natural Resources Canada's Canadian Spatial Reference System (CSRS) PPP software. It is found that geomagnetic storms and associated ionospheric disturbances as well as scintillations can result in the loss of locks of satellite signals, causing a significant reduction in the number of available GPS satellites and alter the distribution pattern of satellites, which leads to the PPP ambiguity reset and decrease of the PPP‐ARP. The stronger the geomagnetic storm, the more severe the impact on PPP‐ARP in general, especially for those located at low‐latitude stations. Key Points: Strong storms induced ionospheric disturbances can cause the decrease of the ambiguity resolved percentage of GPS kinematic PPP (PPP‐ARP)The PPP‐ARP decreases gradually as the increase of geomagnetic storm intensityPPP‐ARP decrease is related to the loss of locks of GPS satellites caused by the geomagnetic storms [ABSTRACT FROM AUTHOR]

Abstract Tropospheric delay is a major error caused by atmospheric refraction in Global Navigation Satellite System (GNSS) positioning. The study evaluates the potential of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5) atmospheric variables in estimating the Zenith Tropospheric Delay (ZTD). Linear regression models (LRM) are applied to estimate ZTD with the ERA5 atmospheric variables. The ZTD are also estimated using standard ZTD models based on ERA5 and Global Pressure and Temperature 3 (GPT3) atmospheric variables. These ZTD estimates are evaluated using the data collected from the permanent GNSS continuously operating reference stations in the Nigerian region. The results reveal that the Zenith Hydrostatic Delay (ZHD) from the LRM and the Saastamoinien model using ERA5 surface pressure are of identical accuracy, having a Root Mean Square (RMS) error of 2.3 mm while the GPT3-ZHD has an RMS of 3.4 mm. For the Zenith Wet Delay (ZWD) component, the best estimates are derived using ERA5 Precipitable Water Vapour (PWV). These include the ZWD derived by the LRM having an average RMS of 20.9 mm and Bevis equation having RMS of 21.1 mm and 21.0 mm for global and local weighted mean temperatures, respectively. The evaluation of GPT3-ZWD estimates gives RMS of 45.8 mm. This study has provided a valuable insight into the application of ERA5 data for ZTD estimation. In line with the findings of the study, the ERA5 atmospheric variables are recommended for improving the accuracy in ZTD estimation, required for GNSS positioning.

The time-hopping (TH) pulsed pseudolite (PL) can be used to enhance the performance of global navigation satellite systems (GNSS), and the slot index table (SIT) is an important part to form this PL signal. In this paper, a new method to generate SITs for multiple PLs is proposed. The general process of the proposed method is that first different time-hopping slot index (THSI) base matrices are generated based on the constructed congruence codes, and then by combining several different THSI base matrices to constitute a new matrix, the SIT for a PL is formed. Further by changing the combined matrices, different SITs for different PLs can be generated. During this process, two critical factors that affect the performance of the given method, i.e., the collision or hit property of the generated THSI base matrix and the correlation property of the formed SIT, are analyzed in detail. The simulations given at the end of this paper show that compared with many other similar schemes, the SITs given by the proposed method can obtain better correlation performance and detection performance in the receiver, and these results also imply that the proposed method offers a more effective way to design this kind of PL signal.

Precision point positioning (PPP) utilizing the Global Navigation Satellite System (GNSS) is a traditional and widely employed technology. Its performance is susceptible to observation discontinuities and unfavorable geometric configurations. Consequently, the integration of the Inertial Navigation System (INS) and GNSS makes full use of their respective advantages and effectively mitigates the limitations of GNSS positioning. However, the GNSS/INS integration faces significant challenges in complex and harsh urban environments. In recent years, the geometry between the user and the satellite has been effectively improved with the advent of lower-orbits and faster-speed Low Earth Orbit (LEO) satellites. This enhancement provides more observation data, opening up new possibilities and opportunities for high-precision positioning. Meanwhile, in contrast to the traditional extended Kalman filter (EKF) approach, the performance of the LEO-enhanced GNSS/INS tightly coupled integration (TCI) can be significantly improved by employing the factor graph optimization (FGO) method with multiple iterations to achieve stable estimation. In this study, LEO data and the FGO method were employed to enhance the GNSS/INS TCI. To validate the effectiveness of the method, vehicle data and simulated LEO observations were subjected to thorough analysis. The results suggest that the integration of LEO data significantly enhances the positioning accuracy and convergence speed of the GNSS/INS TCI. In contrast to the FGO GNSS/INS TCI without LEO enhancement, the average enhancement effect of the LEO is 22.16%, 7.58%, and 10.13% in the north, east, and vertical directions, respectively. Furthermore, the average root mean square error (RMSE) of the LEO-enhanced FGO GNSS/INS TCI is 0.63 m, 1.21 m, and 0.85 m in the north, east, and vertical directions, respectively, representing an average improvement of 41.91%, 13.66%, and 2.52% over the traditional EKF method. Meanwhile, the simulation results demonstrate that LEO data and the FGO method effectively enhance the positioning and convergence performance of GNSS/INS TCI in GNSS-challenged environments (tall buildings, viaducts, underground tunnels, and wooded areas).

Currently, it remains a challenge to effectively monitor areas experiencing intense precipitation and the associated atmospheric conditions on a global scale. This challenge arises due to the limitations on both active and passive remote sensing methods. Apart from the lack of observations in remote areas, the quality of some observations deteriorates when heavy precipitation is present, making it difficult to obtain highly accurate measurements of the thermodynamic parameters driving these weather events. However, there is a promising solution in the form of the Global Navigation Satellite System (GNSS) Polarimetric Radio Occultation (PRO) technique. This approach provides a way to assess the large-scale bulk-hydrometeor characteristics of regions with heavy precipitation and the meteorological conditions associated with them. PRO offers vertical profiles of atmospheric variables, including temperature, pressure, water vapor pressure, and information about hydrometeors, all in a single fine-vertical resolution observation. To continue validating the PRO technique, we make use of polarimetric weather data from Next Generation Weather Radars (NEXRAD), focusing on comparing specific differential phase shift (Kdp) structures to PRO observable differential phase shift (ΔΦ). We have seen that PAZ and NEXRAD exhibit a good agreement on the vertical structure of the observable ΔΦ and that their combination could be useful for enhancing our understanding of the microphysics underlying heavy precipitation events.

Three-dimensional (3D) maps are used extensively in a variety of applications, from air and noise pollution modelling to location-based services such as 3D mapping-aided Global Navigation Satellite Systems (GNSS), and positioning and navigation for emergency service personnel, unmanned aerial vehicles and autonomous vehicles. However, the financial cost associated with creating and updating 3D maps using the current state-of-the-art methods such as laser scanning and aerial photogrammetry are prohibitively expensive. To overcome this, researchers have proposed using GNSS signals to create 3D maps. This paper advances that family of methods by proposing and implementing a novel technique that avoids the difficult step of directly classifying GNSS signals into line-of-sight and non-line-of-sight classes by utilising edge detection techniques adapted from computer vision. This prevents classification biases and increases the range of environments in which GNSS-based 3D mapping methods can be accurately deployed. Being based on the patterns of blockage and attenuation of GNSS signals that are freely and globally available to receive by many mobile phones, makes the proposed technique a free, scalable and accessible solution. This paper also identifies some key indicators affecting data collection scalability and efficiency of the 3D mapping solution. [ABSTRACT FROM AUTHOR]

Carrier phase signals are considered among the key observations in global navigation satellite systems (GNSSs) and several other high-precision inter-ferometric measurement systems. However, these ultra-precise measurements are not fully exploited when the integerness of their inherent ambiguities is discarded during the estimation process. Provided that the integer-estimable functions of their phase ambiguities are properly identified, integer ambiguity resolution (IAR) can be utilized to benefit their parameter solutions. For the GNSS code division multiple access systems with transmitters that broadcast carrier phase signals on identical frequencies, these integer-estimable functions have been characterized and are well-known as double differenced ambiguities. However, this is not the case with "frequency-varying" carrier phase signals that are broadcast by GLONASS satellites, Low-Earth-Orbiting communication satellites, or cellular long-term evolution (LTE) transmitters. This study aims to present full-rank models that can be used to identify integer-estimable ambiguity functions, thereby bringing the observation equations of frequency-varying carrier phase measurements into an IAR-applicable form. Our analytical results are supported by several numerical examples, including GNSS and terrestrial-based IAR as well as a new set of "inter-frequency" integer ambiguities that this study discovers in Galileo multi-frequency carrier phase signals. [ABSTRACT FROM AUTHOR]

The aviation community is pursuing multi-constellation Advanced Receiver Autonomous Integrity Monitoring (ARAIM) to offer safe aircraft navigation services. The computational issue of ARAIM becomes critical when multiple constellations are involved and Fault Exclusion (FE) is enabled. This paper proposes an implementation of fault grouping to improve the efficiency of multi-constellation ARAIM and to benefit navigation performance. To potentially accommodate most ARAIM services, we consider both ARAIM Fault Detection (FD) and ARAIM Fault Detection and Exclusion (FDE) scenarios. In addition, given the difference between Vertical ARAIM (V-ARAIM) and Horizontal ARAIM (H-ARAIM), the implementation steps of fault grouping for V-ARAIM and H-ARAIM are respectively described. More importantly, unlike most of the prior approaches that were limited to dual-constellation scenarios, our implementation can support up to four constellations. The implementation of fault grouping is evaluated using multiple sets of simulations, which are carried out as a function of the number of constellations, prior fault probabilities, error models, and operational services. The results suggest that in most cases, the proposed implementation of fault grouping can effectively reduce the ARAIM computational load while benefiting or maintaining the navigation performance. [ABSTRACT FROM AUTHOR]

Ionospheric scintillation could seriously disrupt the signal tracking of the global navigation satellite systems (GNSS), further causing positioning accuracy degradation or unavailability. BeiDou navigation satellite system (BDS), a newly developed GNSS by China, has begun to provide global positioning, navigation, and timing service. The objective of the present study is to investigate the effects of ionospheric scintillation on BDS‐2 and BDS‐3 multi‐frequency signals. Ionospheric scintillation monitor receiver data from four monitors in Brazil were collected from October 2021 to May 2022. The results illustrate that S4(B2) and S4(B3) linearly increase with S4(B1) for S4(B1) ≤ 0.6, which is consistent with weak scattering theories, and average experimental ratios of S4(B2)/S4(B1), S4(B3)/S4(B1), and S4(B2)/S4(B3) are less than corresponding theoretical ones by 6.1%, 4.4%, and 1.9%, respectively. Meanwhile, as S4 values increase, lower‐frequency scintillation saturates earlier than higher ones, and the probability of ionospheric scintillation events on B2 and B3 signals is approximately twice (S4 ≥ 0.7) as B1 signals in the equatorial ionization anomaly (EIA) regions. To alleviate the undesirable effects of missing data on GNSS positioning, we first investigate the inter‐frequency relationship and distribution probability of two significant spectral parameters, that is, T (the spectral strength of the phase noise at 1 Hz) and p (the spectral slope of the phase power spectral density) in the tracking jitter model among three BDS frequencies. Results show that the performances among B1, B2, and B3 frequencies have a higher correlation respectively, and their values for B2 and B3 signals are more susceptible to be impacted by ionospheric scintillation. Plain Language Summary: Ionospheric scintillation seriously affects the precision and continuity of the global navigation satellite systems (GNSS) signals and causes positioning accuracy degradation or unavailability. Therefore, it is essential to study the impacts of ionospheric scintillation on the GNSS receiver performance. This study first investigates the inter‐frequency relation of the amplitude scintillation index S4 for B1, B2, and B3 signals from BDS‐2 and BDS‐3 satellites based on the Ionospheric scintillation monitor receiver data at four geomagnetic latitudes in the Brazilian territory. Furthermore, the distribution probability and inter‐frequency relationship of two significant phase spectral parameters of the tracking jitter model, which are widely applied in GNSS positioning solutions, are also presented among three BDS frequencies. All investigations conducted within this contribution show agreement with earlier work in ionospheric scintillation and provide supportive and detailed numerical statements and comparisons. Key Points: The inter‐frequency relation of the amplitude scintillation index for multi‐frequency signals from BDS‐2 and BDS‐3 satellites is displayedThe probability of the ionospheric scintillation events on B2 and B3 signals is approximately twice (S4 ≥ 0.6) as B1 signals in EIA regionsThe spectral parameters p and T of B2 and B3 signals are more susceptible to be affected by ionospheric scintillation [ABSTRACT FROM AUTHOR]

The rapid increase of the number of low Earth orbit (LEO) satellites brings up the possibility of LEO satellite missions transmitting dedicated signals for positioning, navigation, and timing (PNT). Although great attention has been paid in recent years to understand the benefits of LEO satellites for PNT, dense LEO constellations will also provide unique measurements of the Earth's upper atmosphere. The benefits of the highly dense LEO-PNT systems are explored in this work to analyze the potential gains of using total electron content (TEC) measurements derived from LEO-PNT systems for 3-D ionospheric imaging. As a result, we have found obvious improvement in the ionospheric imaging system by including LEO satellites to the system geometry. Furthermore, our investigation has discovered that accurate electron density representations can be obtained even when no horizontal viewing angles are included in the imaging system, which is a unique point to imaging systems. In addition, we propose a method to derive accurate 3-D electron density representation based on ranging measurements from intersatellite links. The method provides accurate electron density estimations with no evident bias, but it still depends on the accuracy of background representations. The results indicate that improvements of over 80% can be achieved for both vertical and horizontal distributions of the ionosphere in comparison to the background.

This paper provides a unique multi-disciplinary survey of standalone solutions for clean and sustainable water access in Africa and proposes a three-segment architecture, comprised of an UV-LED-based water disinfection unit, a solar-powered battery, and a wireless positioning system. In addition, our paper also provides initial snapshot results from all the three segments of the proposed architecture, as well as two examples of the water analysis and user-survey-based results collected from Togo, Africa. We believe that the features of our envisaged system such as long battery life, low maintenance cost, ability to significantly increase the quality and disinfect at least 1.5 l/min of water and ability to offer wireless positioning without the need to access a cellular or WiFi network, for accurate annotation of water sources, localization of misplaced or lost disinfection systems, and user standalone positioning capabilities, are attractive features towards improving the quality of life of people with limited access to potable water or to water that fulfills international quality criteria.

Global navigation satellite systems (GNSS) tropospheric tomography can be used to build a three-dimensional water vapor field. In traditional tomography, the signals crossing from the four sides of the tomographic region are not utilized. To make the best use of these valuable side-crossing signals, an improved tomographic model based on back propagation artificial neural network (BP-ANN) is proposed. In the new tomographic model, the inside part of the slant wet delay (SWD) of the side-crossing signal is divided into two sections: the isotropic and anisotropic components. The former is estimated by the zenith wet delay multiplied by the mapping function multiplied by an isotropic scale factor using a BP-ANN model, and the latter is estimated by horizontal gradients of the SWD multiplied by an anisotropic scale factor using an empirical model. The new tomographic model is experimentally evaluated using the HK CORS network measurements for the period of 21 days from 1 to 21 August 2019. Statistical results show that the root mean square error (RMSE) of slant water vapor reconstructed from the improved model is reduced to 1.35 from 2.85 mm of the traditional model. Compared with the traditional/height factor models, the percentages of the reduction in the RMSE of the tomographic result derived from the new model are 16%/9% and 22%/16%, respectively, using radiosonde and ERA5 data as references. These results suggest a good performance of the new model for GNSS tropospheric tomography.

Tropospheric delay is a major error caused by atmospheric refraction in Global Navigation Satellite System (GNSS) positioning. The study evaluates the potential of the European Centre for Medium-range Weather Forecast (ECMWF) Reanalysis 5 (ERA5) atmospheric variables in estimating the Zenith Tropospheric Delay (ZTD). Linear regression models (LRM) are applied to estimate ZTD with the ERA5 atmospheric variables. The ZTD are also estimated using standard ZTD models based on ERA5 and Global Pressure and Temperature 3 (GPT3) atmospheric variables. These ZTD estimates are evaluated using the data collected from the permanent GNSS continuously operating reference stations in the Nigerian region. The results reveal that the Zenith Hydrostatic Delay (ZHD) from the LRM and the Saastamoinien model using ERA5 surface pressure are of identical accuracy, having a Root Mean Square (RMS) error of 2.3 mm while the GPT3-ZHD has an RMS of 3.4 mm. For the Zenith Wet Delay (ZWD) component, the best estimates are derived using ERA5 Precipitable Water Vapour (PWV). These include the ZWD derived by the LRM having an average RMS of 20.9 mm and Bevis equation having RMS of 21.1 mm and 21.0 mm for global and local weighted mean temperatures, respectively. The evaluation of GPT3-ZWD estimates gives RMS of 45.8 mm. This study has provided a valuable insight into the application of ERA5 data for ZTD estimation. In line with the findings of the study, the ERA5 atmospheric variables are recommended for improving the accuracy in ZTD estimation, required for GNSS positioning. [ABSTRACT FROM AUTHOR]

As tsunamis propagate across open oceans, they remain largely unseen due to the lack of adequate sensors. To address this fundamental limitation of existing tsunami warnings, we investigate Global Navigation Satellite Systems (GNSS) data to monitor the ionosphere Total Electron Content (TEC) for Traveling Ionospheric Disturbances (TIDs) created by tsunami-induced internal gravity waves (IGWs). The approach has been applied to regular tsunamis generated by earthquakes, while the case of undersea volcanic eruptions injecting energy into both the ocean and the atmosphere remains mostly unexplored. With both a regular tsunami and air-sea waves, the large 2022 Hunga Tonga-Hunga Ha'apai volcanic eruption is a challenge. Here, we show that even in near-field regions (1000–1500 km), despite the complex wavefield, we can isolate the regular tsunami signature. We also highlight that the eruption-generated Lamb wave induces an ionospheric disturbance with a similar waveform and an amplitude spatial pattern consistent with IGW origin but with a quasi-constant propagation speed (~ 315 m/s). These results imply that when GNSS-TEC measurements are registered near an ocean bottom pressure sensor, they can help discriminating the regular tsunami from the initial air-sea waves appearing in the sensor observations. [ABSTRACT FROM AUTHOR]

The time-hopping (TH) pulsed pseudolite (PL) can be used to enhance the performance of global navigation satellite systems (GNSS), and the slot index table (SIT) is an important part to form this PL signal. In this paper, a new method to generate SITs for multiple PLs is proposed. The general process of the proposed method is that first different time-hopping slot index (THSI) base matrices are generated based on the constructed congruence codes, and then by combining several different THSI base matrices to constitute a new matrix, the SIT for a PL is formed. Further by changing the combined matrices, different SITs for different PLs can be generated. During this process, two critical factors that affect the performance of the given method, i.e., the collision or hit property of the generated THSI base matrix and the correlation property of the formed SIT, are analyzed in detail. The simulations given at the end of this paper show that compared with many other similar schemes, the SITs given by the proposed method can obtain better correlation performance and detection performance in the receiver, and these results also imply that the proposed method offers a more effective way to design this kind of PL signal. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) tropospheric tomography is a promising technique to provide high-resolution three-dimensional (3D) water vapor fields for assimilation into weather forecast models. Recently, a novel tomography method based on special mathematical functions, referred to as function-based tropospheric tomography, has been developed. However, this conventional function-based tomography (CFT) method only expresses the water vapor content through a fixed-degree polynomial function in the horizontal direction. This study suggests an adaptive-degree layered function-based tomography (ALFT) approach to overcome the limitation. The novelties of this approach are (1) the establishment of both horizontal and vertical base functions for tomography and (2) the determination of the optimal adaptive degree of the base functions. The proposed method can estimate the atmospheric water vapor at any position and altitude, producing space-continuous atmospheric water vapor distributions. Comparisons against radiosonde and ERA5 data suggest that the ALFT approach yields more accurate solutions than the CFT method, with the root mean square (RMS) error of tomographic results improved by 34% and 33%, respectively. This suggests that the ALFT method may improve the tomographic water vapor products and advance the function-based tomographic technique. [ABSTRACT FROM AUTHOR]

The precise point positioning (PPP)-based real-time-kinematic (RTK) method attracts increasing attention from both academia and industry because of its potential for high accuracy positioning with a shorter convergence time compared to the traditional PPP. Besides high accuracy, integrity monitoring (IM) is indispensable for safety–critical real-time land vehicle and aviation applications. As the traditional advanced receiver autonomous integrity monitoring (ARAIM) method is designed for (smoothed) pseudorange-based positioning, the complexity of multi-frequency multi-constellation PPP-RTK using carrier phase measurements has not been given sufficient consideration. This study proposes an IM scheme for multi-frequency multi-constellation uncombined PPP-RTK applying the ARAIM theory, with a new comprehensive threat model to accommodate not only pseudorange measurements, but also carrier phase measurements, and other fault events arising from the network corrections that support PPP-RTK. Characteristics of different types of faults are analyzed with the aid of numerical experiments. In addition, the impact of ambiguity-fixed solutions on PPP-RTK integrity performance is investigated. The authors have also conducted case studies, including static and real-kinematic positioning experiments. Experiments have demonstrated that fast convergence in accuracy and the position error bounds, or protection levels, with a given integrity risk, in horizontal position components of PPP-RTK could be achieved. For the open sky environments on a highway, the protection levels estimated by PPP-RTK solutions have the potential to meet the alert limit requirement for road transportation using ambiguity-fixed PPP-RTK positioning under the assumption that the risks of wrong ambiguity fixing are very small and can be ignored. [ABSTRACT FROM AUTHOR]

Low Earth Orbit (LEO) constellations have ecently gained tremendous attention in the navigational field due to their arger constellation size, faster geometry variations, and higher signal power evels than Global Navigation Satellite Systems (GNSS), making them favourable for Position, Navigation, and Timing (PNT) purposes. Satellite signals are heavily attenuated from the atmospheric ayers, especially from the ionosphere. Ionospheric delays are, however, expected to be smaller in signals from LEO satellites than GNSS due to their ower orbital altitudes and higher carrier frequency. Nevertheless, unlike for GNSS, there are currently no standardized models for correcting the ionospheric errors in LEO signals. In this paper, we derive a new model called Interpolated and Averaged Memory Model (IAMM) starting from existing International GNSS Service (IGS) data and based on the observation that ionospheric effects epeat every 11 years. Our IAMM model can be used for ionospheric corrections for signals from any satellite constellation, including LEO. This model is constructed based on averaging multiple ionospheric data and eflecting the electron content inside the ionosphere. The IAMM model’s primary advantage is its ability to be used both online and offline without needing eal-time input parameters, thus making it easy to store in a device’s memory. We compare this model with two benchmark models, the Klobuchar and International Reference Ionosphere (IRI) models, by utilizing GNSS measurement data from 24 scenarios acquired in several European countries using both professional GNSS eceivers and Android smartphones. The model’s behaviour is also evaluated on LEO signals using simulated data (as measurement data based on LEO signals are still not available in the open-access community; we show a significant eduction in ionospheric delays in LEO signals compared to GNSS. Finally, we highlight the remaining open challenges toward viable ionospheric-delay models in an LEO-PNT context.

GNSS_Vel_95CI.py is an open-source Python-3 module for calculating the 95% confidence interval (95% CI) for the site velocity derived from global navigation satellite systems (GNSS) daily positions, which are often affected by time-correlated noises. The detailed methodology for calculating the 95% CI is documented in a recent article (Wang 2022) and initially programmed in Fortran. However, few young researchers (e.g., graduate students) are familiar with Fortran now. In an effort to support a broader user community, we have realized the method in the Python programming language, which has been commonly taught in current college curriculums. Through the use of this module, researchers and engineers can focus on the applications of GNSS time series, rather than on coding and data processing. In particular, the module has the option to output the autocorrelation function (ACF) and the GNSS time-series decomposition results: the linear, nonlinear, seasonal, and residual components, which allow students and researchers having little coding experience to conduct advanced GNSS time-series analysis. The module is versatile, easy to install through the pip installer and GitHub, and simple to use. An example Python program is provided to illustrate the use of this module. [ABSTRACT FROM AUTHOR]

Coordinate–time measurements are considered as one of the most common types of measurements in various radio navigation and radar systems in the field of geodesy and radio communications. It is shown that the increase in the number of coordinate–time measuring instruments, as well as the increased level of requirements for the accuracy of determining user coordinates, stipulated the relevance of the development of the State Primary Special Standard of Location Coordinates. The composition of the State primary special standard of location coordinates GET 218-2022, developed at VNIIFTRI, is presented, which will ensure the uniformity of measurements of coordinates and azimuth, satellite geodetic and navigation equipment using signals from global navigation satellite systems GLONASS, GPS, Galileo, BeiDou and ground augmentations. The metrological characteristics of GET 218-2022 in terms of storing absolute coordinates, reproducing/measuring the unsolicited range in phases of the ranging code and carrier frequency, reproducing the coordinates of the consumer of global navigation satellite systems, measuring increments of coordinates in coordinate systems are studied. The results of studies of the metrological characteristics of GET 218-2022 in terms of storing absolute coordinates and measuring increments of user coordinates showed a comparable level of accuracy with the characteristics of the international ITRS system. With the use of GET 218-2022, it is possible to test such promising measuring instruments as high-precision unsolicited measurement systems, precision receivers of global navigation satellite systems in the mode of absolute/relative phase measurements. [ABSTRACT FROM AUTHOR]

The inter-system real-time kinematic (RTK) model in which multiple systems choose the same reference satellite uses more observations than the traditional intra-system RTK model; however, it is still difficult to accurately determine the differential inter-system biases (DISB) and inter-system ambiguity in a complex environment. We propose a tightly coupled inter-system RTK/INS model that uses the high-precision position information the inertial navigation system (INS) provides to assist in DISB estimation and inter-system ambiguity resolution. Vehicle experiments on urban roads were designed to verify the effectiveness of the method. The vehicle experiments consisted of a simulated rare satellite environment with a high cutoff elevation angle and a real complex environment with buildings and trees obscuration. A robust Kalman filter strategy is used to combat the effects of multipath and non-line-of-sight signals in real complex environments. The results indicate that with the help of INS, the standard deviation of phase and code DISB is reduced by 11 and 17%, respectively, in the simulated environment and by 33 and 18%, respectively, in the real complex environment. Compared with the intra-system RTK/INS model, inter-system RTK/INS mode 3D positioning root-mean-square error is reduced by 79% in the simulated environment and by 27% in the real complex environment. In the single-epoch mode, the ambiguity success rates of the inter-system RTK/INS model, inter-system RTK model, intra-system RTK/INS model and intra-system RTK model are 89, 74, 69 and 58%, respectively, in the simulated environment, and 68, 41, 64 and 12%, respectively, in the real complex environment. [ABSTRACT FROM AUTHOR]

The landslide monitoring method that uses the Sentinel 1A Interferogram Synthetic Aperture Radar (InSAR) through the Stanford Method for Persistent Scatterers (StaMPS) method is a complimentary but complex procedure without exact guidelines. Hence, this paper delivered a parametric test by examining the optimal settings of the Sentinel 1A Persistent Scatterer (PS)- and Small Baseline Subset (SBAS)-InSAR using the StaMPS compared to the Global Navigation Satellite Systems (GNSS) in landslide cases. This study first revealed parameters with the suggested values, such as amplitude dispersion used to describe amplitude stability, ranging from 0.47 to 0.48 for PS and equal to or more than 0.6 for SBAS in WuWanZai, Ali Mt. The study further examined the suggested values for other factors, including the following: unwrap grid size to re-estimate the size of the grid; unwrap gold n win as the Goldstein filtering window to reduce the noise; and unwrap time win as the smoothing window (in days) for estimating phase noise distributions between neighboring pixels. Furthermore, the study substantiated the recommended settings in the Woda and Shadong landslide cases with the GNSS, inferring that the SBAS has adequate feasibility in practical landslide monitoring.

The tropospheric tomography is an ill-posed inversion problem due to the sparsity of global navigation satellite systems (GNSS) stations and the limitation on the projection angles of GNSS signals, which in turn affects the stability and robustness of the tomographic solution. To address this, a new tomographic algorithm, named two-step projected iterative algorithm (TSPIA), is proposed. The wet refractivity (WR) field was constructed in two steps: first, an iterative preprocessing for the initial input values was performed, and then its resultant solution was input into the projected iterative method, in which a hypothesis convex set was constructed to constrain the reconstruction based on the classical algebraic iterative reconstruction (AIR) methods. In addition, a two-dimensional normalized cumulative periodogram (2-D-NCP) termination criterion was investigated since the traditional criteria for judging the convergence of iterations use prefixed empirical thresholds, which may lead to excessive iterations and need complicated work. The TSPIA was tested using GNSS data in Hong Kong over a wet period and a dry period. Statistical results showed that, compared to the classical AIR methods, the accuracy of the reconstructed WR field of the TSPIA were improved by about 10% and 15% when radiosonde and ECMWF data were used as the reference, respectively. Moreover, experiments for the proposed 2-D-NCP criterion demonstrated noticeable computational efficiency. These results suggest that the new approaches proposed in this article can improve the performance of the iterative methods for GNSS tropospheric tomography.

Reliable, accurate, and timely information about oceans is important for many applications, including water resource management, hydrological cycle monitoring, environmental studies, agricultural and ecosystem health applications, economy, and the overall health of the environment. In this regard, remote sensing (RS) systems offer exceptional advantages for mapping and monitoring various oceanographic parameters with acceptable temporal and spatial resolutions over the oceans and coastal areas. So far, different methods have been developed to study oceans using various RS systems. This urges the necessity of having review studies that comprehensively discuss various RS systems, including passive and active sensors, and their advantages and limitations for ocean applications. In this article, the goal is to review most RS systems and approaches that have been worked on marine applications. This review paper is divided into two parts. Part 1 is dedicated to the passive RS systems for ocean studies. As such, four primary passive systems, including optical, thermal infrared radiometers, microwave radiometers, and Global Navigation Satellite Systems, are comprehensively discussed. Additionally, this article summarizes the main passive RS sensors and satellites, which have been utilized for different oceanographic applications. Finally, various oceanographic parameters, which can be retrieved from the data acquired by passive RS systems, along with the corresponding methods, are discussed.

Using global navigation satellite systems as transmitters of opportunity for target detection is a hot issue. The problems of this technology are the restricted power budget provided by navigation satellites and the defocusing caused by the movement of target. To refocus the echo and improve the signal-to-noise ratio under the condition of Beidou Geostationary satellites, this article uses jointly the short-time coherent integration and the long-time integration based on Fractional Fourier transform to correct the range migration and the Doppler frequency migration of signals for refocusing the signal energy. The effectiveness of the proposed algorithm for refocusing the echo from different types of maneuvering ships (a cargo ship and a ferry) is confirmed via an experimental campaign.

Recent studies on the application of Zenith Tropospheric Delay (ZTD) in meteorology and geodesy have shown improved accuracy with the use of locally estimated ZTD compared to Global ZTD. This paper focuses on the refinement of global gridded ray-traced Vienna Mapping Functions 1 (VMF1) ZTD over Nigeria, using ZTD estimates derived from local GNSS Continuously Operating Reference Stations (CORS) observations. Three (3) years of GNSS observations collected at 13 CORS located within the study area were post-processed using GNSS Analysis and Processing Software (GAPS) to derive the local GNSS-ZTD estimates. The global VMF1-ZTD were interpolated at the locations of the local GNSS-CORS. An optimal deep structured supervised Neural Network (NN) was derived by training feedforward networks using the Levenberg-Marquardt backpropagation algorithm, with spatiotemporal variables and VMF1-ZTD as inputs and the GNSS-ZTD as the target. The outputs from the NN model were assessed using internal and external data. Results from internal data evaluations indicated a robust and accurate model output with optimum network performances indicating MSE of ∼7.22 × 10−6 m and ∼4.56 × 10−4 m for hydrostatic and wet ZTD networks respectively. The external data evaluations performed using the International GNSS Services (IGS) Zenith Path Delay (ZPD), indicated an RMSE range of ∼0.041 m to 0.095 m for global-VMF1 and ∼0.030 m to 0.037 m for the Local Refine (LR)-VMF1. The results suggest that the application of ZTD estimates derived from local GNSS observations in the refinement of global gridded VMF1-ZTD yields an improved accuracy over Nigeria. Consequently, the LR-VMF1 ZTD estimates are recommended for improved accuracy, required for geodetic and meteorological applications in and around Nigeria

In this contribution, we generalize PPP–RTK theory by allowing the transmitters to transmit on different frequencies. The generalization is based on the integer-estimability theory of Teunissen (A new GLONASS FDMA model. GPS Solutions, 2019). As the theory and associated algorithms provided are generally applicable, they apply to satellite-based carrier-phase positioning as well as to terrestrial interferometric sensory networks. Based on an identification of the constraints imposed on the admissible ambiguity transformations by PPP–RTK, a fundamental network+user condition is found that determines whether PPP–RTK is possible or not. The discriminating contributions of both the network and user observation equations to this PPP–RTK condition are analysed, followed by a description of PPP–RTK enabling classes of measurement scenarios. [ABSTRACT FROM AUTHOR]

It is well known that for binary offset carrier (BOC) signals the notorious ambiguity problem due to the multipeaked autocorrelation function must be tackled first in order to obtain the potential of improving ranging performance promised by its broader Gabor bandwidth. The existence of multipath makes it more difficult to solve. Dual binary phase-shift keying (BPSK) tracking (DBT) is a kind of low complexity, unambiguous tracking method for BOC-family signals, which makes full use of the coherence between the upper and lower sidebands and employs subcarrier phase locked loop (SLL) and delay locked loop (DLL) to track subcarrier and code components, respectively. Considering that the thermal noise performance of DBT has been fully studied, this article focuses on the analysis of multipath performance and the design of antimultipath methods under DBT framework. The influence of multipath on SLL and DLL of DBT is analyzed first and the corresponding subcarrier and code multipath error envelopes are obtained. Then according to different requirements of subcarrier and code multipath errors, a multipath mitigation scheme specially designed for DBT is proposed, in which SLL and DLL adopt offset correlator and narrow correlation, respectively. Theoretical analysis and simulation are given to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]