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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]

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]

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.

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]

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.

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]

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]

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]

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]

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

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]

Global navigation satellite systems (GNSS) has been applied to the sounding of precipitable water vapor (PWV) due to its high accuracy and high spatiotemporal resolutions. PWV obtained from GNSS (GNSS-PWV) can be used to investigate extreme weather phenomena, such as the formation mechanism and prediction of rainfalls. In the study, a new, improved model for rainfall forecasting was developed based on GNSS data and rainfall data for the 9-year period from 2010 to 2018 at 66 stations located in the USA. The new model included three prediction factors—PWV value, PWV increase, maximum hourly PWV increase. The two key tasks involved for the development of the model were the determination of the thresholds for each prediction factor and the selection of the optimal strategy for using the three prediction factors together. For determining the thresholds, both critical success index (CSI) and true skill statistic (TSS) were tested, and results showed that TSS outperformed CSI for all rainfall events tested. Then, various strategies by combining the three prediction factors together were also tested, and results indicated that the best forecast result was from the case that any two of the prediction factors were over their own thresholds. Finally, the new model was evaluated using the GNSS data for the 2-year period from 2019 to 2020 at the above mentioned 66 stations, and the probability of detection (POD) and false-alarms rate (FAR) were adopted to measure the model performances. Over the 66 stations, the POD values ranged from 73% to 97% with the mean of 87%, and the FARs ranged from 26% to 77% with the mean of 53%. Moreover, it was also found that both POD and FAR values were related to the region of the station; e.g., the results at the stations that are located in humid regions were better than the ones located in dry regions. All these results suggest the feasibility and good performance of using GNSS-PWV for forecasting rainfall. [ABSTRACT FROM AUTHOR]

Low-latitude ionospheric behavior directly interferes with a wide range of applications dependent on signals and information from satellites. The most severe and variable events are plasma bubbles and ionospheric scintillation. During plasma bubble events, large and steep plasma density gradients may intersect transionospheric signal from satellites, especially around the equatorial anomaly region. Large ionospheric spatial gradients (or decorrelations) are a critical component of ionospheric threat models for global navigation satellite systems augmentation systems; however, the models cannot assimilate abrupt changes in ionospheric behavior. In this article, an investigation of the relationship between plasma depletions, the occurrence of scintillation, and strong ionospheric spatial decorrelation events was conducted. The results indicate that strong scintillations occur when large gradients are verified. Additionally, the most critical ionospheric spatial gradients were verified mostly under certain conditions: when satellite signals are aligned with the plasma bubble propagation along geomagnetic field lines. Therefore, even though amplitude scintillation may degrade communications, the critical gradient events seem to be related to a particular configuration of satellite signal across the ionosphere. Thus, it is recommended that ionospheric threat models for augmentation systems in low-latitude regions consider this alignment aspect. [ABSTRACT FROM AUTHOR]

We present two new high-precision physics-based radiation force models for the In-Orbit Validation (IOV) and Full Operational Capability (FOC) spacecraft (s/c) of the Galileo Global Navigation Satellite System (GNSS). In both cases, the s/c bus surfaces are covered in material types, i.e., Laser Retro-reflector Array (LRA), Optical Surface Reflector (OSR) and Single-Layer Insulation (SLI) coverings, that were either not encountered or not specifically dealt with in earlier work. To address this, a number of modelling enhancements were proposed and tested, including: a specific model to account for the direct and reflected solar radiation force for LRA surfaces; a design update of the bus model computation process to allow for more than one insulation material; a specific thermal force model for OSR surfaces; a thermal force model for the Navigation Antenna (NAVANT) surface that includes a temperature model derived from on-orbit temperature measurements; and force models to account for thermal emissions from radiator panels on the + X and ± Y surfaces for both IOV and FOC, and on the - Z surface for FOC only. In the UCL2+ model each of these effects are accounted for. The theoretical impact of each modelling concept introduced is assessed, individually, by considering the magnitude of its effect in acceleration-space. The impact on orbit accuracy is confirmed through a rigorous set of Precise Orbit Determination (POD) validation tests, in which observations from all active Galileo s/c over two full years, 2017 and 2018, including during eclipsing periods, are included in the analysis. The UCL2+ approach results in day boundary discontinuities of 22 mm, 17 mm and 27 mm in the radial, across-track and along-track components, respectively. Analysis of the one-way Satellite Laser Ranging (SLR) residuals suggests that radial accuracy at better than 1 cm (3.7 mm mean residuals) and precision at better than 2 cm (17 mm root mean square (rms) error) is achievable with the UCL2+ model. [ABSTRACT FROM AUTHOR]

Article presented the security issues of the means of transport which apply global navigation satellite systems, with a particular emphasis on transport safety and the risks resulting from the use of those systems. Presented method of detecting spoofing attacks of the global satellite navigation system involves collecting navigational position readings and comparing them with the current vehicle speed read from the vehicle's speedometer (SoV) and sequential testing of GNSS and SoV readings. Proposed a way automatic speedometer calibration to specified threshold value (D). A physical experiment was carried out to confirm the assumptions of the above-mentioned method, the results of which are that when the car appears in range of spoofer, its speed, as stated by the NMEA RMC readings, was equal to the spoofer speed (0 km/h). [ABSTRACT FROM AUTHOR]

Long-span bridges, often exposed to challenging harsh natural environments with severe weather conditions, necessitate real-time examination of load-deformation characteristics to ensure structural integrity and safety. Previous studies have primarily focused on investigating the causes of deformation in bridge structures under different single-load conditions during severe natural disasters, utilizing physics-based, mechanics-based, and data-driven methods. However, these methods cannot achieve fully achieve effective analysis of the real-time effects of multi-factor loads on bridge deformation, particularly in the presence of dynamic and simultaneous loads such as wind or temperature variations. A novel data-driven method is proposed based on a state-of-the-art real-time updating artificial neural networks (ANNs) algorithm to investigate the real-time coupling relationship between multi-loads and bridge deformation, enabling real-time prediction of bridge deformations. Additionally, the real-time characteristics between structural deformation and multi-loads are explained by incorporating SHapley Additive exPlanation (SHAP) in harsh natural environments. The proposed method has been validated on the 1,006-meter Forth Bridge in Scotland, showing high accuracy in real-time displacement prediction. The 9-day testing dataset demonstrated the R 2 values for Y and Z direction deformations were found to be 0.98 and 0.87, respectively. The performance metrics for each day indicated that the majority of Y and Z direction deformations had R 2 values exceeding 0.8, with RMSE and MAE values below 30 mm. The SHAP analysis revealed that an increase in wind speed leads to intensified Y direction deformation (larger SHAP values), while temperature has a significant impact on Z direction deformation (smaller SHAP values). Moreover, the weight influences of each load on the deformation are not fixed. The study's findings demonstrate that the proposed method enables accurate long-term prediction and assessment, allowing precise monitoring and prevention of abnormal risks in bridges under harsh environmental conditions. • Correlation and time lag coefficients between multi-loads and deformations are obtained based on the Pearson coefficient.Correlation coefficients and time lag coefficients between multi loads and deformations are obtained based on the Pearson coefficient. • A real-time updating artificial neural network is introduced for the purpose of real-time prediction of bridge structural deformation.A real-time updating artificial neural network is proposed for predicting bridge structural deformation. • Combining the real-time updated model, we employed the SHAP method to assess the load-deformation characteristics of the FRB in harsh natural environments and conduct validation. [ABSTRACT FROM AUTHOR]

Centimetre-level accurate ionospheric corrections are required for a high accuracy and rapid convergence of Precise Point Positioning (PPP) GNSS positioning solutions. This research aims to evaluate the accuracy of a local/regional ionospheric delay model using a linear interpolation method across Australia. The accuracy of the ionospheric corrections is assessed as a function of both different latitudinal regions and the number and spatial density of GNSS Continuously Operating Reference Stations (CORSs). Our research shows that, for a local region of 5° latitude ×10° longitude in mid-latitude regions of Australia (~30° to 40°S) with approximately 15 CORS stations, ionospheric corrections with an accuracy of 5 cm can be obtained. In Victoria and New South Wales, where dense CORS networks exist (nominal spacing of ~100 km), the average ionospheric corrections accuracy can reach 2 cm. For sparse networks (nominal spacing of >200 km) at lower latitudes, the average accuracy of the ionospheric corrections is within the range of 8 to 15 cm; significant variations in the ionospheric errors of some specific satellite observations during certain periods were also found. In some regions such as Central Australia, where there are a limited number of CORSs, this model was impossible to use. On average, centimetre-level accurate ionospheric corrections can be achieved if there are sufficiently dense (i.e., nominal spacing of approximately 200 km) GNSS CORS networks in the region of interest. Based on the current availability of GNSS stations across Australia, we propose a set of 15 regions of different ionospheric delay accuracies with extents of 5° latitude ×10° longitude covering continental Australia. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS) differential code bias (DCB) is one of main errors in ionospheric modeling and applications. Accurate estimation of multiple types of GNSS DCBs is important for GNSS positioning, navigation, and timing, as well as ionospheric modeling. In this study, a novel method of multi-GNSS DCB estimation is proposed without using an ionospheric function model and global ionosphere map (GIM), namely independent GNSS DCB estimation (IGDE). Firstly, ionospheric observations are extracted based on the geometry-free combination of dual-frequency multi-GNSS code observations. Secondly, the VTEC of the station represented by the weighted mean VTEC value of the ionospheric pierce points (IPPs) at each epoch is estimated as a parameter together with the combined receiver and satellite DCBs (RSDCBs). Last, the estimated RSDCBs are used as new observations, whose weight is calculated from estimated covariances, and thus the satellite and receiver DCBs of multi-GNSS are estimated. Nineteen types of multi-GNSS satellite DCBs are estimated based on 200-day observations from more than 300 multi-GNSS experiment (MGEX) stations, and the performance of the proposed method is evaluated by comparing with MGEX products. The results show that the mean RMS value is 0.12, 0.23, 0.21, 0.13, and 0.11 ns for GPS, GLONASS, BDS, Galileo, and QZSS DCBs, respectively, with respect to MGEX products, and the stability of estimated GPS, GLONASS, BDS, Galileo, and QZSS DCBs is 0.07, 0.06, 0.13, 0.11, and 0.11 ns, respectively. The proposed method shows good performance of multi-GNSS DCB estimation in low-solar-activity periods. [ABSTRACT FROM AUTHOR]

The ionospheric response and the associated mechanisms to geomagnetic storms are very complex, particularly during the February 2014 multiphase geomagnetic storm. In this paper, the low-latitude ionosphere responses and their coupling mechanisms, during the February 2014 multiphase geomagnetic storm, are investigated from ground-based magnetometers and global navigation satellite system (GNSS), and space weather data. The residual disturbances between the total electron content (TEC) of the International GNSS Service (IGS) global ionospheric maps (GIMs) and empirical models are used to investigate the storm-time ionospheric responses. Three clear sudden storm commencements (SSCs) on 15, 20, and 23 February are detected, and one high speed solar wind (HSSW) event on 19 February is found with the absence of classical SSC features due to a prevalent magnetospheric convection. The IRI-2012 shows insufficient performance, with no distinction between the events and overestimating approximately 20 TEC units (TECU) with respect to the actual quiet-time TEC. Furthermore, the median average of the IGS GIMs TEC during February 2014 shows enhanced values in the southern hemisphere, whereas the IRI-2012 lacks this asymmetry. Three low-latitude profiles extracted from the IGS GIM data revealed up to 20 TECU enhancements in the differential TEC. From these profiles, longer-lasting TEC enhancements are observed at the dip equator profiles than in the profiles of the equatorial ionospheric anomaly (EIA) crests. Moreover, a gradual increase in the global electron content (GEC) shows approximately 1 GEC unit of differential intensification starting from the HSSW event, while the IGS GIM profiles lack this increasing gradient, probably located at higher latitudes. The prompt penetration electric field (PPEF) and equatorial electrojet (EEJ) indices estimated from magnetometer data show strong variability after all four events, except the EEJ's Asian sector. The low-latitude ionosphere coupling is mainly driven by the variable PPEF, DDEF (disturbance dynamo electric fields), and Joule heating. The auroral electrojet causing eastward PPEF may control the EIA expansion in the Asian sector through the dynamo mechanism, which is also reflected in the solar-quiet current intensity variability. [ABSTRACT FROM AUTHOR]

In recent years, precipitable water vapor has been widely used in heavy precipitation prediction, which is obtained from a conversion of the zenith total delay (ZTD) of the GNSS signal. Since the parameter directly estimated for the tropospheric delay from Global Navigation Satellite Systems (GNSS) data processing is the ZTD, this study investigated the feasibility of directly using ZTD to predict heavy precipitation. Based on the finding that, prior to a heavy precipitation events, ZTD was likely to start with a duration of continuous rise followed by a sharp drop, a new heavy precipitation detection model containing seven predictors derived from ZTD was established. The seven predictors reflect not only the ascending and descending trends but also long-term and short-term variations in the ZTD time series. Three criteria, representing different situations for the formation of heavy precipitation, were also constructed using the predictors to detect heavy precipitation. The optimal set of thresholds for the seven predictors for each summer month were determined based on hourly ZTD and precipitation records at a pair of co-located GNSS/weather station−HKSC-KP in Hong Kong over the period 2010−2017. The model was evaluated using the predictions in 2018 and 2019 to compare against the corresponding precipitation records. Results showed that the new model correctly predicted 98.8% of the heavy precipitation events, with a mean lead time of 4.37 h. Compared with the existing models, the new model also reduced the false alarms by 32.3%. Similar results were also obtained from the other three pairs of co-located stations. These results suggest that it is rational and effective to use the new ZTD-based model for improving the performance of heavy precipitation detection.

We consider Global Navigation Satellite Systems (GNSS) spoofing attacks and devise a countermeasure appropriate for mobile GNSS receivers. Our approach is to design detectors that, operating after the signal acquisition, enable the victim receiver to determine with high probability whether it is under a spoofing attack or not. Namely, the binary hypothesis is that either the GNSS receiver tracks legitimate satellite signals, $\mathcal {H}_{0}$ , or spoofed signals, $\mathcal {H}_{1}$ . We assume that there exists an unknown number of multiple spoofers in the environment and the attack strategy (which legitimate signals are spoofed by which spoofers) is not known to the receiver. Based on these assumptions, we propose an algorithm that identifies the number of spoofers and clusters the spoofing data by using Bayesian information criterion (BIC) rule. Depending on the estimated and clustered data we propose a detector, called as generalized likelihood ratio (GLRT)-like detector. We compare the performance of the GLRT-like detector with a genie-aided detector in which the attack strategy and the number of spoofers is known by the receiver. In addition to this, we extend the GLRT-like detector for the case where the noise variance is also unknown and present the performance results.

We assess the accuracy and the precision of the TanDEM-X digital elevation model (DEM) of the western Gulf of Corinth, Greece. We use a dense set of accurate ground coordinates obtained by kinematic Global Navigation Satellite Systems (GNSS) observations. Between 2001 and 2019, 148 surveys were made, at a 1 s sampling rate, along highways, roads, and tracks, with a total traveled distance of ∼25 000 km. The data are processed with the online Canadian Spatial Reference System precise point positioning software. From the output files, we select 885 252 coordinates from epochs with theoretical uncertainty below 0.1 m in horizontal and 0.2 m in vertical. Using specific calibration surveys, we estimate the mean vertical accuracy of the GNSS coordinates at 0.2 m. Resampling the DEM by a factor of 10 allows one to compare it with the GNSS in pixels of metric size, smaller than the width of the roads, even the small trails. The best fit is obtained by shifting the DEM by 0.47 ± 0.03 m upward, 0.10 ± 0.1 m westward, and 0.36 ± 0.1 m southward. Those values are 20 times below the nominal resolution of the DEM. Once the shift is corrected, the root mean square deviation between TanDEM-X DEM and GNSS elevations is 1.125 m. In forest and urban areas, the shift between the DEM and the GNSS increases by ∼0.5 m. The metric accuracy of the TanDEM-X DEM paves the way for new applications for long-term deformation monitoring of this area.

The space segment of the Global Navigation Satellite System (GNSS) is equipped with highly stable atomic clocks. In order to use these clocks as references, their time offsets must be estimated from ground measurements as accurately as possible. This work presents a multi‐frequency and multi‐constellation method for estimating satellite and receiver clock corrections, starting from unambiguous, uncombined, and undifferenced carrier‐phase measurements. A byproduct of the estimation process is phase biases (i.e., the hardware delays of the carrier‐phase measurements occurring at receivers and satellites). The stability and predictability of our clock estimates for receivers and satellites (GPS and Galileo) are compared with those obtained by the International GNSS Service (IGS), whereas the phase biases are assessed against two independent determinations involving combinations of carrier‐phase measurements. We conclude that the method reduces day boundary discontinuities in the clock corrections, and that the estimated phase biases reproduce variabilities already observed by other authors. [ABSTRACT FROM AUTHOR]

Annual and seasonal diurnal precipitable water vapor (PWV) variations over Central and South America are analyzed for the period 2007–2013. PWV values were obtained from Global Navigation Satellite Systems (GNSS) observations of sixty-nine GNSS tracking stations. Histograms by climate categories show that PWV values for temperate, polar and cold dry climate have a positive skewed distribution and for tropical climates (except for monsoon subtype) show a negative skewed distribution.The diurnal PWV and surface temperatures (T) anomaly datasets are analyzed by using principal components analysis (PCA). The first two modes represent more than 90% of the PWV variability. The first PCA mode of PWV variability shows a maximum amplitude value in the late afternoon few hours later than the respective values for surface temperature (T), therefore the temperature and the surface conditions (to yield evaporation) could be the main agents producing this variability; PWV variability in inland stations are mainly represented by this mode. The second mode of PWV variability shows a maximum amplitude at midnight, a possible explanation of this behavior is the effect of the sea/valley breeze. The coastal and valley stations are affected by this mode in most cases. Finally, the “undefined” stations, surrounded by several water bodies, are mainly affected by the second mode with negative eigenvectors. In the seasonal analysis, both the undefined and valley stations constitute the main cases that show a sea or valley breeze only during some seasons, while the rest of the year they present a behavior according to their temperature and the surface conditions. As a result, the PCA proves to be a useful numerical tool to represent the main sub-daily PWV variabilities.

Global Positioning System (GPS) services could be improved through prediction of ionospheric delays for satellite-based radio signals. With respect to latitude, longitude, local time, season, solar cycle and geomagnetic activity the Total Electron Content (TEC) have significant variations in both time and space. These temporal and spatial TEC variations driven by interplanetary space weather conditions such as solar and geomagnetic activities can degrade the communication and navigation links of GPS. Hence, in this paper, performance of TEC forecasting models based on Neural Networks (NN) have been evaluated to forecast (1-h ahead) ionospheric TEC over equatorial low latitude Bengaluru (12.97∘N,77.59∘E), Global Navigation Satellite System (GNSS) station, India. The VTEC data is collected for 2009–2016 (8 years) during current 24th solar cycle. The input space for the NN models comprise the solar Extreme UV flux, F10.7 proxy, a geomagnetic planetary A index (AP) index, sunspot number (SSN), disturbance storm time (DST) index, solar wind speed (Vsw), solar wind proton density (Np), Interplanetary Magnetic Field (IMF Bz). The performance of NN based TEC forecast models and International Reference Ionosphere, IRI-2016 global TEC model has evaluated during testing period, 2016. The NN based model driven by all the inputs, which is a NN unified model (NNunq) has shown better accuracy with Mean Absolute Error (MAE) of 3.15 TECU, Mean Square Deviation (MSD) of 16.8 and Mean Absolute Percentage Error (MAPE) of 19.8% and is 1–25% more accurate than the other NN based TEC forecast models (NN1, NN2 and NN3) and IRI-2016 model. NNunq model has less Root Mean Square Error (RMSE) value 3.8 TECU and highest goodness-of-fit (R2) with 0.85. The experimental results imply that NNunq/NN1 model forecasts ionospheric TEC accurately across equatorial low-latitude GNSS station and IRI-2016 model performance is necessarily improved as its forecast accuracy is limited to 69–70%.

Abstract High speed train (HST) is an excellent platform to perform ultra-high spatial and temporal resolution observations of atmosphere using global navigation satellite systems (GNSS). However, we find that signal attenuation caused by HST window glass is a major barrier for HST-based GNSS applications inside HST chambers. A field experiment is conducted to analyze the effect of HST glass on GNSS signal propagation. In the experiment, GNSS observations are collected and analyzed from a receiver covered with an HST window glass and one with an open-sky view. The size of the HST window glass is 670 mm × 720 mm, with a thickness of 34 mm. The window glass is a double-glazing glass in which each layer has an actual thickness of 6 mm, and the two layers are separated by an air gap of 22 mm. The experiment results indicate that HST window glass can cause significant degradation to GNSS signals and even loss of tracking of the signal. Based on statistical results, HST window glass causes 39%, 56%, 49%, and 59% loss in GPS, GLONASS, Galileo, and BDS signals, respectively. Additionally, up to 20 dB-Hz of carrier-to-noise ratio (C/N0) degradation is also observed in the remaining observations. The significant signal attenuation and loss further lead to the decrease in the number of tracked satellites and occurrence of more cycle slips. The results of the study indicate that 44–230 cycle slips are detected for the HST glass-covered receiver whereas the receiver without glass does not exhibit more than 16 cycle slips. Additionally, the number of GNSS satellites tracked by the HST glass-covered receiver is reduced by 65% owing to the loss of signal. Furthermore, GNSS positioning performances from two receivers are also tested. With respect to GPS + GLONASS static precise point positioning (PPP), HST glass causes a degradation of 1.516 m and 1.159 m in the single-frequency and dual-frequency three-dimensional positioning accuracy, respectively. With respect to the GPS + GLONASS kinematic PPP, the accuracy degradations for single-frequency and dual-frequency kinematic PPP are 2.670 m and 4.821 m, respectively.

Abstract Global navigation satellite systems (GNSS) are being the target of various jamming, spoofing, and meaconing attacks. This paper proposes a new statistical test for the presence of multiple spoofers based on range measurements observed by a plurality of receivers located on a rigid body platform. The relative positions of the receivers are known, but the location and orientation of the platform are unknown. The test is based on the generalized likelihood ratio test (GLRT) paradigm and essentially performs a consistency check between the set of observed range measurements and known information about the satellite topology and the geometry of the receiver constellation. Optimal spoofing locations and optimal artificial time delays (as induced by the spoofers) are also determined.Exact evaluation of the GLRT requires the maximum-likelihood estimates of all parameters, which proves difficult. Instead, approximations based on iterative algorithms and the squared-range least squares algorithm are derived. The accuracy of these approximations is benchmarked against Cramér-Rao lower bounds.Numerical examples demonstrate the effectiveness of the proposed algorithm and show that increasing the number of GNSS receivers makes the attack easier to detect. We also show that using multiple GNSS receivers limits the availability of optimal attack positions.

Serious mutual coupling effect must be taken into account for the miniaturized adaptive anti-jamming antenna arrays in global navigation satellite system (GNSS) receivers. In this paper, the effect of mutual coupling on the performance of GNSS antenna arrays has been comprehensively discussed. The nulling performance of the GNSS antenna array in presence of mutual coupling is efficiently evaluated by the proposed embedded element pattern method, which is further verified by a simulated miniaturized four-element GNSS antenna array in nonadaptive mode. In the space-time adaptive processing (STAP) array, a generalized wideband signal model based on the dispersion phenomenon is introduced to study the effect of mutual coupling on the optimal STAP array response, signal-to-interference-plus-noise ratio (SINR) loss, and the minimum mean square error (MMSE) of the Wiener filter. Employing the STAP normalized least mean square (NLMS) algorithm, the simulation results show that the mutual coupling leads to a decrease in convergence rate, an increase in misadjustment, and a loss of SINR, whether or not the compensation is considered.

Global Navigation Satellite Systems (GNSS) are crucial for applications that demand very accurate positioning. Tensor-based time-delay estimation methods, such as CPD-GEVD, DoA/KRF, and SECSI, combined with the GPS3 L1C signal, are capable of, significantly, mitigating the positioning degradation caused by multipath components. However, even though these schemes require an estimated model order, they assume that the number of multipath components is constant. In GNSS applications, the number of multipath components is time-varying in dynamic scenarios. Thus, in this paper, we propose a tensor-based framework with model order selection and high accuracy factor decomposition for time-delay estimation in dynamic multipath scenarios. Our proposed approach exploits the estimates of the model order for each slice by grouping the data tensor slices into sub-tensors to provide high accuracy factor decomposition. We further enhance the proposed approach by incorporating the tensor-based Multiple Denoising (MuDe).

GNSS tomography is an emerging remote sensing technique in the field of meteorology that is gaining increased attention in recent years. This method is used as a tool for atmospheric (particularly tropospheric) sensing and then applied in nowcasting and forecasting research. The tomographic approach can be used to determine the distribution of water vapor (WV), the most active component of the atmosphere. WV is one of the most important drivers of convection and precipitation. In this method, numerous line-of-sight integral observations at different locations and directions are used to derive a 3-D distribution of a WV structure. One of the challenges in GNSS tomography is that different parameterization methods are used for computing the design matrix. Here, the effect of the straight-line method versus the ray-tracing method is investigated for computing the length of a ray which passes through the model element. In addition, the effect of considering the topography of the area in the tomography model is analyzed. The accuracy of the developed model is verified using radiosonde measurements in the COST benchmark dataset. Results show that the Eikonal ray-tracing method is superior to other schemes whether used with topography or not. The mean values of RMSE of estimated wet refractivity with respect to the radiosonde profiles for these schemes are about 1.313 and 1.766 ppm, respectively. This work is conducted within COST Action ES1206 on “Advanced global navigation satellite systems tropospheric products for monitoring severe weather events and climate (GNSS4SWEC) (2013-2017)” and IAG Working Group “GNSS tomography”.

We have established a stable regional geodetic reference frame, designated as SChina20, using approximately 10-year observations (2010–2019) at 40 Global Navigation Satellite System (GNSS) sites in South China. The regional reference frame is tied to the International GNSS Service (IGS) Reference Frame 2014 (IGS14) at epoch 2020.0. The primary products from this study are seven parameters for transforming the positional time series from IGS14 to SChina20 and a seasonal model for predicting vertical land surface displacements in South China. The frame stability of SChina20 is 0.4 mm/year in all three directions. We suggest that the scope of usage of SChina20 is spatially limited to the South China region and temporally limited to the 25-year time window from 2000 to 2025. We have also established a local-scale reference frame for the Hong Kong–Zhuhai–Macau (HZM) area, designated as HZM20. Applications of SChina20 in delineating urban subsidence and landslide are demonstrated in this article. SChina20 will be updated every few years to mitigate degradation of the frame stability over time and to synchronize with future updates of the IGS reference frame. [ABSTRACT FROM AUTHOR]