Search Results (1,293)

This study introduces an innovative GNSS-based monitoring system designed to evaluate deformation in mining headframes, effectively addressing the limitations of traditional methods, such as inadequate real-time capabilities and complex data processing requirements. The research was conducted at the Liuzhuang Mine in Anhui Province, China, where a monitoring network was established, consisting of one reference station and eight GNSS stations strategically positioned on sheave platforms and structural supports. Over a period of 66 days, high-frequency 3D deformation data were collected and processed using advanced methodologies, including cubic spline interpolation, generalized extreme studentized deviate (GESD) outlier removal, and Gaussian filtering. Spatiotemporal analysis, employing the “base state with amendments” model, indicated that 90% of the deformations (ΔX, ΔY, ΔH) were confined within ±8 mm, with more significant fluctuations observed near the sheave wheels due to mechanical stress. Correlation analysis identified the distance to the sheave wheel as the primary factor influencing horizontal deformation, with Pearson correlation coefficients exceeding 0.67, while vertical settlement remained stable. Risk thresholds, derived from statistical fluctuations, demonstrated that 99.2% of the data fell within safe limits during validation. In comparison to traditional approaches, the GNSS system delivers enhanced precision, real-time functionality, and a decreased field workload. This study presents a scalable framework for assessing headframe safety and guides the optimization of sensor placement in analogous mining settings. It is proposed that future integration with multi-source sensors, such as inertial navigation systems, will further augment monitoring robustness. Full article

In high-orbit space missions, the significant attenuation of Global Navigation Satellite System (GNSS) signals due to long transmission distances and complex environmental interferences has led to a drastic degradation in the accuracy of traditional positioning models, which has attracted great attention in recent years. Although multi-system GNSS fusion positioning can alleviate the problem of insufficient satellite visibility, the existing methods are difficult to effectively cope with the challenges of multi-source noise coupling and inter-system error differences unique to high orbit. In this paper, we propose an adaptive GNSS positioning optimization framework for a high-orbit environment, which improves the orbiting reliability under complex signal conditions through dynamic weight allocation and a multi-system cooperative strategy. Different from the traditional weighting model, this method innovatively constructs a two-layer optimization mechanism: (1) Based on BP neural network, it evaluates the noise characteristics of pseudo-range observations in real time and realizes the adaptive suppression of receiver thermal noise, ionospheric delay, etc.; (2) it introduces Helmert variance component estimation to optimize the weighting ratio of GPS, GLONASS, BeiDou, and Galileo and reduces the impact of signal heterogeneity on the positioning solution of the multi-system. Simulation results show that the new method reduces the root-mean-square error of positioning by 32.8% compared with the traditional algorithm to 97.72 m in typical high-orbit scenarios and significantly improves the accuracy loss caused by the defective satellite geometrical configurations under multi-system synergy. Full article

Remotely piloted aircraft (RPA) are essential in precision coffee farming due to their capability for continuous monitoring, rapid data acquisition, operational flexibility at various altitudes and resolutions, and adaptability to diverse terrain conditions. This study evaluated the soil water conditions in a coffee plantation using remotely piloted aircraft to obtain multispectral images and vegetation indices. Fifteen vegetation indices were chosen to evaluate the vigor, water stress, and health of the crop. Soil samples were collected to measure gravimetric and volumetric moisture at depths of 0–10 cm and 10–20 cm. Data were collected at thirty georeferenced sampling points within a 1.2 ha area using GNSS RTK during the dry season (August 2020) and the rainy season (January 2021). The highest correlation (51.57%) was observed between the green spectral band and the 0–10 cm volumetric moisture in the dry season. Geostatistical analysis was applied to map the spatial variability of soil moisture, and the correlation between vegetation indices and soil moisture was evaluated. The results revealed a strong spatial dependence of soil moisture and significant correlations between vegetation indices and soil moisture, highlighting the effectiveness of RPA and geostatistics in assessing water conditions in coffee plantations. In addition to soil moisture, vegetation indices provided information about plant vigor, water stress, and general crop health. Full article

Low Earth Orbit (LEO) satellites utilizing Doppler measurements can be an effective supplementary positioning solution when Global Navigation Satellite System (GNSS) signals are unavailable. LEO satellites pose challenges to the efficiency and stability of real-time satellite selection algorithms due to their high dynamic and large number. The traditional satellite selection algorithms have the problems of high computational complexity and significant hardware dependence. In contrast, the intelligent optimization algorithm significantly improves the accuracy and real-time performance of satellite selection through global search and efficient processing. According to the characteristics of LEO satellites, a Multi-Strategy Fusion Grey Wolf Optimization (MSFGWO) algorithm is proposed for satellite selection. The experimental results show that when six satellites are selected, the average Doppler Geometric Dilution of Precision (DGDOP) value of the MSFGWO algorithm is 222.08. Compared with the DGDOP ratio of the traversal method, it is 1.03. The three-dimensional positioning accuracy is 192.86 m, and the positioning error is improved by 54.43% compared with the positioning accuracy of the traditional GWO algorithm. The longest continuous observation was achieved for 45 s, during which no switching of six satellites occurred at adjacent moments. The calculation time of the algorithm was only 0.0174 s, and the efficiency was improved by 93.43%. The MSFGWO algorithm proposed in this paper not only improves the overall optimization ability of the Grey Wolf Optimizer (GWO) algorithm and effectively reduces the DGDOP value but also significantly reduces the satellite switching number and prolongates the continuous observation time, thus improving the stability and accuracy of the positioning solution. Full article

Atmospheric Rivers (ARs) transport significant amounts of moisture and cause extreme precipitation events, yet their behavior over Africa is not well understood. This study addresses this gap by analyzing the occurrence, seasonal variability, and spatial dynamics of ARs across the continent from 2009 to 2019. Utilizing ERA5 reanalysis data, Global Navigation Satellite Systems Radio Occultation (GNSS RO) measurements, and the Image-Processing-based Atmospheric River Tracking (IPART) method, distinct seasonal AR patterns are identified. Southern Africa experiences peak activity during austral summer, while AR occurrence in Northern Africa peaks in boreal winter and spring, aligning with regional rainy seasons. Moisture sources include the Atlantic Ocean, the Arabian Sea, and the Red Sea. A comparison of ERA5 Integrated Water Vapor (IWV) estimates with high-resolution GNSS RO data shows that both datasets effectively capture broad-scale moisture patterns. However, ERA5 consistently delivers higher IWV values compared to GNSS RO, which is likely due to underrepresentation of GNSS RO IWV values, since profiles generally do not reach all the way down to the surface—but also due to an overrepresentation of humidity in the ERA5 reanalyses. Understanding AR dynamics in Africa is essential to improve climate resilience, water management and understanding extreme precipitation events. Full article

The synthetic meta-signal reconstruction approach enables the generation of wideband Global Navigation Satellite System (GNSS) measurements from side-band observations. This approach is of particular interest for automotive market devices where, for instance, hardware constraints do not allow full-band Galileo Alternative Binary Offset Carrier (Alt-BOC) processing. In this paper, the applicability of the synthetic GNSS meta-signal paradigm is extended by introducing a half-cycle ambiguity detector for the reconstructed carrier phases and a jump detector for the pseudoranges. These accessories make the reconstruction approach more robust and suitable for mass market devices. Tests conducted using the STMicroelectronics TeseoV receiver demonstrate the validity and potential of this approach. Full article

The propagation of seismo-traveling ionospheric disturbances (STIDs) is generally observed at one specific altitude layer. On 2 April 2024, a Mw 7.4 earthquake struck Hualien, which was the biggest earthquake since the 1999 Chi-Chi earthquake in the Taiwan region. In this study, a co-located vertical monitoring system combined with the observation of two horizontal layers in the ionosphere was utilized to study the STIDs associated with the Hualien earthquake. The vertical monitoring system can capture disturbances from the ground surface up to a height of ~350 km. In addition, changes in electric currents and the TEC (total electron content) at two horizontal layers, ~100 km and ~350 km, were monitored by permanent geomagnetic stations and a ground-based GNSS (global navigation satellite system) receivers network, respectively. The observations from this four-dimensional (4D) monitoring network show that the STIDs at a height of ~100 km associated with Rayleigh waves can propagate as far as 2000 km from the epicenter, while at an altitude of ~350 km, they can only propagate to about 1000 km. At an altitude of about 200 km, STIDs were also captured by a high-frequency Doppler sounder in a vertical monitoring system, which was consistent with the results in the geomagnetic field. The results from the 4D monitoring network suggest that the STIDs associated with Rayleigh waves exhibit different propagation ranges at various altitudes and prefer to propagate at low ionosphere layers. The vertical propagating waves typically only reach the bottom of the ionosphere and struggle to propagate to higher regions over long distances. Full article

Cape Canaveral, home to critical space exploration infrastructure, is facing potential flooding hazards from land subsidence and sea-level rise. This study utilized three geodetic datasets, the Interferometric Synthetic Aperture Radar (InSAR), the Global Navigation Satellite System (GNSS), and precise leveling, to investigate the spatial and temporal patterns of vertical land motion (VLM) in Cape Canaveral and its surrounding areas. Our analysis revealed that Cape Canaveral experiences both long-term regional subsidence and localized subsiding areas, while Merritt Island and the Peninsular Mainland remain relatively stable. The long-term regional subsidence in Cape Canaveral is likely driven by the compaction of younger, unconsolidated siliciclastic sediments, with a small contribution from glacial isostatic adjustment (GIA). The three localized subsiding areas identified in Cape Canaveral are each driven by distinct mechanisms: wetland modification in the western area, runway infrastructure development in the central area, and the natural compaction of young siliciclastic sediments in the southeastern region. Historical leveling data indicated temporal variations in subsidence rates at Cape Canaveral, from 5 mm/yr during the 1950–70s to 2 mm/yr in the 2000s. These findings have significant implications for infrastructure resilience and flood hazard assessment, as the observed subsidence compounds with the projected accelerated sea-level rise in the region. Our results highlight the importance of integrating long-term datasets to better characterize VLM in the dynamic coastal region for effective planning and risk mitigation. Full article

Coaxial drones have garnered popularity owing to their energy efficiency and compact design. However, the precise navigation of these drones in complex and dynamic flight scenarios is limited by inaccuracies in heading/yaw estimation. Conventional heading estimation methods rely on magnetometers and real-time kinematic Global Navigation Satellite Systems (RTK-GNSS), which directly measure heading angle. However, the small size of microdrones restricts the placement of magnetometers away from magnetic interference and prevents the use of directional antennas. Moreover, single-antenna alignment algorithms are highly susceptible to errors caused by nonlinearity, leading to significant inaccuracies in heading estimation. To address these challenges, this paper proposes a hybrid heading estimation approach that integrates Motion-Adaptive Stabilization with an Angle-Parameterized Extended Kalman Filter (APEKF). This method utilizes low-cost GNSS, a magnetometer, and an Inertial Measurement Unit (IMU). Heading is initialized based on the drone’s static attitude, with an adaptive threshold established during takeoff to account for varying flight conditions. As the drone reaches higher altitudes, heading estimation is further stabilized. GNSS velocity observations enhance estimation accuracy through horizontal maneuvering alignment achieved by incorporating multiple sub-filter techniques and residual-based fusion. In the simulations and onboard experiments in this study, the proposed heading estimation method demonstrated a precision of approximately 1.01° post-takeoff, with the alignment speed enhanced by 43%. Moreover, the method outperformed existing estimation techniques and, owing to its low computational overhead, can serve as a reliable full-stage backup across various drone applications. Full article

The Earth’s time-variable gravity field holds significant research and application value. However, satellite gravimetry missions such as GRACE and GRACE-FO face limitations in spatial resolution when detecting monthly gravity fields, while traditional radar altimeters lack the observational efficiency needed for monthly gravity anomaly inversion. These limitations hinder further exploration and application of the Earth’s time-variable gravity field. Leveraging its advantages, such as rapid global coverage, high revisit frequency, and low cost for constellation formation, spaceborne GNSS-R technology holds the potential to address the observational efficiency gaps of traditional radar altimeters. This study presents the first assessment of the capability of spaceborne GNSS-R interferometric altimetry for high spatial resolution monthly marine gravity anomaly inversion through simulations. The results indicate that under the PARIS Operational scenario of a single GNSS-R satellite (a spaceborne GNSS-R interferometric altimetry scenario proposed by Martin-Neira), a 30′ grid resolution marine gravity anomaly can be inverted with an accuracy of 4.93 mGal using one month of simulated data. For a dual-satellite constellation, the grid resolution improves to 20′, achieving an accuracy of 4.82 mGal. These findings underscore the promise of spaceborne GNSS-R interferometric altimetry technology for high spatial resolution monthly marine gravity anomaly inversion. Full article

As the peak of solar cycle 25 approaches, increased ionospheric and scintillation activity is being observed, which is negatively impacting the quality of GNSS measurements and presenting challenges in the positioning domain. Ionospheric refraction and diffraction introduce delays and distortions to GNSS carrier phase measurements, leading to positioning errors that exceed the anticipated accuracies. These position errors can be a significant concern for users across the world who depend on precise GNSS positioning, such as in agriculture, offshore marine positioning and autonomous automotive positioning. To understand the direct impact on NovAtel receivers and its positioning engines, a comprehensive analysis was conducted. A closer look was taken at what happened in 2023–2024 by characterizing scintillation using the amplitude scintillation index (S4) values in an equatorial region. Additionally, the scintillation effect on the receivers was characterized through the analysis of C/N0, lock breaks, double differences and other indicators. With a substantial amount of data collected at 20° latitude, where high solar activity occurs due to the proximity to the equator, the positioning performance of Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) was analyzed. Full article

Extended Kalman Filter (EKF) is extensively employed in Global Navigation Satellite System (GNSS)-based real-time orbit determination (RTOD) for microsatellites due to its low complexity. However, the performance of EKF-RTOD is markedly degraded when the microsatellite deviates from a stable Earth-pointing attitude and employs a low-cost receiver. Factor graph optimization (FGO), which addresses nonlinear problems through multiple iterations and re-linearization, has demonstrated superior accuracy and robustness compared to EKF in challenging environments such as urban canyons. In this study, we introduce a novel FGO-based RTOD (FGO-RTOD) approach, which integrates state transfer factors to establish temporal connections between state nodes across multiple epochs. Real-time processing is achieved through a sliding window mechanism combined with marginalization. This paper evaluates the performance of the proposed algorithm in a regular scenario using data from GRACE-FO-A, which maintains the Earth-pointing attitude and employs a high-performance receiver. The positioning results of GRACE-FO-A indicate that FGO-RTOD marginally outperforms EKF-RTOD in accuracy. Furthermore, the performance of FGO-RTOD is assessed in challenging scenarios using simulation data and on-orbit data from Tianping-2B microsatellite, which is not in an Earth-pointing attitude and employs a low-cost receiver. The simulation results reveal that FGO-RTOD reduces the Root Mean Square (RMS) of positioning error by 79.0% relative to EKF-RTOD and exhibits significantly enhanced smoothing. In the Tianping-2B experiments, FGO-RTOD reduces the RMS of carrier-phase ionosphere-free combination residuals from 2 cm to 1 cm relative to EKF-RTOD, alongside a substantial improvement in the ratio of valid observations. These findings underscore the effectiveness of FGO-RTOD in managing outlier measurements in challenging scenarios. Full article

Although the traditional Carrier-to-Code Leveling (CCL) method can provide ideal slant total electron content (STEC) observables for establishing ionospheric models, it must rely on dual-frequency (DF) receivers, which results in high hardware costs. In this study, an ionosphere-weight (IW) single-frequency (SF) precise point positioning (PPP) method for extracting STEC observables is proposed, and multi-global navigation satellite system (GNSS)-integrated processing is adopted to improve the spatial resolution of the ionospheric model. To investigate the advantages of this novel method, 41 European stations are used to establish the regional ionospheric model, and both low- and high-solar-activity conditions are considered. The results show that the IW SFPPP-derived regional ionospheric model has a significantly better quality of vertical total electron content (VTEC) than the CCL method when using the final global ionospheric map (GIM) as a reference, especially in areas with sparse monitoring stations. Compared with the CCL method, the RMS VTEC accuracy of the IW SFPPP method can be improved by 17.4% and 12.7% to 1.09 and 2.83 total electron content unit (TECU) in low- and high-solar-activity periods, respectively. Regarding GNSS carrier-phase-derived STEC variation (dSTEC) as the reference, the dSTEC accuracy of the IW SFPPP method is comparable to that of the CCL method, and its RMS values are about 1.5 and 2.8 TECU in low- and high-solar-activity conditions, respectively. This indicates that the proposed method using SF-only observations can achieve the same external accord accuracy as the CCL method in regional ionospheric modeling. Full article

Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, and signalling. However, remote sensing data from Earth Observation (EO) satellites remain comparatively underutilized in railway applications. While the use of GNSS data in railways is well documented in the literature, research on EO-based remote sensing methods remains relatively limited. This paper aims to bridge this gap as it presents a comprehensive review of the use of satellite data in railway applications, with a particular focus on the underexplored potential of EO data. It provides the first in-depth analysis of EO techniques, primarily examining the use of synthetic aperture radar (SAR) and optical satellite data for key applications for infrastructure managers and railway operators, such as assessing track stability, detecting deformations, and monitoring surrounding environmental conditions. The goal of this review is to explore the diverse range of EO-based applications in railways and to identify emerging trends, including the integration of thermal EO data and the novel use of SAR for dynamic and predictive analyses. By synthesizing existing research and addressing knowledge gaps, the presented review underscores the potential of EO data to transform railway infrastructure management. Enhanced spatial resolution, frequent revisit cycles, and advanced AI-driven analytics are highlighted as key enablers for safer, more reliable, and cost-effective solutions. This review provides a framework for leveraging EO data to drive innovation and improve railway monitoring practices. Full article

On 1 December 2023, a strong geomagnetic storm was triggered by an interplanetary shock caused by a coronal mass ejection (CME). This study used data from 193 Global Navigation Satellite System (GNSS) observation stations in China to study the three-dimensional morphological total electron content (TEC) disturbances during this storm. By analyzing GNSS TEC data from 15 GNSS stations along the magnetic field lines, it was found that TEC disturbances spread from low to high latitudes, confirmed by ionosonde NmF2 data. The TEC disturbance first appeared at the LJHP station, (21.68° N) at 11:30 UT and propagated to the BJFS station (39.60° N) at 13:30 UT with a propagation speed of about 217 m/s and maximum amplitude of ±0.2 m. The TEC disturbance lasted the longest, approximately 4 h, between latitudes 25° N and 32° N. Additionally, this study investigated the ionosphere’s three-dimensional electron density distribution in the Guangxi region using an ionospheric tomography algorithm. Results showed that the TEC disturbances were mainly concentrated between 450 and 580 km in altitude. At 12:00 UT, the maximum change in electron density occurred at a 580 km height at 26° N, 112° E, increasing by 20.54 total electron content unit (TECU). During the main phase of the geomagnetic storm, the electron density expanded from higher to lower layers, while during the recovery phase, it recovered from the lower layers to the higher layers. Full article