Search Results (601)

This research paper presents the results of an analysis conducted on a microstrip patch antenna designed to operate within the 1.559–1.591 GHz frequency band, which encompasses three major satellite constellations: GPS, Galileo and BeiDou. The objective of this study is to perform a comparative evaluation of the materials used in the antenna design, assess the geometric configuration and analyze the key performance parameters of the proposed microstrip patch antenna. Prior to the numerical modeling and simulation process, a preliminary assessment was conducted to evaluate how different substrate materials influence antenna efficiency. For instance, a comparison between FR-4 and RT Duroid 5880 dielectric substrates revealed signal attenuation differences of approximately −1 dB at the target frequency. The numerical simulations were carried out using Ansys HFSS design. The antenna was mounted on a dielectric substrate, which was also mounted on a ground plane. The microstrip antenna was fed using a coaxial cable at a single point, strategically positioned to achieve circular polarization within the operating frequency band. The aim of this study is to design and analyze a microstrip antenna that operates within the previously specified frequency range, ensuring optimal impedance matching of 50 Ω with a return loss of S₁₁ < −10 dB at the operating frequency (with these parameters also contributing to the definition of the antenna’s operational bandwidth). Furthermore, the antenna is required to provide a gain greater than 3 dB for integration into GNSS’ receivers and to achieve an Axial Ratio value below 3 dB in order to ensure circular polarization, thereby facilitating the antenna’s integration into GNSSs. Full article

The CYGMEN (Cyprus GNSS Meteorology Enhancement) infrastructure project aims to establish a meteorological cluster (CyMETEO) in Cyprus of a lightning detection network, a dense GNSS (Global Navigation Satellite System) network for atmospheric water vapor estimation, a Radar Wind Profiler, and a microwave radiometer. Additionally, observational data generated by CyMETEO infrastructure will be assimilated into the Weather Research and Forecasting (WRF) model with the aim of improving short-term weather forecasting. The preliminary results of precipitable water vapor (PWV) estimation by employing (a) a GNSS network, (b) a microwave radiometer, (c) radiosonde, and (d) ERA5 reanalysis datasets over the Athalassas super-site in Nicosia, during May 2025, are intercompared in this study. Full article

The loss of localization in robots navigating GNSS-denied environments poses a critical challenge that can compromise mission success and safe operation. This article presents a method that fuses visual odometry outputs from both direct and feature-based (indirect) methods using Iterated Conditional Modes (ICMs), an efficient iterative optimization algorithm that maximizes the posterior probability in Markov random fields, combined with uncertainty-aware gain adjustment to perform pose estimation and mapping. The proposed method enhances the performance of visual localization and mapping algorithms in low-texture or visually degraded scenarios. The method was validated using the TUM RGB-D benchmark dataset and through real-world tests in both indoor and outdoor environments. Outdoor experiments were conducted on an electric vehicle, where the method maintained stable tracking. These initial results suggest that the technique could be transferable to electric vehicle platforms and applicable in a variety of real-world conditions. Full article

In this study, we present a novel data assimilation framework, the Ensemble Kalman Filter One-Dimensional Variational (EnKF1D-Var) framework, which assimilates observations from a Ground-based Microwave Radiometer (GMWR), a Mie–Raman Aerosol Lidar (MRL), and a Global Navigation Satellite System Meteorology sensor (GNSS/MET). The framework integrates multi-source vertical observations of water vapor and temperature with hourly temporal and 15 m vertical resolutions, driven by GFS forecasts. Three-month-long studies from May to July 2024 at Anqing Station in subtropical China demonstrate that the EnKF1D-Var retrievals reduce biases in temperature and humidity within the low troposphere, especially for daytime retrievals, by dynamically updating the observational error covariance matrices. Maximum humidity corrections reach up to 0.075 g/kg (120 PPMV), and temperature bias reductions exceed 3%. Incremental analysis reveals that the contribution to bias correction differs across instruments. GNSS/MET plays a dominant role in temperature adjustment, while GMWR provides supplementary support. In contrast, the majority of the improvements in water vapor retrieval can be attributed to MRL observations. This study achieved a reasonable application of multiple ground-based remote sensing observations, providing a new approach for the inversion of temperature and humidity profiles in the atmospheric boundary layer. Full article

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

The Hunga-Tonga volcanic eruption on 15 January 2022, produced significant atmospheric and ionospheric disturbances that may degrade global navigation satellite system (GNSS) and precise point positioning (PPP) accuracy. Using data from the GEONET GNSS network and Soratena barometric pressure sensors across Japan, we analyzed the eruption’s effects through the gradient ionospheric index (GIX) and the rate of TEC index (ROTI) to characterize the propagation and effects of these disturbances on ionospheric total electron content (TEC) gradients. Our analysis identified two separate ionospheric disturbance events. The first event, coinciding with the arrival of atmospheric Lamb waves, was characterized by wave-like pressure anomalies, differential TEC (dTEC) fluctuations, and modest horizontal gradients of vertical TEC (VTEC). In contrast, the second, more pronounced disturbance was driven by equatorial plasma bubbles (EPBs), which generated severe ionospheric irregularities and large TEC gradients. Further analysis revealed that these two disturbances had markedly different impacts on GNSS positioning accuracy. The Lamb wave–induced disturbance mainly caused moderate TEC fluctuations with limited effects on positioning accuracy, and mid-latitude stations maintained both average and 95th percentile positioning ($\mathrm{p}\mathrm{p}\mathrm{p},{\mathrm{P}}_{95}$) errors below 0.1 m throughout the event. In contrast, the EPB-driven disturbance had a substantial impact on low-latitude regions, where the average horizontal PPP error peaked at 0.5 m and the horizontal and vertical $\mathrm{p}\mathrm{p}\mathrm{p},{\mathrm{P}}_{95}$ errors exceeded 1 m. Our findings reveal two episodes of spatial-gradient enhancement and successfully estimate the propagation speed and direction of the Lamb waves, supporting the potential application of ionospheric gradient monitoring in forecasting GNSS performance degradation. Full article

Traditional landslide monitoring systems struggle to capture the spatiotemporal dynamics of rainfall-induced hydro-mechanical processes, with a significant risk of signal loss during critical “unsaturated-saturated” state transitions. To address this issue, we propose an integrated framework that utilizes FLAC3D numerical simulation to dynamically optimize multi-sensor deployments. Through coupled seepage-stress analysis under different rainfall scenarios in China’s Tianshan Mountains, this study achieved the following objectives: (1) risk-based sensor deployment by precisely identifying shallow shear strain concentration zones (5–15 m) through FLAC3D simulation (with FBG density of 0.5 m/point in the core sliding belt and GNSS spacing ≤ 50 m); (2) establishment of a multi-parameter cooperative early warning system (displacement > 50 mm/h, pore water pressure > 0.4 MPa, strain > 6400 με), where red alerts are triggered when at least two parameters exceed thresholds, reducing false alarm rates; and (3) development of an adaptive sampling framework based on three rainfall intensity scenarios, which increases measurement frequency during heavy rainfall to capture transient critical points (GNSS sampling rate enhanced to 10 Hz). This approach significantly enhances the capture capability of critical hydro-mechanical transition processes while reducing the monitoring redundancy. The framework provides a scientifically robust and reliable solution for slope disaster-risk prevention and management. Full article

Accurate user-equipment positioning is crucial for the successful deployment of 5G New Radio (NR) networks, particularly in dense urban and vehicular environments where multipath effects and signal blockage frequently compromise GNSS reliability. Building upon the pseudo-signal-word (PSW) paradigm initially developed for low-power wide-area networks, this paper proposes GeoNR-PSW, a novel localization architecture designed for sub-6 GHz (FR1, 2.8 GHz) and mmWave (FR2, 60 GHz) fingerprints from the Raymobtime S007 dataset. GeoNR-PSW encodes 5G channel snapshots into concise PSW sequences and leverages a frozen GPT-2 backbone enhanced by lightweight PSW-Adapters to enable few-shot 3D localization. Despite the limited size of the dataset, the proposed method achieves median localization errors of 5.90 m at FR1 and 3.25 m at FR2. These results highlight the potential of prompt-aligned language models for accurate and scalable 5G positioning with minimal supervision. Full article

In mountainous canyon regions, BeiDou Navigation Satellite System (BDS)/Global Navigation Satellite System (GNSS) receivers are susceptible to multireflection and tropospheric factors, which frequently reduce the accuracy in monitoring vertical deformation monitoring under short-baseline methods. This limitation hinders the application of BDS/GNSS in high-precision monitoring scenarios in those cases. To address this issue, this study proposes a three-dimensional (3D) deformation measurement method that integrates BDS/GNSS positioning with dihedral corner reflectors (CRs). By incorporating high-precision horizontal positioning results obtained from BDS/GNSS into the radar line-of-sight (LOS) correction process and utilizing ascending and descending Synthetic Aperture Radar (SAR) data for joint monitoring, the method achieves millimeter-level- accuracy in measuring vertical deformation at corner reflector sites. At the same time, it enhances the 3D positioning accuracy of BDS/GNSS to the 1 mm level under short-baseline configurations. Based on monitoring stations deployed at the Jinsha River dam site, the proposed deformation fusion monitoring method was validated using high-resolution SAR imagery from Germany’s TerraSAR-X (TSX) satellite. Simulated horizontal and vertical displacements were introduced at the stations. The results demonstrate that BDS/GNSS achieves better than 1 mm horizontal monitoring accuracy and a vertical accuracy of around 5 mm. Interferometric SAR (InSAR) CRs achieve approximately 2 mm in horizontal accuracy and 1 mm in vertical accuracy. The integrated method yields a 3D deformation monitoring accuracy better than 1 mm. This paper’s results show high potential for achieving high-precision deformation observations by fusing BDS/GNSS and dihedral CRs, offering promising prospects for deformation monitoring in reservoir canyon regions. Full article

In Global Navigation Satellite System (GNSS)-denied environments, navigation using signals of opportunity (SOP) from Low Earth Orbit (LEO) satellites is considered a feasible alternative. Compared with single-constellation systems, multiple-constellation LEO systems offer improved satellite visibility and geometric diversity, which enhances positioning continuity and accuracy. To allocate weights among heterogeneous observations, prior studies have employed the Helmert variance component estimation (HVCE) method, which iteratively determines relative weight ratios of different observation types through posterior variance estimation. HVCE enables error modeling and weight adjustment without prior noise information but is highly sensitive to outliers, making it vulnerable to their impact. This study proposes a Robust HVCE-based dual-constellation weighted positioning method. The approach integrates prior weighting based on satellite elevation, observation screening based on characteristic slopes, HVCE, and IGG-III robust estimation to achieve dynamic weight adjustment and suppress outliers. Experimental results over a 33.9 km baseline demonstrate that the proposed method attains Two-Dimensional (2D) and Three-Dimensional (3D) positioning accuracies of 12.824 m and 23.230 m, corresponding to improvements of 29% and 16% over conventional HVCE weighting, respectively. It also outperforms single-constellation positioning and equal-weighted fusion, confirming the effectiveness of the proposed approach. Full article

As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather. Full article

Ground-based LiDAR sensing offers a promising approach for delivering short-range landing feedback to aerial vehicles operating near vertiports and in GNSS-degraded environments. This work introduces a detection system capable of classifying aerial vehicles and estimating their 3D positions with sub-meter accuracy. Using a simulated Gazebo environment, multiple LiDAR sensors and five vehicle classes, ranging from hobbyist drones to air taxis, were modeled to evaluate detection performance. RGB-encoded point clouds were processed using a modified YOLOv6 neural network with Slicing-Aided Hyper Inference (SAHI) to preserve high-resolution object features. Classification accuracy and position error were analyzed using mean Average Precision (mAP) and Mean Absolute Error (MAE) across varied sensor parameters, vehicle sizes, and distances. Within 40 m, the system consistently achieved over 95% classification accuracy and average position errors below 0.5 m. Results support the viability of high-density LiDAR as a complementary method for precision landing guidance in advanced air mobility applications. Full article

Point clouds from 3D sensors such as LiDAR are increasingly used in agriculture for tasks like crop characterisation, pest detection, and leaf area estimation. While traditional point cloud processing typically occurs in Cartesian space using methods such as principal component analysis (PCA), this paper introduces a novel frequency-domain approach for point cloud registration. The central idea is that point clouds can be transformed and analysed in the spectral domain, where key frequency components capture the most informative spatial structures. By selecting and registering only the dominant frequencies, our method achieves significant reductions in localisation error and computational complexity. We validate this approach using public datasets and compare it with standard Iterative Closest Point (ICP) techniques. Our method, which applies ICP only to points in selected frequency bands, reduces localisation error from 4.37 m to 1.22 m (MSE), an improvement of approximately 72%. These findings highlight the potential of frequency-domain analysis as a powerful and efficient tool for point cloud registration in agricultural and other GNSS-challenged environments. Full article

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS. Full article

As wireless systems evolve toward multiband, multifunctional convergence and high-throughput services, the demand for ultra-wideband circularly polarized (CP) antennas for multi-standard terrestrial–satellite terminals continues to grow; however, because of the dispersive nature of the three-quarter-ring phase shifter, the relative bandwidth achievable with conventional crossed-dipole antennas rarely exceeds 100%. This paper presents a compact left-hand circularly polarized (LHCP) crossed-dipole antenna that combines a cavity-backed ground, ground-slot perturbations, and parasitic patches to simultaneously broaden the impedance and axial-ratio bandwidths. The fabricated prototype achieves an impedance bandwidth (IMBW) of 0.71–3.89 GHz (138%) and a 3 dB axial-ratio bandwidth (ARBW) of 0.98–3.27 GHz (108%), while maintaining gains above 3.5 dBic across most of the frequency range. The good agreement validates the multi-technique co-design and shows that the compact architecture (0.302 λ × 0.302 λ × 0.129 λ) breaks classical crossed-dipole limits. The antenna provides a scalable building block for wideband conformal arrays in next-generation integrated GNSS–SatCom systems. Full article