Search Results (190)

A practical methodology for estimating regionally optimized Klobuchar coefficients using only International GNSS Service (IGS) Global Ionosphere Map (GIM) data is proposed. The method preserves computational simplicity, enabling near-real-time corrections suitable for accurate GNSS positioning. Utilizing both slant and vertical total electron content (STEC and VTEC) values extracted from GIM as inputs to estimate eight Klobuchar coefficients, robust parameter sets were obtained. Root mean square error (RMSE) analysis was used to compare these models to the standard Klobuchar model. Comprehensive performance evaluations using STEC-derived parameters, encompassing both seasonal and spatial analyses across South Korea, demonstrated significant reductions in ionospheric delay errors, with improvements reaching up to 57% compared to the conventional Klobuchar model. The far less computationally intensive VTEC-based model was applied over a wider region with 120 grid points. Continuous testing of this model over an entire year confirmed consistent enhancements in correction accuracy every day, demonstrating stable performance throughout the period. The developed regional Klobuchar models were further validated indirectly through satellite positioning performance, demonstrating daily RMSE improvements over the standard Klobuchar model ranging from 17.3% to 44.6%. Full article

Accurate ionospheric channel estimation is of great significance for acquisition of ionospheric structure, error correction of remote sensing data, high-precision Synthetic Aperture Radar (SAR) imaging, over-the-horizon (OTH) detection, and the establishment of stable communication links. Traditional super-resolution channel estimation algorithms face challenges in terms of multipath correlation and noise interference when estimating ionospheric channel information. Meanwhile, some super-resolution algorithms struggle to meet the requirements of real-time measurement due to their high computational complexity. In this paper, we propose the Cross-correlation Oblique Projection Pursuit (CC-OPMP) algorithm, which constructs an atom selection strategy for anti-interference correlation metric and a dual-space multipath separation mechanism based on a greedy framework to effectively suppress noise and separate neighboring multipath components. Simulations demonstrate that the CC-OPMP algorithm outperforms other algorithms in both channel estimation accuracy and computational efficiency. Full article

Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step toward fostering rigorous and reproducible evaluation of AI models for ionospheric forecasting by introducing IonoBench: a benchmarking framework that employs a stratified data split, balancing solar intensity across subsets while preserving 16 high-impact geomagnetic storms (Dst ≤ $-100$ nT) for targeted stress testing. Using this framework, we benchmark a field-specific model (DCNN) against state-of-the-art spatiotemporal architectures (SwinLSTM and SimVPv2) using the climatological IRI 2020 model as a baseline reference. DCNN, though effective under quiet conditions, exhibits significant degradation during elevated solar and storm activity. SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to $32.1\%$ across various subsets under diverse solar conditions. The reported results highlight the value of cross-domain architectural transfer and comprehensive evaluation frameworks in ionospheric modeling. With IonoBench, we aim to provide an open-source foundation for reproducible comparisons, supporting more meticulous model evaluation and helping to bridge the gap between ionospheric research and modern spatiotemporal deep learning. Full article

The BeiDou-3 Navigation Satellite System (BDS-3) precise point positioning (PPP) service through the B2b signal (PPP-B2b) leverages precise correction data disseminated by satellites to eliminate or mitigate key error sources, including satellite orbit errors, clock biases, and ionospheric delays, thereby enabling high-precision timing and positioning. This paper investigates the disparities in time comparison and positioning capabilities associated with the PPP-B2b signals transmitted by the BDS-3 Geostationary Earth Orbit (GEO) satellites (C59 and C61). Three stations in the Asia–Pacific region were selected to establish two time comparison links. The study evaluated the time transfer accuracy of PPP-B2b signals by analyzing orbit and clock corrections from BDS-3 GEO satellites C59 and C61. Using multi-GNSS final products (GBM post-ephemeris) as a reference, the performance of PPP-B2b-based time comparison was assessed. The results indicate that while both satellites achieve comparable time transfer accuracy, C59 demonstrates superior stability and availability compared to C61. Additionally, five stations from the International GNSS Service (IGS) and the International GNSS Monitoring and Assessment System (iGMAS) were selected to assess the positioning accuracy of PPP-B2b corrections transmitted by BDS-3 GEO satellites C59 and C61. Using IGS/iGMAS weekly solution positioning results as a reference, the analysis demonstrates that PPP-B2b enables centimeter-level static positioning and decimeter-level simulated kinematic positioning. Furthermore, C59 achieves higher positioning accuracy than C61. Full article

This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI employs lobster-eye optics to perform panoramic imaging of the magnetosphere based on the Solar Wind Charge Exchange (SWCX) mechanism. However, several factors are expected to hinder future in-orbit observations, including the intrinsically low signal-to-noise ratio (SNR) of soft-X-ray emission, pronounced vignetting, and the non-uniform effective-area distribution of lobster-eye optics. These limitations could severely constrain the accurate interpretation of magnetospheric structures—especially the magnetopause boundary. To address these challenges, a boundary detection approach is developed that combines image calibration with denoising based on deep image prior (DIP). The method begins with calibration procedures to correct for vignetting and effective area variations in the SXI images, thereby restoring the accurate brightness distribution and improving spatial uniformity. Subsequently, a DIP-based denoising technique is introduced, which leverages the structural prior inherent in convolutional neural networks to suppress high-frequency noise without pretraining. This enhances the continuity and recognizability of boundary structures within the image. Experiments use ideal magnetospheric images generated from magnetohydrodynamic (MHD) simulations as reference data. The results demonstrate that the proposed method significantly improves the accuracy of magnetopause boundary identification under medium and high solar wind number density conditions (N = 10–20 cm⁻³). The extracted boundary curves consistently achieve a normalized mean squared error (NMSE) below 0.05 compared to the reference models. Additionally, the DIP-processed images show notable improvements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), indicating enhanced image quality and structural fidelity. This method provides adequate technical support for the precise extraction of magnetopause boundary structures in soft X-ray observations and holds substantial scientific and practical value. Full article

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article

Electrically charged particles present in this layer of the Earth’s atmosphere can alter radio waves, such as those from GPS, Galileo, or BeiDou, resulting in non-estimated errors with respect to the available navigation models for the end user. For most positioning algorithms based in sequential filters, this effect is translated into a slow convergence towards a solution around the decimeter error level. If we consider that the ionosphere’s effect varies based on the user’s location and solar activity due to the atmosphere particle composition, it becomes clear that a global accurate model, valid across wide areas accounting for different seasons and timespans, is, at the very least, quite challenging. The focus of this paper is the demonstration of a global ionosphere model designed to improve the positioning accuracy of the end user through the estimation of ionospheric corrections to the broadcasted navigation message. Mathematically, this method is based on a spherical harmonic expansion model. This approach has the advantage of reducing the dependency from a highly densified station network where the ionosphere delay must be constantly estimated in dozens of locations, in favor of a simplified model that barely needs to be adjusted with a limited set of real-time data (around 40 stations). In this case, GMV’s global station network was used, which comprises geodetic-grade receivers tracking the signal in open-sky locations around the globe. The global ionospheric model is configured to process signals from GPS and Galileo constellations. To evaluate the performances of this model on the final user position estimation, several precise point positioning (PPP) solutions were computed at different locations. The results were compared with PPP solutions calculated without ionospheric corrections at the same stations. The goal of this paper is to show the significant performance improvement observed with the implementation of the global model. Full article

Precise point positioning–real-time kinematic (PPP-RTK) enables users to achieve rapid centimeter-level absolute positioning accuracy within a few epochs. The interpolation of ionospheric delay corrections at the user end, extracted from reference stations, constitutes a key aspect of the process, which depends not solely on the precision of the interpolation model. This study investigates the recommended number of selected reference stations and proposes a method to mitigate the potential loss of observations due to missing ionospheric corrections. According to the experimental results, the number of reference stations should be determined based on the reference network size. Under normal conditions (terrain is relatively flat and the atmospheric conditions are inactive) where reference stations are approximately evenly distributed in all directions, and using low-order surface interpolation model, for networks with 50 km spacing, four or five reference stations are recommended, while for 100 km networks, six or seven stations are enough to calculate precise corrections. Adding more stations beyond these thresholds provides limited improvement in interpolation accuracy and increases the communication load. In addition, an interpolation basis recovery algorithm is proposed to preserve otherwise excluded satellite observations through intelligent handling of correction data gaps at individual reference stations. Experimental validation demonstrates that the recovered ionospheric delay corrections obtained through the algorithm deviate from the ground-truth interpolated values of no more than ±1 cm, an accuracy level deemed adequate for PPP-RTK applications. Furthermore, approximately 3% of the observations, which would otherwise have been discarded due to the missing corrections from a specific reference station, are retained by the algorithm. Full article

Enhanced real-time onboard orbit determination for low-Earth-orbit satellites is essential for autonomous spacecraft operations. However, the accuracy of such systems is often limited by signal propagation delays between GPS satellites and the user spacecraft. These delays, primarily due to Earth’s rotation and ionospheric effects become particularly significant in high-dynamic LEO environments, leading to considerable errors in range and range rate measurements, and consequently, in position and velocity estimation. To mitigate these issues, this paper proposes a real-time orbit determination algorithm that applies Earth rotation correction and dual-frequency (L1 and L2) ionospheric compensation to raw GPS measurements. The enhanced orbit determination method is processed directly in the Earth-centered Earth-fixed frame, eliminating repeated coordinate transformations and improving integration with ground-based systems. The proposed method employs a reduced-dynamic orbit determination strategy to balance model fidelity and computational efficiency. A predictive correction model is also incorporated to compensate for GPS signal delays under dynamic motion, thereby enhancing positional accuracy. The overall algorithm is embedded within an extended Kalman filter framework, which assimilates the corrected GPS observations with a stochastic process noise model to account for dynamic modeling uncertainties. Simulation results using synthetic GPS measurements, including pseudoranges and pseudorange rates from a dual-frequency spaceborne receiver, demonstrate that the proposed method provides a significant improvement in orbit determination accuracy compared to conventional techniques that neglect signal propagation effects. These findings highlight the importance of performing orbit estimation directly in the Earth-centered, Earth-fixed reference frame, utilizing pseudoranges that are corrected for ionospheric errors, applying reduced-dynamic filtering methods, and compensating for signal delays. Together, these enhancements contribute to more reliable and precise satellite orbit determination for missions operating in low Earth orbit. Full article

This study investigates the ionospheric response over China during the geomagnetic storm that occurred on 1–2 December 2023. The data used include GPS measurements from the Crustal Movement Observation Network of China, BDS-GEO satellite data from IGS MEGX stations, [O]/[N₂] ratio information obtained by the TIMED/GUVI, and electron density (Ne) observations from Swarm satellites. The Prophet time series forecasting model is employed to detect ionospheric anomalies. VTEC variations reveal significant daytime increases in GNSS stations such as GAMG, URUM, and CMUM after the onset of the geomagnetic storm on 1 December, indicating a dayside positive ionospheric response primarily driven by prompt penetration electric fields (PPEF). In contrast, the stations JFNG and CKSV show negative responses, reflecting regional differences. The [O]/[N₂] ratio increased significantly in the southern region between 25°N and 40°N, suggesting that atmospheric gravity waves (AGWs) induced thermospheric compositional changes, which played a crucial role in the ionospheric disturbances. Ne observations from Swarm A and C satellites further confirmed that the intense ionospheric perturbations were consistent with changes in VTEC and [O]/[N₂], indicating the medium-scale traveling ionospheric disturbance was driven by atmospheric gravity waves. Precise point positioning (PPP) analysis reveals that ionospheric variations during the geomagnetic storm significantly impact GNSS positioning precision, with various effects across different stations. Station GAMG experienced disturbances in the U direction (vertical positioning error) at the onset of the storm but quickly stabilized; station JFNG showed significant fluctuations in the U direction around 13:00 UT; and station CKSV experienced similar fluctuations during the same period; station CMUM suffered minor disturbances in the U direction; while station URUM maintained relatively stable positioning throughout the storm, corresponding to steady VTEC variations. These findings demonstrate the substantial impact of ionospheric disturbances on GNSS positioning accuracy in southern and central China during the geomagnetic storm. This study reveals the complex and dynamic processes of ionospheric disturbances over China during the 1–2 December 2023 storm, highlighting the importance of ionospheric monitoring and high-precision positioning corrections during geomagnetic storms. The results provide scientific implications for improving GNSS positioning stability in mid- and low-latitude regions. Full article

In this study, we introduce a decentralized architecture aimed at enhancing the efficiency of precise point positioning real-time kinematics (PPP-RTK) in large-scale networks with a common pivot station. Initially, we partition the extensive network into multiple smaller subnetworks (SNs), each with a common pivot station. The augmentation parameters for each SN are then computed using the precise orbit corrections and ionosphere-weighted constraints. However, directly applying the estimated augmentation parameters to users across subnetworks poses challenges due to inter-subnetwork discontinuities. These discontinuities arise from variations in the network configurations and the time correlation of the Kalman filters, despite the use of the same pivot station. To address this, common augmentation parameters, such as the satellite clocks and phase biases from each SN, are integrated into a unified set of parameters and broadcast to users. The aligned common augmentation parameters are then fed back into each SN, and the Kalman filter is re-updated to mitigate the inter-subnetwork discontinuities. The proposed architecture offers a reduced computational burden compared to the centralized PPP-RTK architecture, which handles a full-scale network simultaneously. Unlike previous research on decentralized PPP-RTK, the use of a common pivot station ensures a consistent basis for the common augmentation parameters. This approach enables seamless user positioning during transitions between SNs, eliminating the need to reset the user navigation filter during handover operations and simplifying the integration process. To evaluate the effectiveness of our proposed architecture, we gather dual-frequency global positioning system (GPS) observation data from over 40 continuously observed reference stations (CORSs) in Korea. These data are then partitioned into four SNs, each sharing a common pivot station. Subsequently, we compare the static positioning error and processing time of our proposed architecture with those of the centralized architecture. Additionally, the mitigation performance of the inter-network discontinuities is shown. Full article

Real-time ionospheric products can accelerate the convergence of real-time precise point positioning (PPP) to improve the real-time positioning services of global navigation satellite systems (GNSSs), as well as to achieve continuous monitoring of the ionosphere. This study applied an extended Kalman filter (EKF) to total electron content (TEC) modeling, proposing a regional real-time EKF-based ionospheric model (REIM) with a spatial resolution of 1° × 1° and a temporal resolution of 1 h. We examined the performance of REIM through a 7-day period during geomagnetic storms. The post-processing model from the China Earthquake Administration (IOSR), CODG, IGSG, and the BDS geostationary orbit satellite (GEO) observations were utilized as reference. The consistency analysis showed that the mean deviation between REIM and IOSR was 0.97 TECU, with correlation coefficients of 0.936 and 0.938 relative to IOSR and IGSG, respectively. The VTEC mean deviation between REIM and BDS GEO observations was 4.15 TECU, which is lower than those of CODG (4.68 TECU), IGSG (5.67 TECU), and IOSR (6.27 TECU). In the real-time single-frequency PPP (RT-SF-PPP) experiments, REIM-augmented positioning converges within approximately 80 epochs, and IGSG requires 140 epochs. The REIM-augmented east-direction positioning error was 0.086 m, smaller than that of IGSG (0.095 m) and the Klobuchar model (0.098 m). REIM demonstrated high consistencies with post-processing models and showed a higher accuracy at IPPs of BDS GEO satellites. Moreover, the correction results of the REIM model are comparable to post-processing models in RT-SF-PPP while achieving faster convergence. Full article

High-Frequency (HF) radar is well suited to the surveillance of low-earth-orbit space. For large targets, a small deployable HF radar is able to match the detection performance of much larger space surveillance radar systems operating at higher frequencies. However, there are some unique challenges associated with the use of HF, including the range–Doppler coupling bias, coarse detection-level localisation, and the presence of meteor returns and other unwanted signals. This paper details the use of HF radar for space surveillance, including signal processing and radar product formation, tracking, ionospheric correction, and orbit determination. It is shown that by fusing measurements from multiple passes, accurate orbital estimates can be obtained. Included are results from recent SpaceFest trials of the Defence Science and Technology Group’s HF space surveillance radar, achieving real-time wide-area surveillance in tracking, orbit determination, and cueing of other space surveillance sensors. Full article

In the conventional PPP-RTK mode, the platform and users act only as the generator and the utilizer of ionospheric corrections, respectively. In sparse reference station networks or regions with an active ionosphere, high-precision modeling still faces challenges. This study utilizes the concept of crowdsourcing and treats users as dynamic reference stations. By continuously feeding back ionospheric information to the platform, high-spatial-resolution modeling is achieved. Additionally, weight factors related to user positions are incorporated into conventional polynomial models to transform the regional ionosphere model from a common model into customized models, thereby providing more personalized services for different users. Validation was conducted with a sparse reference network with an average inter-station distance of approximately 391 km. While increasing the number of crowdsourcing users generally improves modeling performance, the enhancement also depends on their spatial distribution; that is, crowdsourcing users primarily provide localized improvements in their vicinity. Therefore, crowdsourcing users should ideally be uniformly distributed across the whole network. Compared with the conventional common model, the proposed customized model can more effectively characterize the irregular physical characteristics of the ionosphere, and the modeling accuracy is improved by about 12% to 41% in different scenarios. Furthermore, the performance of single-frequency PPP-RTK was verified on the terminal. In general, both crowdsourcing enhancement and the customized model can accelerate the convergence speed of the float solutions and improve positioning accuracy to varying degrees, and the epoch fix rate of the fixed solutions is also significantly improved. Full article

This study addresses the issue of atmospheric delay correction for the rational polynomial coefficient (RPC) model associated with spaceborne synthetic aperture radar (SAR) imagery under conditions lacking ephemeris data, proposing a novel approach to enhance the geometric positioning accuracy of RPC models. A satellite position inversion method based on the vector-autonomous intersection technique was developed, incorporating ionospheric delay and neutral atmospheric delay models to derive atmospheric delay errors. Additionally, an RPC model reconstruction approach, which integrates atmospheric correction, is proposed. Validation experiments using GF-3 satellite imagery demonstrated that the atmospheric delay values obtained by this method differed by only 0.0001 m from those derived using the traditional ephemeris-based approach, a negligible difference. The method also exhibited high robustness in long-strip imagery. The reconstructed RPC parameters improved image-space accuracy by 18–44% and object-space accuracy by 19–32%. The results indicate that this approach can fully replace traditional ephemeris-based methods for atmospheric delay extraction under ephemeris-free conditions, significantly enhancing the geometric positioning accuracy of SAR imagery RPC models, with substantial application value and development potential. Full article