Search Results (470)

In modern Global Navigation Satellite Systems (GNSS), interleavers are essential for improving system performance. However, limited research has focused on interleaver hardware optimization specifically for GNSS, primarily due to GNSS authorities strictly adhering to Interface Control Documents (ICDs), which discourage significant deviations in hardware implementations. Traditional GNSS interleavers employ double buffering and typically require extensive hardware resources, specifically 2N registers and 2(N$-$ 1) $+$ 1 multiplexers (MUXs), leading to increased complexity, power consumption, and latency. To bridge this gap, this paper presents a novel and efficient interleaver hardware architecture optimized specifically for modern GNSS by leveraging Lifetime Analysis and Forward–Backward Register Allocation. Lifetime Analysis precisely identifies the minimum number of registers necessary, and Forward–Backward Register Allocation ensures efficient utilization of these resources under continuous operation. Experiments were conducted for all seven currently used GNSS interleaving configurations. Synthesis results, obtained using a standard 28 nm Complementary Metal-Oxide Semiconductor (CMOS) process, demonstrate significant reductions in chip area (38.82% reduction), power consumption (52.01% reduction), and latency (25.05% improvement) compared to conventional architectures. These benefits become increasingly critical as the interleaver data length N continues to grow, providing a practical, ICD-compliant solution for resource-constrained satellite payloads and receiver designs. Full article

Navigation errors due to drifting in inertial systems using low-cost sensors are some of the main challenges for land vehicle navigation in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose an autonomous navigation strategy with a wheel-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU), referred to as the wheeled inertial navigation system (INS), to effectively suppress drifted navigation errors. The position, velocity, and attitude (PVA) of the vehicle are predicted through the inertial mechanization algorithm, while gyro outputs are utilized to derive the vehicle’s forward velocity, which is treated as an observation with non-holonomic constraints (NHCs) to estimate the inertial navigation error states. To establish a theoretical foundation for wheeled INS error characteristics, a comprehensive system observability analysis is conducted from an analytical point of view. The wheel rotation significantly improves the observability of gyro errors perpendicular to the rotation axis, which effectively suppresses azimuth errors, horizontal velocity, and position errors. This leads to the superior navigation performance of a wheeled INS over the traditional odometer (OD)/NHC/INS. Moreover, a hybrid extended particle filter (EPF), which fuses the extended Kalman filter (EKF) and PF, is proposed to update the vehicle’s navigation states. It has the advantages of (1) dealing with the system’s non-linearity and non-Gaussian noises, and (2) simultaneously achieving both a high level of accuracy in its estimation and tolerable computational complexity. Kinematic field test results indicate that the proposed wheeled INS is able to provide an accurate navigation solution in GNSS-denied environments. When a total distance of over 26 km is traveled, the maximum position drift rate is only 0.47% and the root mean square (RMS) of the heading error is 1.13°. Full article

Total Electron Content (TEC) maps allow the evaluation of the state of the ionosphere. There are many providers/sources of worldwide or regional TEC maps for the continuous monitoring of the ionosphere, which employ different GNSS monitoring networks for data acquisition, TEC calculation or interpolation methods for generating the maps, or different spatial and temporal resolutions and coverage. How reliable are TEC maps over Brazil? We employed TEC maps from four different providers for 2022–2024, in the growing phase of the current solar cycle 25. Seasonality is also taken into account. A systematic comparison of TEC maps over Brazil was performed using correlation and similarity analysis between maps of different sources. Significant differences were found. Even for the same source there are differences in the density of monitoring stations according to the region. An example of bubble signature in TEC maps is also analyzed. Ground truth validation of TEC is performed by comparing TEC point values extracted from the maps with values derived from a set of GNSS stations over Brazil. As a result, no TEC maps of these sources were deemed reliable, due to low spatial and/or temporal resolution, low monitoring station density, or inadequate interpolation scheme. Full article

This study investigates the horizontal positioning accuracy of a low-cost, multi-frequency GNSS receiver operating in static mode using a newly released PPP-RTK correction service delivering localized corrections. To the authors’ knowledge, this represents one of the first performance evaluations of this service, which optimizes correction data based on the approximate receiver location. The results are compared against those from the previous version of the service, which provided non-localized corrections. Analyses were conducted in both the time and frequency domains, employing robust statistical tools to characterize error behavior. The localized service achieved a mean horizontal error of approximately 0.020 m and a 95% Circular Error Probable (CEP95) of 0.046 m, in line with its declared performance. By contrast, the earlier non-localized service yielded a mean horizontal error of approximately 0.074 m and a CEP95 of 0.124 m under comparable static conditions, confirming the significant improvement achieved by localized corrections. Spectral and wavelet analyses revealed a dominant 33 mHz harmonic in the positioning error, corresponding to the 30 s update period of atmospheric corrections, indicating a periodic influence arising from the correction stream. Continuous wavelet analysis further identified intervals in which this harmonic was absent, during which positioning accuracy improved markedly (CEP95 reduced to 0.019 m). To properly address the non-Gaussian nature of the error distribution, bias-corrected and accelerated (BCa) bootstrap methods were applied to estimate confidence intervals. Overall, the results demonstrate the benefits of localized corrections, while emphasizing the importance of accounting for the temporal structure of correction data in PPP-RTK performance assessments. Future developments will focus on kinematic scenarios and adaptive filtering strategies to mitigate periodic errors induced by correction updates. Full article

The emerging development of Global Navigation Satellite System (GNSS) software receivers has opened new opportunities in diverse operations. However, non-line-of-sight (NLOS) concatenated signal reception is one prevalent deterioration factor causing positioning errors in urban scenarios. To enhance integrity and reliability through receiver autonomous integrity monitoring (RAIM) techniques in urban environments, distinguishing between line-of-sight (LOS) and NLOS signals facilitates the exclusion of NLOS channels: this is challenging due to uncertain signal reflections/refractions from diverse obstruction conditions in the built environment. Moreover, NLOS features show similarity to multipath effects like scattering and diffraction which causes difficulty in identifying the NLOS type. Recent work exploited NLOS detections with multi-correlator outputs using neural networks that outperform using signal strength techniques for NLOS detection. This paper proposes a neural network approach designed to recognise and learn spatial features among early, late, and prompt correlator outputs, differentiating between correlations, and also by memorising temporal features to acquire propagation information. Specifically, the spatial features of correlator IQ streams are derived from convolutional layers incorporated with concatenations, to formulate associate models like early-minus-late discrimination. A Recurrent Neural Network (RNN), i.e., long short-term memory (LSTM), is integrated to obtain comprehensive temporal features; hereby, a softmax classifier is appended in the last layer to distinguish between NLOS and LOS signals. By simulating synthetic datasets generated by a Spirent simulator and captured by a software-defined radio (SDR), the correlator outputs are acquired during the scalar tracking stage. The product of the proposed network demonstrates high performance in terms of accuracy, time consumption and sensitivity, affirming the efficiency of utilising early-stage correlations for NLOS detection. Moreover, an impact analysis of varying the sliding window length on NLOS discrimination underscores the need to fine-tune the parameter, as well as balancing accuracy, operation complexity and sensitivity. Full article

Mobile Mapping Systems (MMSs) have emerged as indispensable instruments for producing high-precision road maps in recent years. Despite incorporating modern devices, their efficacy may be influenced by operational variables such as vehicle speed or the type of GNSS (Global Navigation Satellite System) differential correction employed. This study assesses the impact of varying vehicle speeds and differential correction settings on the accuracy of point grids acquired with an MMS on a two-lane rural road. The experiment was performed across a 7 km distance, incorporating two speeds (40 and 60 km/h) and two travel directions. Three correction methodologies were examined: a proximate local base (MBS), a network station solution of the National Geographic Institute (NET), and virtual reference stations (VRSs). The methodology encompassed normality analysis, descriptive statistics, mean comparisons, one- and two-factor analysis of variance (ANOVA), and the computation of the root mean square error (RMSE) as a measure of accuracy. The findings indicate that horizontal discrepancies remain steady and unaffected by the correction technique; however, notable changes are seen in the vertical component, with the NET option proving to be the most effective. The acquisition rate is the primary determinant, exacerbating errors at 60 km/h. In conclusion, the dependability of MMS surveys is contingent upon the correction approach and operational conditions, and it is advisable to sustain moderate speeds to guarantee precise three-dimensional models. Full article

Precipitable water vapor (PWV) is an important component of atmospheric spatial parameters and plays a vital role in meteorological studies. In this study, PWV retrieval by real-time precise point positioning (PPP) technique is validated by using global navigation satellite system (GNSS) observations and four real-time products from different analysis centers, which are Centre National d’Etudes Spatiales (CNES), Internation GNSS Service (IGS), Japan Aerospace Exploration Agency (JAXA), and Wuhan University (WHU). To comparatively analyze the performance of each scenario, the single-system (GPS/Galileo/BDS3), and multi-system (GPS + Galileo + BDS) PPP techniques are applied for zenith tropospheric delay (ZTD) and PWV retrieval. Then, the ZTD and PWV are evaluated by comparison with the IGS final ZTD product, the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data, and radiosondes observations provided by the University of Wyoming. Experimental results demonstrate that the root mean squares error (RMS) of ZTD differences from multi-system solutions are below 11 mm with respect to the four-product series and the RMS of PWV differences are below 3.5 mm. As for single-system solution, the IGS real-time products lead to the worst accuracy compared with the other products. Besides the scenario of BDS3 observations with IGS real-time products, the RMS of ZTD differences from the GPS-only and Galileo-only solutions are all less than 15 mm compared to the four-product series, as well as the RMS of PWV differences is under 5 mm, which meets the accuracy requirement for GNSS atmosphere sounding. Full article

Paddy field leveling is the foundation of high-yield rice cultivation. In response to the current issues of low leveling accuracy and the lack of efficient multi-operation machinery, an Integrated Multi-operation Paddy Field Leveling Machine was designed in this study. This machine can complete soil crushing, stubble burying, mud stirring, and leveling in a single pass. Combined with an adaptive control system based on Global Navigation Satellite System—Real-Time Kinematic (GNSS-RTK) technology, it enables adaptive and precise paddy field leveling operations. To verify the operational performance of the equipment, field tests were conducted. The results showed that the machine achieved an average puddling depth of 14.21 cm, a surface levelness of 2.16 cm, an average stubble burial depth of 8.15 cm, and a vegetation coverage rate of 89.33%, demonstrating satisfactory leveling performance. Furthermore, to clarify the feasibility and superiority of applying this equipment in actual rice production, experiments were conducted to investigate the effects of different field leveling methods on early rice growth, yield, and its components. One-way analysis of variance was employed to examine the differences in agronomic indicators between the different field leveling treatments. The results indicated that using this equipment for paddy field leveling, compared to traditional methods and dry land preparation, can improve the seedling emergence rate, thereby laying a solid population foundation for the formation of effective panicles. It also promoted root growth and development and increased the total dry matter accumulation at maturity, thereby contributing to high yield formation. Over the two-year experimental period, the rice yield remained above 9.8 t·hm⁻². This research provides theoretical support and practical guidance for the further optimization and development of subsequent paddy field preparation equipment, thereby promoting the widespread application of this technology in rice production. Full article

This study examines the possibilities of using hybrid data collection methods based on photogrammetric and LiDAR imaging for documenting traffic accident sites. The evaluation was performed with an iPhone 15 Pro and a viDoc GNSS receiver. Comparative measurements were made against instruments with higher accuracy. The test scenarios included measuring errors along a 25 m line and scanning a larger traffic area. Measurements were conducted under limiting conditions on a homogeneous surface without terrain irregularities or objects. The results show that although hybrid scanning cannot fully replace traditional surveying instruments, it provides accurate results for documenting traffic accident sites. The analysis additionally revealed an almost linear spread of errors on homogeneous asphalt surfaces. Moreover, it was confirmed that the use of a GNSS receiver and control points has a significant impact on the quality of the data. Such a comprehensive assessment of surface homogeneity has not been tested yet. To achieve accuracy, it is recommended to use a scanning mode based on at least 90% image overlap with RTK GNSS. The relative error rate on a linear section ranged from 0.5 to 1.0%, which corresponds to an error of up to 5 cm over a 5 m section. When evaluating a larger area using hybrid data collection, 93.38% of the points had an error below 10 cm, with a mean deviation of 6.2 cm. These findings expand current knowledge and define practical device settings and operational limits for the use of hybrid mobile scanning. Full article

Throughout the history of civil aviation, radio navigation aids have played a crucial role in ensuring the safety and continuity of air transportation. Although the development of Global Navigation Satellite Systems (GNSS) over the past half-century has significantly improved positioning accuracy, the system’s vulnerability to interference considerably reduces its reliability and poses a risk to civil aviation safety. This limitation highlights the crucial role of ground-based radio navigation networks in ensuring nominal flight operations. This study presents a comprehensive analysis of the global coverage and performance of radio navigation aid networks and assesses the implementation level of Performance-Based Navigation (PBN) by Air Navigation Service Providers (ANSPs) worldwide. A novel methodology is proposed for network performance evaluation, incorporating spatial characteristics of parameter distribution across global airspace using a geospatial indexing framework to determine airspace configurations compliant with various area navigation (RNAV) specifications. The performance of DME/DME, VOR/DME, and VOR/VOR positioning methods is evaluated within the official ICAO regional airspace structure. The results indicate that the European and North American regions currently maintain the most developed DME and VOR networks and propose reliable infrastructure sustainability. Globally, RNAV 1 capability is supported within approximately 20.2% of airspace using DME/DME and 3.45% using VOR/DME, while RNAV 5 coverage extends over 23.61% of global airspace, which approves resource efficiency distribution. RNAV 10 coverage could be supported by the VOR/VOR positioning method only in 13.48% of global airspace. Overall, the obtained results confirm the limited positioning performance of VOR network compared with DME, supporting the continuation of VOR network rationalization strategies and highlighting the need for optimized resource sharing to ensure the resilience and safety of the global air navigation system. Full article

The Aeolian Archipelago, located in the southern margin of the Tyrrhenian Sea, is a key area to investigate the interplay between regional active fault systems and volcanic activity, making it a focal point for geodynamic studies. In particular, Salina Island lies at the intersection of two major tectonic structures: the Sisifo–Alicudi fault system in the western sector and the Aeolian–Tindari–Letojanni fault system in the central sector both exert a significant influence on the region’s deformation patterns. Detecting these signals requires high-quality GNSS data, yet the performance of newly installed stations in tectonic environments must be rigorously assessed. Between June 2023 and February 2024, a new continuous local GNSS network, which consists of five stations, Salin@Net, was established, on Salina Island. The central scientific objective of this study is to verify whether the new GNSS network achieves the data quality necessary for reliable geodetic monitoring and to evaluate its potential to resolve strain gradients in the area. We performed an extensive performance analysis of Salin@net GNSS stations, analyzing data quality, encompassing assessments of multipath effect, signal-to-noise ratio, observation continuity, and cycle slip occurrences, alongside GNSS position time series. These metrics were compared against the ISAL-RING station and benchmarked International GNSS Service (IGS) standards. Results show that the newly installed stations consistently meet the required standards, delivering robust and reliable measurements that are comparable to those of the RING GNSS continuous network. Positioning time series, processed in the ITRF14, indicate that the precision of the derived velocity estimates is comparable to that of standard continuous stations, although longer time spans are required to better constrain linear velocity estimates. Finally, spherical wavelet analysis demonstrates that the geometry of Salin@net significantly improves the spatial resolution of the strain field across the Aeolian–Tindari–Letojanni fault system and enhances resolution along the Sisifo–Alicudi fault, underscoring the role of dense, small-aperture GNSS networks in tectonic environment. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides all-weather, high-resolution ocean wind speed monitoring that offers additional benefits for forecasting tropical cyclones and severe weather events. However, existing GNSS-R wind retrieval models often lack interpretability and suffer accuracy degradation during high wind conditions. To address these limitations, we leverage a mathematical equivalence between Transformers and graph neural networks (GNNs) on complete graphs, which provides a physically grounded interpretation of self-attention as spatiotemporal influence propagation in GNSS-R data. In our model, each GNSS-R footprint is treated as a graph node whose multi-head self-attention weights quantify localized interactions across space and time. This aligns physical influence propagation with the computational efficiency of GPU-accelerated Transformers. Multi-head attention disentangles processes at multiple scales—capturing local (25–100 km), mesoscale (100 km–500 km), and synoptic (>500 km) circulation patterns. When applied to Level 1 Version 3.2 data (2023–2024) from four Asian sea regions, our Transformer–GNN achieves an overall wind speed RMSE reduction of 32% (to 1.35 m s⁻¹ from 1.98 m s⁻¹) and substantial gains in high-wind regimes (winds >25 m s⁻¹: 3.2 m s⁻¹ RMSE). The model is trained on ERA5 reanalysis 10 m equivalent-neutral wind fields, which serve as the primary reference dataset, with independent validation performed against Stepped Frequency Microwave Radiometer (SFMR) aircraft observations during tropical cyclone events and moored buoy measurements where spatiotemporally coincident data are available. Interpretability analysis with SHAP reveals condition-dependent feature attributions and suggests coupling mechanisms between ocean surface currents and wind fields. These results demonstrate that our model advances both predictive accuracy and interpretability in GNSS-R wind retrieval. With operationally viable inference performance, our framework offers a promising approach toward interpretable, physics-aware Earth system AI applications. Full article

Nowadays, information obtained through Global Navigation Satellite Systems (GNSSs) is widely employed in modern geodesy. The Precise Point Positioning (PPP) approach, which leverages signals from multiple GNSS constellations (e.g., GPS, GLONASS, Galileo, and BeiDou), enables high-precision positioning—crucial for seismic monitoring and early tsunami warning systems (EEWs). Recent advances, such as increased satellite availability and additional frequency bands, have significantly improved PPP performance, particularly in terms of positioning accuracy and convergence time. This study focuses on the Rete Integrata Nazionale GNSS (RING) network, managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), which comprises dual-frequency GNSS receivers distributed across the Italian peninsula and parts of the Mediterranean Basin. We evaluate the performance of the RING data (GPS and GNSS) acquired in a period of three weeks between 19 January 2024 and 9 February 2024 and analyzed in real time by using different PPP strategies: standard PPP and PPP with Regional Augmentation (PPP-RA). The preliminary results show that the PPP-RA approach enhances positioning accuracy and reduces convergence time, especially when comparing GPS-only datasets with those incorporating full multi-GNSS configurations. For the daily solution, in the optimal setup (i.e., full GNSS with RA), real-time solutions exhibit average accuracies of 2.05, 1.73, and 4.35 cm for the North, East, and vertical components, respectively. Sub-daily accuracies’ analysis, using 300 s sliding windows, showed even better uncertainties, exhibiting median values of 0.41, 0.32, and 0.9 cm for the North, East and vertical components, respectively. Based on the outcomes for network-wide sub-daily accuracies, 84% of the stations demonstrate average errors within 2 cm for North and East components and 3 cm for the vertical one. The analysis on the convergence time after data gaps occurred during the investigation period shows that 87% of the RING stations experienced convergence times lower than five minutes in the GNSS PPP-RA solution. These findings underscore the potential of RT-GNSS RING data for enhancing seismic monitoring and early warning systems, particularly in tectonically active regions. Full article

The Global Navigation Satellite System (GNSS) positioning performance may be degraded due to the effects of various natural and adversarial causes, most notably those related to space weather, geomagnetic, and ionospheric conditions and disturbances. Here we present a contribution to understanding the nature of geomagnetic and ionospheric conditions in terms of the effects on the GPS positioning performance through the comparative time-series analysis of the long-term annual (Year 2014) non-linear properties of Disturbance storm-time (Dst) index, an indicator of geomagnetic conditions, and the single-frequency commercial-grade GPS horizontal positioning errors as derived from raw single-frequency commercial-grade GPS observations taken at the International GNSS Service (IGS) reference station at Darwin, Northern Territory (NT), Australia. The analysis reveals candidate non-linear property indicators for future assessments and modelling, as potential descriptors of the long-term non-linear association between geomagnetic/ionospheric disturbances and GNSS positioning performance degradation: recurrence rate ($RR$), total number of lines in the recurrent plot, Shannon entropy, and trapping time ($TT$). The inference presented may serve as a framework for introducing advanced GNSS PNT correction procedures to mitigate environmental ionospheric effects on GNSS positioning performance, thereby offering more resilient and robust PNT services for GNSS applications in urban mobility, systems, and services. Full article

Cooperative navigation is a fundamental enabling technology for unlocking the full potential of Unmanned Underwater Vehicle (UUV) clusters in GNSS-denied environments. However, the severe constraints of the underwater acoustic channel, such as high latency, low bandwidth, and non-Gaussian noise, pose significant challenges to designing robust and efficient state estimation and information fusion algorithms. While numerous surveys have cataloged the available techniques, they have remained largely descriptive, lacking a rigorous, quantitative comparison of their performance trade-offs under realistic conditions. This paper provides a comprehensive and critical review that moves beyond qualitative descriptions to establish a novel quantitative comparison framework. Through a standardized benchmark scenario, we provide the first data-driven, comparative analysis of key frontier algorithms—from recursive filters like the Maximum Correntropy Kalman Filter (MCC-KF) to batch optimization methods like Factor Graph Optimization (FGO)—evaluating them across critical metrics including accuracy, computational complexity, communication load, and robustness. Our results empirically reveal the fundamental performance gaps and trade-offs, offering actionable insights for system design. Furthermore, this paper provides in-depth technical analyses of advanced topics, including distributed fusion architectures, intelligent strategies like Deep Reinforcement Learning (DRL), and the unique challenges of navigating in extreme environments such as the polar regions. Finally, leveraging the insights derived from our quantitative analysis, we propose a structured, data-driven research roadmap to systematically guide future investigations in this critical domain. Full article