Explore the vast range of titles available.

Continuously operating Global Navigation Satellite Systems (cGNSS) can be used to convert relative values of vertical land motion (VLM) derived from Interferometric Synthetic Aperture Radar (InSAR) to absolute values in a global or regional reference frame. Artificial trihedral corner reflectors (CRs) provide high-intensity and temporally stable reflections in SAR time series imagery, more so than naturally occurring permanent scatterers. Therefore, it is logical to co-locate CRs with cGNSS as ground-based geodetic infrastructure for the integrated monitoring of VLM. We describe the practical considerations for such co-locations using four case-study examples from Perth, Australia. After basic initial considerations such as land access, sky visibility and security, temporary test deployments of co-located CRs with cGNSS should be analysed together to determine site suitability. Signal to clutter ratios from SAR imagery are used to determine potential sites for placement of the CR. A significant concern is whether the co-location of a deliberately designed reflecting object generates unwanted multipath (reflected signals) in the cGNSS data. To mitigate against this, we located CRs >30 m from the cGNSS with no inter-visibility. Daily RMS values of the zero-difference ionosphere-free carrier-phase residuals, and ellipsoidal heights from static precise point positioning GNSS processing at each co-located site were then used to ascertain that the CR did not generate unwanted cGNSS multipath. These steps form a set of recommendations for the installation of such geodetic ground-infrastructure, which may be of use to others wishing to establish integrated InSAR-cGNSS monitoring of VLM elsewhere.

In this paper, we propose an approach to determine seven parameters of the Helmert transformation by transforming the coordinates of a continuous GNSS network from the World Geodetic System 1984 (WGS84) to the International Terrestrial Reference Frame. This includes (1) converting the coordinates of common points from the global coordinate system to the local coordinate system, (2) identifying and eliminating outliers by the Dikin estimator, and (3) estimating seven parameters of the Helmert transformation by least squares (LS) estimation with the \"clean\" data (i.e. outliers removed). Herein, the local coordinate system provides a platform to separate points' horizontal and vertical components. Then, the Dikin estimator identifies and eliminates outliers in the horizontal or vertical component separately. It is significant because common points in a continuous GNSS network may contain outliers. The proposed approach is tested with the Géoazur GNSS network with the results showing that the Dikin estimator detects outliers at 6 out of 18 common points, among which three points are found with outliers in the vertical component only. Thus, instead of eliminating all coordinate components of these six common points, we only eliminate all coordinate components of three common points and only the vertical component of another three common points. Finally, the classical LS estimation is applied to \"clean\" data to estimate seven parameters of the Helmert transformation with a significant accuracy improvement. The Dikin estimator's results are compared to those of other robust estimators of Huber and Theil-Sen, which shows that the Dikin estimator performs better. Furthermore, the weighted total least-squares estimation is implemented to assess the accuracy of the LS estimation with the same data. The inter-comparison of the seven estimated parameters and their standard deviations shows a small difference at a few per million levels (E-6).

By presenting the composition of the crustal deformation observation system and the development of the observation techniques, we show the space-time characteristics of the different observation techniques, the advantage of the application of the optical fiber and long-baseline strainmeter and the integrated technique. It was found that the strain recorded by the 1500 m laser strainmeter constructed at Gifu Prefecture, Japan are nearly consistent with the calculated values based on the continuous GNSS stations of the GEONET (GPS Earth Observation Network). Some earthquakes can cause abnormal strains. The strainmeter is more sensitive to the environments. In practical work, we should develop the advanced techniques, use various observation techniques and research method and increase the monitoring density. It will improve the spatial and temporal resolution and promote the identification ability of the earthquake precursory characteristics and better serve scientific mitigating the disasters.

In various practical applications, such as autonomous vehicle and unmanned aerial vehicle navigation, Global Navigation Satellite Systems (GNSSs) are commonly used for positioning. However, traditional GNSS positioning methods are often affected by disturbances due to external observational conditions. For instance, in areas with dense buildings, tree cover, or tunnels, GNSS signals may be obstructed, resulting in positioning failures or decreased accuracy. Therefore, improving the accuracy and stability of GNSS positioning in these complex environments is a critical concern. In this paper, we propose a novel multi-sensor fusion framework based on multi-frame residual optimization for GNSS/INS/LiDAR to address the challenges posed by complex satellite environments. Our system employs a novel residual detection and optimization method for continuous-time GNSS within keyframes. Specifically, we use rough pose measurements from LiDAR to extract keyframes for the global system. Within these keyframes, the multi-frame residuals of GNSS and IMU are estimated using the Median Absolute Deviation (MAD) and subsequently employed for the degradation detection and sliding window optimization of the GNSS. Building on this, we employ a two-stage factor graph optimization strategy, significantly improving positioning accuracy, especially in environments with limited GNSS signals. To validate the effectiveness of our approach, we assess the system’s performance on the publicly available UrbanLoco dataset and conduct experiments in real-world environments. The results demonstrate that our system can achieve continuous decimeter-level positioning accuracy in these complex environments, outperforming other related frameworks.

In this study, the key challenges faced by global navigation satellite systems (GNSSs) in the field of security are addressed, and an eye diagram-based deep learning framework for intelligent classification of interference types is proposed. GNSS signals are first transformed into two-dimensional eye diagrams, enabling a novel visual representation wherein interference types are distinguished through entropy-centric feature analysis. Specifically, the quantification of information entropy within these diagrams serves as a theoretical foundation for extracting salient discriminative features, reflecting the structural complexity and uncertainty of the underlying signal distortions. We designed a hybrid architecture that integrates spatial feature extraction, gradient stability enhancement, and time dynamics modeling capabilities and combines the advantages of a convolutional neural network, residual network, and bidirectional long short-term memory network. To further improve model performance, we propose an improved coati optimization algorithm (ICOA), which combines chaotic mapping, an elite perturbation mechanism, and an adaptive weighting strategy for hyperparameter optimization. Compared with mainstream optimization methods, this algorithm improves the convergence accuracy by more than 30%. Experimental results on jamming datasets (continuous wave interference, chirp interference, pulse interference, frequency-modulated interference, amplitude-modulated interference, and spoofing interference) demonstrate that our method achieved performance in terms of accuracy, precision, recall, F1 score, and specificity, with values of 98.02%, 97.09%, 97.24%, 97.14%, and 99.65%, respectively, which represent improvements of 1.98%, 2.80%, 6.10%, 4.59%, and 0.33% over the next-best model. This study provides an efficient, entropy-aware, intelligent, and practically feasible solution for GNSS interference identification.

Multipath is a source of error that limits the Global Navigation Satellite System (GNSS) positioning precision in short baselines. The tightly combined model between systems increases the number of observations and enhances the strength of the mathematical model owing to the continuous improvement in GNSS. Multipath mitigation of the multi-GNSS tightly combined model can improve the positioning precision in complex environments. Interoperability of the multipath hemispherical map (MHM) models of different systems can enhance the performance of the MHM model due to the small multipath differences in single overlapping frequencies. The adoption of advanced sidereal filtering (ASF) to model the multipath for each satellite brings computational challenges owing to the characteristics of the multi-constellation heterogeneity of different systems; the balance efficiency and precision become the key issues affecting the performance of the MHM model owing to the sparse characteristics of the satellite distribution. Therefore, we propose a modified MHM method to mitigate the multipath for single-frequency multi-GNSS tightly combined positioning. The method divides the hemispherical map into 36 × 9 grids at 10° × 10° resolution and then searches with the elevation angle and azimuth angle as independent variables to obtain the multipath value of the nearest point. We used the k-d tree to improve the search efficiency without affecting precision. Experiments show that the proposed method improves the mean precision over ASF by 10.20%, 10.77%, and 9.29% for GPS, BDS, and Galileo satellite single-difference residuals, respectively. The precision improvements of the modified MHM in the E, N, and U directions were 32.82%, 40.65%, and 31.97%, respectively. The modified MHM exhibits greater performance and behaves more consistently.

Evaluation for Global Navigation Satellite System (GNSS) Position Dilution Of Precision (PDOP) is generally based on a simulated global grid with a specific Temporal–Spatial (T–S) resolution. However, the lack of a unified evaluation standard regarding the grid model, T–S resolution and evaluation period leads to inaccurate PDOP evaluation results and unreasonable comparisons among multi-GNSS. We propose the Equal-Arc-Length Grid (GRID_EAL) for PDOP evaluation, which can avoid the bias caused by uneven point distribution present in the commonly used Equal-Interval of Longitude and Latitude Grid (GRID_ELL) and provide more accurate results. Based on GRID_EAL, we thoroughly analyze the varying characteristics and convergence of PDOP metrics with different T–S resolutions. The results indicate that the optimal T–S resolution is 300 s and 3 degrees, reducing time and memory costs by 90% compared to the T–S resolution of 30 s and 3 degrees, while ensuring evaluation accuracy. Moreover, to ensure the representativity of PDOP evaluation for each system, a sliding window method is developed based on the Constellation Ground Track Repeat Period, which enables continuous daily comparisons among multi-GNSS. The proposed method satisfies the requirements for the unified evaluation standard set by the International Committee on Global Navigation Satellite Systems, International GNSS Monitoring and Assessment, and benefits PDOP evaluation and comparison for multi-GNSS.

In recent years, mining operators have adopted Information and Communications Technology (ICT) for monitoring and inspection operations but archaic/manual processes to collect and analyse data are still in use, thus making the protection of mining critical assets a costly and highly complex problem, reducing the capabilities of the monitoring system to trigger automatic alarms. Within the SEC4TD project, a low-cost IoT-based GNSS positioning device is developed, with the aim to provide accurate absolute positions of infrastructures. The solution includes the use of a two bands (L1, L2) GNSS receiver, long power autonomy and RTK correction on edge nodes. The paper describes the first prototype and the initial testing to evaluate its positional precision. A long term evaluation over 15 days is performed including a comparison with a high-end receiver. Lastly, field conditions are mimicked by reducing to 60 the number of epochs used in estimating the position each hour allowing for continuous monitoring of the infrastructure of interest.

In pasture-based dairy farming, animal behavior data can improve data-driven pasture management. Information on the grazing behavior of dairy cows can be retrieved from sensor-based data. However, this approach generally requires sophisticated sensor equipment and involves labor-intensive animal observations. As an alternative, the use of data from simple and commonly used collar-mounted accelerometers and global navigation system services (GNSS) receivers was investigated. In our on-farm study, cows grazed in a rotational or in a continuous grazing system, with a higher sward or a lower sward height, respectively. As an indicator of grazing activity, the overall dynamic body acceleration (ODBA) was calculated from the accelerometer data. After differentiating the grazing process (forage uptake) into grazing with grazing steps (i.e., moving to the next feeding station) and grazing without grazing steps (i.e., true standing) from the GNSS data, only a negligible effect of grazing steps on ODBA was found. The ODBA was higher in short swards (3.47 m s −2 ) than in tall swards (2.88 m s −2 ). The ODBA was also affected by the time of the day, with major grazing activity around dusk. These findings show the potential of simple accelerometers on collars in research on grazing patterns and cattle monitoring and for use in pasture management. The ODBA can be calculated from any three-dimensional accelerometer also from existing commercial technology, which allows a wide in-field application.

Global Navigation Satellite Systems (GNSSs) are vulnerable to interference due to low satellite signal transmission power, and thus the problem of tracking GNSS interference sources has attracted much attention. A new nonlinear filtering algorithm called the continuous-discrete Gaussian kernel quadrature Kalman filter (CD-GKQKF) is proposed for tracking such interference sources. Continuous-discrete filtering framework considers the process model as being in the continuous-time domain and subsequently constructs the univariate Gaussian kernel quadrature (GKQ) rule based on scaled Gaussian Hermite quadrature rule. On that basis, it is extended to the multivariate space with tensor product rule. Finally, the multivariate GKQ rule is introduced into the continuous-discrete filtering framework and the CD-GKQKF algorithm is obtained. The proposed algorithm has been applied to the Van der Pol Oscillator and the GNSS interference source tracking application, respectively. The results show that the proposed CD-GKQKF algorithm with appropriate Gaussian kernel bandwidth provides better accuracy than the traditional continuous-discrete filtering algorithms.