Search Results (1,654)

To address the degraded accuracy and poor robustness of Strapdown Inertial Navigation Systems (SINSs)/Global Positioning Systems (GPSs) integrated navigation systems under time-varying non-Gaussian measurement noises, this paper proposes a variational Bayesian generalized maximum correntropy cubature Kalman filter (VBGMCCKF). The proposed method combines variational Bayesian adaptive method with the generalized maximum correntropy criterion, enabling the filter to handle the noises with time-varying statistical characteristics and effectively improving its applicability to different types of non-Gaussian noises. The results under different scenarios demonstrate that VBGMCCKF achieves superior estimation accuracy and robustness in the SINS/GPS integrated navigation systems compared with other existing methods. These results confirm the effectiveness of the proposed method for integrated navigation systems under complex noise environments. Full article

Location-based games (LBGs) have increasingly been adopted in education for experiential, situated, and collaborative learning. While used in subjects such as geography and history, their application in mathematics remains underexplored, partly because mathematical concepts are abstract and hard to embed in spatial game environments. This study examines the feasibility and educational potential of LBGs in lower-secondary mathematics. Using a mixed-methods approach, two location-based activities were tested with 28 students aged 12–14. In the first, students used Global Positioning System (GPS)-enabled devices to reach landmarks (e.g., a volleyball court, a church), where they took on-site measurements and applied geometric reasoning to calculate areas, perimeters, and volumes. In the second, they followed a treasure hunt, solving algebraic equations and word problems to form a secret word. Questionnaires, observations, and teacher interviews showed high engagement, participation, and collaboration, with students viewing the activities as meaningful revision. Teachers found them valuable and feasible within curricular limits, despite challenges such as preparation time, technical issues, and regulations. However, given the small sample and exploratory design, findings should be interpreted with caution: no general inferences can be drawn, and no direct learning-outcome measures were used. The study offers empirical insights into designing mathematics-oriented LBGs and future research directions. Full article

Soil salinity and alkalinity are major constraints to agricultural productivity in arid regions, particularly where treated wastewater (TWW) is used for irrigation. This study evaluated the spatial variability of water and soil physicochemical properties along Wadi Hanifa, Saudi Arabia, and compared soils from two farms irrigated with TWW for approximately 5 and 15 years to assess the effects of irrigation duration on soil properties. Soil samples were collected from 25 locations along the Wadi using a handheld Global Positioning System (GPS), and water and soil properties were analyzed using standard laboratory procedures. The treated wastewater exhibited moderate electrical conductivity (EC = 2.0 dS m⁻¹) and low sodicity hazard (SAR = 1.55), indicating its suitability for irrigation under appropriate management practices. Soils were predominantly coarse-textured and showed considerable spatial variability in salinity and chemical composition. Soil pH remained relatively stable (7.33–8.07), while EC ranged from 0.88 to 2.64 dS m⁻¹, indicating non-saline to moderately saline conditions across the study area. Comparison of soil profiles from the two farms revealed greater salinity in subsurface layers, particularly at the farm irrigated with TWW for 15 years, where EC reached 4.15 dS m⁻¹ and Na⁺ concentrations reached 16.4 meq L⁻¹. These observations suggest salt redistribution and accumulation within deeper soil horizons under prolonged irrigation. Overall, soil and water quality in Wadi Hanifa are strongly influenced by spatial variability, coarse soil texture, and arid climatic conditions. The findings highlight the importance of regular monitoring of salinity and sodicity indicators, together with adequate leaching and drainage practices, to ensure the sustainable use of treated wastewater for agricultural production in arid environments. Full article

This study explores the miniaturization of the Controlled Reception Pattern Antenna (CRPA) for Global Positioning System (GPS) receivers, addressing the challenge of mutual coupling, which adversely affects antenna performance. In this work, a miniaturized CRPA is designed and manufactured by using Rogers RO3006 substrate. To provide a performance benchmark, a four-element reference CRPA array was also designed with a 0.5 λ inter-element spacing, yielding an overall aperture size of 149.58 mm × 150.24 mm and a worst-case inter-element isolation larger than 14.4 dB. For the miniaturized CRPA, the target inter-element spacing was set to be 0.3 λ. To overcome isolation limitations, several coupling-mitigation techniques were developed and integrated into the miniaturized design. The final configuration consisted of a four-element CRPA, with each element rotated by 90° relative to its neighbor, inter-element slots incorporated into the shared ground-plane, and an individual ground plane segmentation to reduce surface–wave coupling. The proposed miniaturized CRPA achieved an overall footprint of 104.21 mm × 104.55 mm with the worst-case isolation exceeding 18.36 dB, surpassing the isolation performance of the reference array. This work demonstrates that it is possible to realize a compact CRPA with enhanced inter-element isolation by integrating tailored coupling suppression methods. Full article

Global Positioning System (GPS) spoofing poses severe threats to navigation safety, necessitating robust detection mechanisms with enhanced interpretability. While existing methods have been explored for GPS spoofing detection, algorithmic interpretability is rarely addressed and detection performance remains suboptimal. This study proposes Stack-TabNet, a stacked ensemble learning framework integrating various decision trees and the attentive transformer-based TabNet network. To address model opacity, an interpretable feature attribution mechanism is employed to quantify feature contributions and guide optimization. Experiments are conducted on a complex dataset comprising authentic and spoofed GPS signals across four classes, characterized by high-dimensional signal metrics and severe class imbalances. The initial model utilizing all available features demonstrates robust detection capability. Subsequently, an optimized variant employs a subset of top-ranked features identified by the interpretation mechanism, yielding further improved accuracy. Comparative analysis confirms that the proposed framework outperforms traditional baselines, achieving a maximum accuracy of 95.91% after SHAP-guided feature selection, which exceeds all baseline models by at least 3.54%. The analysis identifies Pseudorange and Time of Code Delay as the most important features, consistently ranking at the top across all base learners within the ensemble architecture. Full article

Timely situational awareness is essential in disaster management but normal Unmanned Aerial Vehicle (UAV) flight cannot take place when the Global Positioning System (GPS) signals are blocked or jammed. This paper addresses the issue of swarm cohesion and localization in these hostile conditions. We present a Cooperative Swarm-Mesh Network (CSMN), a hybrid structure that can alternate between an implicit Silent Mode and an explicit Leader–Follower mode based on distributed Extended Kalman Filters (DEKFs) in the face of communication failures. The system takes advantage of convex polygon decomposition to optimize the coverage in the area. The use of simulation studies with NS-3 and ROS has shown that the proposed framework can retain sub-meter localization error (RMSE < 0.9 m) in GPS-denied environments and provide 92% coverage of the area, which is 35% higher than the coverage with other baseline approaches. Within the simulated conditions evaluated using Gazebo/NS-3, sensor drift and network vulnerability are effectively addressed by the CSMN framework. These simulation-based results offer a promising blueprint for autonomous disaster evaluation, pending hardware-in-the-loop and field validation. Validation is conducted across two qualitatively distinct simulated environments: dense urban rubble and a sparse open field. Performance advantages generalise beyond a single test configuration, with mean localization RMSE remaining below 0.85 m in both scenarios. Full article

Inadequate vehicle maintenance management is one of the main causes of road accidents and elevated operating costs in light vehicles. This paper addresses this problem through the development and implementation of a low-cost integrated system for preventive maintenance management and alerts. The device, based on an open-hardware architecture (Arduino Mega 2560), integrates Global Positioning System (GPS) and mobile communication (GSM/LTE) modules to monitor distance traveled in real time and notify the user via SMS about the proximity of critical services such as oil changes, brake inspections, and timing-belt replacements. Its technical contribution lies in the integration of non-intrusive virtual ignition, filtered GPS-based odometry, configurable MicroSD-based persistence, and progressive SMS alert logic into a low-cost aftermarket system for conventional vehicles without OBD-II dependence. Experimental validation was conducted in the city of Guayaquil using a 2012 Hyundai Accent. Field tests were carried out in three scenarios: a dense urban route, a peripheral road, and interurban routes. Results showed satisfactory accuracy with a global average percentage error of 3.98% compared to the vehicle’s odometer and 100% effectiveness in sending alerts under the tested conditions (20/20 events; exact 95% binomial confidence interval: 83.2–100.0%). These results provide strong evidence of technical feasibility for the proposed architecture under the tested conditions in a representative single-vehicle proof-of-concept, while broader cross-vehicle validation remains necessary before generalizing the system to the wider diversity of aging fleets. Full article

In the field of drone navigation systems, a high-precision positioning solution can be provided by an integrated strapdown inertial navigation system (SINS)/global navigation satellite system (GNSS). But when satellite signals are interfered with or blocked by tall buildings, the errors of SINS will disperse rapidly due to the complex air and mechanical vibrations, leading to a serious degradation of navigation accuracy. To enhance the positioning performance in this situation, this paper proposes a hybrid information fusion method based on attention-augmented temporal convolutional network (TCN) for drone SINS/GNSS navigation system. A feature integration and prediction model is constructed to provide a pseudo-positioning reference for the integrated navigation filter during GNSS-denied periods, in which TCN is used to establish a predictive positioning error correction model based on inertial measurements and SINS data, while a self-attention model is incorporated to extract complex global drone motion features. The performance of the proposed method has been experimentally verified using Global Positioning System (GPS) and SINS data collected from real drone flight test. Comparison results among the proposed model, SINS with TCN, SINS with convergent Kalman filter (KF) prediction section and SINS-only indicate that the proposed method can effectively improve the drone positioning accuracy in specific GNSS-denied environments. Full article

Wheelchair users face significant mobility limitations related to both medical issues (e.g., musculoskeletal strain, pressure ulcers) and urban accessibility challenges. This pilot study introduces a sensor system integrating an inertial measurement unit (IMU), GPS (Global Positioning System), and a pressure-measuring seat to monitor distance travelled, speed, and posture in relation to real-world conditions. Seven participants navigated an approximately 800-metre outdoor course, divided into 13 sections, while real-time data were recorded. The results showed an average speed of 1.24 ± 0.41 m/s with peak speeds of up to 2.67 m/s. The centre of pressure on the seat fluctuated by an average of 25 mm in the x and y directions (left-right: COPx, back-forward: COPy). The data for average speed, COPx, and COPy showed significant differences between most of the 13 sections, with large, very large, and huge effect sizes. Comparing the speed, COPx, and COPy data with respect to distance travelled, and correlating them between the seven participants by applying the rank-sum method to the mean R² and calculating Kendall’s W, revealed that speed, COPx, and COPy were influenced by course conditions (R² medians between 0.013 and 0.499; W = 0.7857, strong agreement; χ²p = 0.0281). Small R² values indicate more individualised participant behaviour, while large R² values highlight the stronger influence of course conditions on the parameters. This non-invasive and cost-effective system provides objective motion data that can be used for future research in wheelchair design and rehabilitation strategies. Despite its advantages, this study was limited to able-bodied participants, so further clinical trials with individuals with mobility impairments are needed. Full article

A model predictive control (MPC) strategy is proposed based on state observation and updating for image-based visual servoing (IBVS) tasks of micro aerial vehicles (MAVs). This control strategy enables precise pose adjustment of MAVs without relying on the global positioning system (GPS). Specifically, image features are first defined on a virtual image plane to decouple the translational motion of the MAV. Subsequently, a linear velocity observer is developed to provide high-quality real-time velocity information for the MAV during IBVS execution. Furthermore, the image dynamics on the virtual image plane are linearized using a first-order Taylor expansion, and a linear MPC controller is formulated to efficiently compute the optimal control inputs. Moreover, the state inputs to the MPC controller are updated at each control cycle to eliminate errors accumulated during the rolling optimization based on the linearized dynamics, thereby ensuring the precision of IBVS. Simulation and experimental results demonstrate the performance of the proposed observer and control strategy. Full article

Many place recognition approaches, which identify previously visited places or locations by matching current sensory data, such as 2D RGB images and 3D point clouds, have been proposed to achieve accurate and robust localization and loop closure detection in global positioning system (GPS)-denied environments. Since visual place recognition (VPR) methods that rely on images captured by camera sensors are highly sensitive to variations in appearance, including changes in lighting, surface color, and shadows, they can lead to poor place recognition accuracy. In contrast, light detection and ranging (LiDAR)-based place recognition (LPR) approaches based on 3D point cloud data that captures the shape and geometric structure of the environment are robust to changes in place appearance and can therefore provide more reliable place recognition results than VPR methods. This work presents an indoor LPR method called PointNetVLAD-based indoor pedestrian localization (PIPL). PIPL is a deep network model that uses PointNetVLAD to learn to extract global descriptors from 3D LiDAR point cloud data. PIPL can recognize places previously visited by a pedestrian using point clouds captured by a low-cost LiDAR sensor on a smartphone in small-scale indoor environments, while PointNetVLAD performs place recognition for vehicles using high-cost LiDAR, GPS, and inertial measurement unit (IMU) sensors in large-scale outdoor areas. For place recognition on 3D point cloud reference maps generated from LiDAR scans, PointNetVLAD exploits the universal transverse mercator (UTM) coordinate system based on GPS and IMU measurements, whereas PIPL uses a virtual coordinate system designed in this study due to the unavailability of GPS indoors. In experiments conducted in campus buildings, PIPL shows significant advantages over NetVLAD (known as a convolutional neural network (CNN)-based VPR method). Particularly in indoor environments with repetitive scenes where geometric structures are preserved and image-based appearance features are sparse or unclear, PIPL achieved $39\%$ higher top-1 accuracy and $10\%$ higher top-3 accuracy compared to NetVLAD. Furthermore, PIPL achieved place recognition accuracy comparable to NetVLAD even with a small number of points in a 3D point cloud and outperformed NetVLAD even with a smaller model training dataset. The experimental results also indicate that PIPL requires over $76\%$ less place retrieval time than NetVLAD while maintaining robust place classification performance. Full article

Global Navigation Satellite Systems (GNSSs) provide essential position, velocity, and time (PVT) information worldwide. Accurate evaluation of GNSS signals and receiver performance requires realistic simulation environments, particularly for the carrier-to-noise-density ratio (C/N₀), a critical indicator reflecting signal quality dependent on satellite elevation angles. This paper presents the development of a GNSS testbed specifically designed to simulate and estimate C/N₀ values, focusing on GPS L1 C/A signals. The proposed testbed comprises three main components, a satellite simulator that controls signal power accurately according to satellite elevation angles, an up-/down-converter for RF/IF band conversion, and a signal receiver that estimates C/N₀ using the Narrowband–Wideband Power Ratio (NWPR) method. The performance of the proposed testbed was evaluated under four scenarios, namely static, dynamic, jamming, and real-signal. In the static scenario, the proposed system achieved a maximum C/N₀ estimation RMSE of 0.60 dB-Hz for satellites with elevation angles above 30° and 1.63 dB-Hz for those below 30°. In the dynamic scenario, the corresponding RMSE values were 0.68 dB-Hz and 0.86 dB-Hz, whereas under jamming conditions they increased to 2.08 dB-Hz and 2.12 dB-Hz, respectively. Furthermore, in the real-signal scenario, the C/N₀ values estimated by the proposed testbed exhibited trends consistent with those reported by a commercial u-blox receiver processing the same live-sky signals, thereby confirming its reliability under actual GNSS reception conditions. These results demonstrate that the proposed GNSS testbed enables reliable C/N₀ simulation and estimation for GNSS receiver performance evaluation. Full article

Many Internet of Things (IoT) applications that use LoRaWAN require node localization, often relying on signal strength or message timestamps to estimate distance. However, traditional techniques typically require prior knowledge of signal propagation models or clock synchronization between multiple nodes. Therefore, this paper proposes a one-way ranging method based on LoRa to estimate link distances using the received signal from a single node, with no additional infrastructure or synchronization requirements. The approach uses the inherent properties of the LoRa chirp-based waveform to extract time delay information and estimate distance. The proposed method consists of a transmitter and a receiver capable of detecting the link delay using demodulation of the preamble. Then, the method estimates the distance using the link delay without requiring additional hardware or information. The method was validated through MATLAB R2025a simulations, including five nodes distributed over an 18 km² area. The proposed method achieves distance estimation with mean errors of 25 m under semi-urban, non-line-of-sight conditions, outperforming existing methods. Additionally, the study identifies two practical system configurations for LoRa, at 8 Msps and 2 Msps, which reduce the ranging error while considering hardware feasibility. These findings are especially relevant for researchers developing Global Positioning System (GPS) free localization techniques in resource-constrained IoT environments. Full article

Background: Global positioning system (GPS) technology is widely used to quantify external training load (ETL) in youth soccer. Despite its extensive application in training and match contexts, considerable heterogeneity is present in the selection, definition, and interpretation of GPS-derived variables, limiting comparability between studies and practical implementation by coaches. Objective: This narrative review aimed to summarize and critically evaluate the current literature on GPS-based ETL monitoring in youth soccer players, with a focus on commonly used variables, methodological considerations, and practical applications in training and match contexts. Methods: A narrative literature search was conducted using PubMed, SPORTDiscus, and Scopus databases. Peer-reviewed studies published in English between the years of 2012 and 2025 were included. Data were extracted on participant characteristics, GPS technology, monitored ETL variables, and contextual settings. Results: The 34 reviewed studies primarily reported total distance (TD; m), high-speed running distance (HSR; m), sprint distance (SD; m), distance per minute (m·min⁻¹), peak speed (km·h⁻¹), and acceleration- and deceleration-based (ACC, DEC; count) ETL variables. Substantial variability was observed in speed thresholds, acceleration definitions, and data processing methods. Positional roles, training formats (e.g., small-sided games), and seasonal phase influenced ETL demands, although methodological inconsistencies limited cross-study comparisons. Conclusion: GPS technology provides valuable insights into the ETL demands of youth soccer. The lack of standardized variable definitions and thresholds remains a major limitation. Greater methodological consistency and clearer reporting standards are required to enhance the practical usefulness of GPS monitoring for coaches in youth soccer. Full article

GPS GNSS position signal manipulation in shipping can lead to significant navigational challenges. Such disruptions may result from various factors, including atmospheric conditions, satellite malfunctions, or intentional positioning satellite signal disturbance. Impacts on shipping operations include delays, increased operational costs, and safety risks for crews and vessels. Understanding these disturbances and their implications is crucial for enhancing maritime safety and efficiency. Common causes of GNSS disturbances in shipping include atmospheric effects such as ionospheric and tropospheric delays, satellite signal obstructions due to terrain or buildings, satellite malfunctions or failures, and intentional interference like jamming. These factors can lead to inaccuracies in positioning, affecting navigation and safety. GPS signals are vulnerable to various cyber threats, including spoofing, jamming, and signal interference. Spoofing involves sending counterfeit GPS signals to mislead receivers, while jamming disrupts the legitimate signals. Ensuring the integrity and security of GPSs is crucial for applications like navigation, timing, and critical infrastructure. Advanced encryption and authentication methods can help safeguard the security of GPS signals. These vulnerabilities can have profound implications for navigation systems and critical infrastructure. Enhancing GPS security requires a combination of advanced technologies and policies to improve signal integrity and authentication processes. The Global Positioning System (GPS) is the most widely used GNSS positioning method in commercial shipping. Moreover, deliberate disturbance technical birth mechanisms are similar across the field of GNSS systems. Therefore, this study focuses on the deliberate disturbance of the GPS, recognising the ability to upscale the research results to other commonly used GNSSs such as Beidou, Galileo, and Glonass. This paper introduces a behavioural approach to enhancing cybersecurity and preparedness to external threats in commercial shipping through European collaboration in the CyberSEA project. Full article