Search Results (189)

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article

To address the issue of agricultural robot loss of control caused by GNSS signal degradation or loss in complex agricultural environments such as farmland and orchards, this study proposes a neural network-based SLAM/GNSS fusion localization algorithm aiming to enhance the robot’s localization accuracy and stability in weak or GNSS-denied environments. It achieves multi-sensor observed pose coordinate system unification through coordinate system alignment preprocessing, optimizes SLAM poses via outlier filtering and drift correction, and dynamically adjusts the weights of poses from distinct coordinate systems via a neural network according to the GDOP. Experimental results on the robotic platform demonstrate that, compared to the SLAM algorithm without pose optimization, the proposed SLAM/GNSS fusion localization algorithm reduced the whole process average position deviation by 37%. Compared to the fixed-weight fusion localization algorithm, the proposed SLAM/GNSS fusion localization algorithm achieved a 74% reduction in average position deviation during transitional segments with GNSS signal degradation or recovery. These results validate the superior positioning accuracy and stability of the proposed SLAM/GNSS fusion localization algorithm in weak or GNSS-denied environments. Orchard experimental results demonstrate that, at an average speed of 0.55 m/s, the proposed SLAM/GNSS fusion localization algorithm achieves an overall average position deviation of 0.12 m, with average position deviation of 0.06 m in high GNSS signal quality zones, 0.11 m in transitional sections under signal degradation or recovery, and 0.14 m in fully GNSS-denied environments. These results validate that the proposed SLAM/GNSS fusion localization algorithm maintains high localization accuracy and stability even under conditions of low and highly fluctuating GNSS signal quality, meeting the operational requirements of most agricultural robots. Full article

Smartphone GNSS technology has advanced significantly, but its performance varies considerably among Android devices due to differences in hardware and software. This study compares the GNSS capabilities of the Google Pixel 5 and Pixel 9 Pro XL (Google LLC, Mountain View, CA, USA) using five-hour static measurements under three environmental conditions: open area, canopy, and indoor. Complete raw GNSS data and the tools used for positioning are freely available. The analysis focuses on signal quality and positioning accuracy, derived using raw GNSS measurements. Results show that the Pixel 9 Pro XL provides better signal completeness, a higher carrier-to-noise density (C/N0), and improved L5 frequency reception. However, this enhanced signal quality does not always translate to superior positioning accuracy. In single-point positioning (SPP), the Pixel 5 outperformed the Pixel 9 Pro XL in open conditions when considering mean positional errors, while the Pixel 9 Pro XL performed better under canopy conditions. The precise point positioning results are modest compared to the current state of the art, only achieving accuracies of a few meters. The static method achieved sub-decimeter accuracy for both devices in optimal conditions, with Pixel 9 Pro XL demonstrating a higher fix rate. Findings highlight ongoing challenges in smartphone GNSS, particularly related to the limited quality of signals received by smartphone GNSS receivers. While newer devices show improved signal reception, precise positioning remains limited. Future research should explore software enhancements and the use of various external correction sources to optimize GNSS accuracy for mobile users. Generally, a shift from research to user-ready applications is needed. Full article

In Global Navigation Satellite System (GNSS)-denied environments, opportunistic positioning using non-cooperative Low Earth Orbit (LEO) satellite signals has shown strong potential. However, dynamic platforms face challenges in maintaining sufficient satellite counts and favorable geometric distributions due to limited signal quality and short observation windows. To address this, we propose a fast satellite selection algorithm based on the Non-Dominated Sorting Whale Optimization Algorithm (NSWOA) for dynamic, multi-constellation LEO opportunistic navigation. By introducing Pareto non-dominated solutions, the algorithm balances Doppler Geometric Dilution of Precision (DGDOP), signal strength, residual visibility time, and receiver sensitivity. Through iterative optimization, it constructs a subset of satellites with minimal DGDOP while reducing computational burden, enabling real-time fusion and switching at the receiver end. We validate the algorithm through UAV-based experiments in dynamic scenarios. Compared to GWO, PSO, and NSGA-II, the proposed method achieves computation time reductions of 27.06%, 27.05%, and 68.57%, respectively. It also reduces the overall navigation solution time to 54.96% of that required when using all visible satellites, significantly enhancing real-time responsiveness and system robustness. These results demonstrate that the NSWOA-based satellite selection algorithm outperforms existing intelligent methods in both computational efficiency and optimization accuracy, making it well-suited for real-time, multi-constellation LEO dynamic opportunistic navigation. Full article

In complex urban electromagnetic environments, wireless positioning signals are subject to various types of interference, including narrowband, chirp, and pulse jamming. Traditional generic suppression methods struggle to achieve global optimization tailored to specific interference mechanisms. This paper proposes a classification-driven differentiated jamming suppression (CDDJ) method, which adaptively selects the optimal mitigation strategy by pre-identifying interference types and integrating classification confidence levels. First, the theoretical bounds of the output carrier-to-noise ratio ($C/N_{0_{out}}$) under typical interference scenarios are derived, characterizing the performance distribution of anti-jamming efficiency ($\mathsf{\Gamma }$). Then, a mapping relationship between interference categories and their corresponding suppression strategies is established, along with decision criteria for strategy switching based on signal quality evaluation metrics. Finally, an OpenMax-Lite rejection layer is designed to handle low-confidence inputs, identify unknown jamming using the Weibull distribution, and implement a broadband conservative suppression policy. Simulation results demonstrate that the proposed method exhibits significant advantages across different interference types. Under high JSR conditions, the signal recovery rate improves by over 10% and 8% compared to that of the WPT and KLT methods, respectively. In terms of SINR performance, the proposed approach outperforms the AFF, TDPB, and FDPB methods by 1.5 dB, 1.1 dB, and 5.3 dB, respectively, thereby enhancing the reliability of wireless positioning in complex environments. Full article

Requirements for Unmanned Aerial Vehicle (UAV) applications in low-altitude operations are escalating, which demands resilient Position, Navigation and Timing (PNT) solutions incorporating global navigation satellite system (GNSS) services. However, UAVs often operate in stringent environments with degraded GNSS performance. Practical challenges often arise from dense, dynamic, complex, and uncertain obstacles. When flying in complex environments, it is important to consider signal degradation caused by reflections (multipath) and obscuration (Non-Line of Sight (NLOS)), which can lead to positioning errors that must be minimized to ensure mission reliability. Recent works integrate GNSS reliability maps derived from pseudorange error estimations into path planning to reduce loss-of-GNSS risks with PNT degradations. To accommodate multiple constraint conditions attempting to improve flight resilience against GNSS-degraded environments, this paper proposes a reinforcement learning (RL) approach to feature GNSS signal quality awareness during path planning. The non-linear relations between GNSS signal quality in the form of dilution of precision (DoP), geographic locations, and the policy of searching sub-minima points are learned by the clipped Proximal Policy Optimization (PPO) method. Other constraints considered include static obstacle occurrence, altitude boundary, forbidden flying regions, and operational volumes. The reward and punishment functions and the training method are designed to maximize the success criteria of approaching destinations. The proposed RL approach is demonstrated using a real 3D map of Indianapolis, USA, in the Godot engine, incorporating forecasted DoP data generated by a Geospatial Augmentation system named GNSS Foresight from Spirent. Results indicate a 36% enhancement in mission success rates when GNSS performance is included in the path planning training. Additionally, the varying tensor size, representing the UAV’s DoP perception range, exhibits a positive proportion relation to a higher mission rate, despite an increment in computational complexity. Full article

Horizontal dilution of precision (HDOP) is a widely used quality indicator of Global Navigation Satellite System (GNSS) positioning, considering only satellite geometry. In this study, HDOP was simulated using GNSS almanacs and high-resolution digital surface models (DSMs) along three European road sections: Oslo— Svinesund Bridge (Norway); Hamburg city center (Germany); and Rotterdam—Dutch–German border (Netherlands). This study was accomplished as part of the MODI project, which is a cross-border initiative to accelerate Cooperative, Connected, and Automated Mobility (CCAM). Our analysis revealed excellent or good overall GNSS performance in the study areas, particularly on highway sections with 99–100% of study points having a median HDOP that is categorized as excellent (HDOP < 2) or good (HDOP < 5). However, the road section in Hamburg’s city center presents challenges. When GPS is used alone, 8% of the study points experience weak or poor HDOP, and there are study points where the system is available (HDOP < 5) less than 50% of the time. Combining GNSS constellations significantly improved system availability, reaching 95% for 99% of the study points in Hamburg. To validate our simulations, we compared results with GNSS observations from a survey vehicle in Hamburg. Initial low correlation was attributed to the reception of signals from non-line-of-sight satellites. By excluding satellites with low signal-to-noise ratios, the correlation increased significantly, and reasonable agreement was obtained. We also examined the impact of using a 10 m DSM instead of a 1 m DSM in Hamburg. While the coarser spatial resolution offers computational benefits, it may miss critical details for accurate assessment of satellite visibility. Full article

The hybridization of cellular networks and GNSS systems has gained increasing attention, especially in urban canyons and indoor environments where GNSS performance degrades significantly. Hybrid localization is part of the 3rd Generation Partnership Project (3GPP) standard, offering an effective solution when satellite visibility is limited. Additional cellular measurements can enhance the accuracy and reliability of standalone UE. Hybrid methods offer multiple benefits: an improved availability, continuity, and integrity; better signal penetration due to proximity; a lower power consumption; and, in harsh environments, potentially more accurate positioning than a GNSS. Moreover, GNSS chipsets in mobile phones or smartwatches are typically power-intensive. This work presents a user-level hybridization method that enables UE to receive both GNSS and 4G/5G data and autonomously determine whether to apply hybrid positioning. The developed algorithms improve the precision and reliability, allowing user-driven decisions based on data quality. The system was tested under static conditions across various scenarios: outdoors, in urban canyons, and indoors. The results show that, while hybridization enhances positioning, the 4G-only solution often performs in terms of vertical accuracy. Standard deviation metrics help guide the selection of the most precise option in real time. Full article

The last decade has seen a surge in the development and deployment of low Earth orbit (LEO) constellations primarily serving broadband communication applications. These developments have also influenced the interest providing positioning, navigation, and timing (PNT) services from LEO. Potential services include new ranging signals from LEO, augmentation of global navigation satellite systems (GNSS), and monitoring of GNSS. The latter promises an advantage over existing ground-based monitoring due to the reception of observables with reduced atmospheric error contributions and the potential for lower costs. In this paper, we investigate the influence of LEO constellation design on the line-of-sight visibility conditions for GNSS monitoring. We simulate a series of Walker constellations in LEO with a varying number of total satellites, orbital planes, and orbital heights. From the simulated data, we gather statistics on the number of visible GNSS and LEO satellites, durations of visibility periods, and the quality of this visibility quantified by the dilution of precision (DOP) metric. Our findings indicate that increasing the total number of LEO satellites results in diminishing returns. We find that constellations with relatively few total satellites equally yield an adequate monitoring capability. We also identify orbital geometric constraints resulting in suboptimal performance and discuss optimization strategies. Full article

The aim of this study was to develop basic findings for a continuous Signal Quality Monitoring system based on a measurement campaign. Four Galileo satellites were repeatedly recorded, using a dish antenna, and their metrics were analyzed. Due to the stable course, thresholds for the detection of threat models can be determined. These values were tested against simulated signals and the sensitivity of the detection was found to be satisfactory. Based on the convergence behavior of the data, a measurement duration of 180–200 s can be recommended. Finally, the influence of the GPU and memory clock on the performance of predefined conditions close to the receiver was tested. The core clock of the GPU was identified as the bottleneck of the processing. Full article

This study utilizes a low-cost Global Navigation Satellite System (GNSS) buoy platform, combined with multi-system GNSS data, to investigate the impact of GNSS signal quality and multipath effects on the accuracy of atmospheric precipitable water vapor (PWV) retrievals. It also explores the methods for oceanic rainfall event forecasting and precipitation prediction based on GNSS-PWV. By analyzing the data quality from various GNSS systems and using the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset as a reference, the study assesses the accuracy of PWV retrievals in dynamic marine environments. The results show that the GNSS-derived PWV from the buoy platform is highly consistent with ERA5 data in both trend and characteristics, with an RMSE of 3.8 mm for the difference between GNSS-derived PWV and ERA5 PWV. To enhance rainfall forecasting accuracy, a balanced threshold selection (BTS) method is proposed, significantly improving the balance between the probability of detection (POD) and false alarm rate (FAR). Furthermore, a Random Forest model based on multiple meteorological parameters optimizes precipitation forecasting, especially in reducing false alarms. Additionally, a particle swarm optimization (PSO)-based BP Neural Network model for rainfall prediction achieves high precision, with an R² of 97.8%, an average absolute error of 0.08 mm, and an RMSE of 0.1 mm. The findings demonstrate the potential of low-cost GNSS buoy for monitoring atmospheric water vapor and short-term rainfall forecasting in dynamic marine environments. Full article

Global Navigation Satellite Systems (GNSSs) are the primary source of information for Positioning, Navigation, and Timing (PNT) in the maritime sector; however, they are vulnerable to unintentional or deliberate interference, such as jamming, spoofing, or meaconing. The continuous monitoring of GNSS signals is crucial for vessels and mobile maritime platforms to ensure the integrity, availability, and accuracy of positioning and navigation services. This monitoring is essential for guaranteeing the safety and security of navigation and contributes to the accurate positioning of vessels and platforms involved in hydrographic and oceanographic research. This paper presents the implementation of a complex system for monitoring the quality of signals within the GNSS spectrum at the Maritime Hydrographic Directorate (MHD). The system provides real-time analysis of signal parameters from various GNSSs, enabling alerts in critical situations and generating statistics and reports. It comprises four permanent stations equipped with state-of-the-art GNSS receivers, which integrate a spectrum analyzer and store raw data for post-processing. The system also includes software for monitoring the GNSS spectrum, detecting interference events, and visualizing signal quality data. Implemented using a Docker-based platform to enable efficient management and distribution, the software architecture consists of a reverse proxy, message broker, front-end, authorization service, GNSS orchestrator, and GNSS monitoring module. This system enhances the quality of command, control, communications, and intelligence decisions for planning and execution. It has demonstrated a high success rate in detecting and localizing jamming and spoofing events, thereby improving maritime situational awareness and navigational safety. Future development could involve installing dedicated stations to locate interference sources. Full article

Frequent droughts significantly threaten economic development, necessitating effective long-term drought monitoring. The Gravity Recovery and Climate Experiment (GRACE) satellite and its follow-on mission along with Global Navigation Satellite System (GNSS) inversion technologies provide long-term terrestrial water storage signals. However, their limitations in temporal resolution and spatial continuity are inadequate for current requirements. To solve this problem, this study combines a daily terrestrial water storage anomaly (TWSA) reconstruction method with the GNSS inversion technique to explore daily, spatially continuous TWSA in China’s Yellow River Basin (YRB). Furthermore, the Daily Drought Severity Index (DDSI) is employed to analyze drought dynamics in the YRB. Finally, by reconstructing the climate-driven water storage anomalies model, this study explores the influence of climate and human factors on drought. The results indicate the following: (1) The reconstructed daily TWSA product demonstrates superior quality compared to other available products and exhibits a discernible correlation with GNSS-derived daily TWSA data, while REC_TWSA is closer to the GRACE-based TWSA dataset. (2) The DDSI demonstrates superior drought monitoring capabilities compared to conventional drought indices. During the observation period from 2004 to 2021, the DDSI detected the most severe drought event occurring between 30 October 2010 and 10 September 2011. (3) Human activities become the primary driver of drought in the YRB. The high correlation of 0.81 between human-driven water storage anomalies and groundwater storage anomalies suggests that the depletion of TWSA is due to excessive groundwater extraction by humans. This study aims to provide novel evidence and methodologies for understanding drought dynamics and quantifying human factors in the YRB. Full article

The increasing demand for clean and reliable water resources, coupled with the growing threat of water pollution, has made real-time water quality (WQ) monitoring and assessment a critical priority in many urban areas. Urban environments encounter substantial challenges in maintaining WQ, driven by factors such as rapid population growth, industrial expansion, and the impacts of climate change. Effective real-time WQ monitoring is essential for safeguarding public health, promoting environmental sustainability, and ensuring adherence to regulatory standards. The rapid advancement of Internet of Things (IoT) sensor technologies and smartphone applications presents an opportunity to develop integrated platforms for real-time WQ assessment. Advances in the IoT provide a transformative solution for WQ monitoring, revolutionizing the way we assess and manage our water resources. Moreover, recent developments in Location-Based Services (LBSs) and Global Navigation Satellite Systems (GNSSs) have significantly enhanced the accessibility and accuracy of location information. With the proliferation of GNSS services, such as GPS, GLONASS, Galileo, and BeiDou, users now have access to a diverse range of location data that are more precise and reliable than ever before. These advancements have made it easier to integrate location information into various applications, from urban planning and disaster management to environmental monitoring and transportation. The availability of multi-GNSS support allows for improved satellite coverage and reduces the potential for signal loss in urban environments or densely built environments. To harness this potential and to enable the seamless integration of the IoT and LBSs for sustainable WQ monitoring, a systematic literature review was conducted to determine past trends and future opportunities. This research aimed to review the limitations of traditional monitoring systems while fostering an understanding of the positioning capabilities of LBSs in environmental monitoring for sustainable urban development. The review highlights both the advancements and challenges in using the IoT and LBSs for real-time WQ monitoring, offering critical insights into the current state of the technology and its potential for future development. There is a pressing need for an integrated, real-time WQ monitoring system that is cost-effective and accessible. Such a system should leverage IoT sensor networks and LBSs to provide continuous monitoring, immediate feedback, and spatially dynamic insights, empowering stakeholders to address WQ issues collaboratively and efficiently. Full article

The time to first fix (TTFF) measures the time elapsed by a global navigation satellite system (GNSS) receiver from switch-on to provision of a navigation solution. This parameter is crucial for applications where a position, within an acceptable error, is needed as soon as possible after turning the device on. The quality of the TTFF depends mainly on the receiver, the environment, and the GNSS satellites employed. Although all four available GNSSs (BeiDou, Galileo, GLONASS, and GPS) are complementary, their constellations and signals differ, providing different TTFF performances. This becomes even more relevant in hostile environments, where the TTFF degrades from nominal results. In this work, the robustness of the signals of the four GNSSs against different levels of harshness and its influence on the TTFF performance are evaluated in a comparative way. For this purpose, a typical scenario for mass-market GNSS applications, involving cold-start conditions, single-frequency signals, and a low-cost receiver, is considered. The results indicate that GPS provides the most robust TTFF, followed by GLONASS (although at the expense of positioning accuracy), BeiDou, and Galileo, in that order. Full article