Search Results (164)

Autonomous tractors have been shown to possess the capability to ensure a high degree of operational precision during seeding activities on flat terrain. However, in topographically challenging environments characterised by significant elevations and pronounced variations in slope, factors such as road gradients have been shown to compromise the precision of satellite-based positioning systems. This, in turn, can lead to alterations in vehicle posture and the generation of disparate longitudinal driving forces between the left and right tyres. It is important to note that this deviation from the predefined path has the potential to result in rollover accidents. Evidence has been presented that indicates a correlation between road gradient and vehicle roll motion. The proposed methodology is an algorithmic approach to the estimation of lateral slope, integrating inertial measurement unit (IMU) sensors and ground-based ultrasonic radars. This algorithmic approach is proposed as a means to achieve more accurate estimations of lateral slope. The initial development of the vehicle dynamics model was based on slope operation requirements, and the model was endowed with eight degrees of freedom. The utilisation of an unscented Kalman filter (UKF) facilitates the integration of inertial measurement unit (IMU) and ground-based ultrasonic radar measurements, thereby enabling real-time estimation of key motion states, such as lateral slope. The validity of the proposed algorithm was established through a combination of hardware-in-the-loop testing and field trials involving real tractors. The findings indicate that the implementation of this algorithm leads to a substantial enhancement in the trajectory tracking accuracy of tractors during slope operations. This enhancement is characterised by a substantial reduction in lateral deviation and an effective augmentation in the operational pass rate. In the course of empirical trials conducted in a mountainous environment, the lateral positioning deviation during straight-line driving was diminished from 10 cm to within 5 cm. Concurrently, the precision of lateral slope estimation was enhanced to 0.04 degrees. Full article

Satellites supporting IoT connectivity may need to serve a large population of LoRa terminals, where collisions among packets using the same spreading factor (SF) can severely degrade uplink reliability. The ALOHA-based access used in LEO-IoT leads to frequent collisions under massive terminal access, which limits system capacity. Conventional signal separation methods that rely on the capture effect typically require a sufficiently large power difference between colliding signals. However, due to the channel characteristics of LEO links, this condition is often difficult to satisfy. We propose ConvLoRa, a collision demodulation method for co-SF LoRa uplink signals in LEO-IoT based on a fully convolutional neural network (FCN). To improve robustness to synchronization deviations, ConvLoRa uses an up-chirp in the preamble as a reference for feature matching, and employs data augmentation to emulate synchronization deviations during training. In addition, a multi-task design is adopted to estimate the payload length with minimal introduction of extra network parameters. Experiments show that ConvLoRa achieves lower demodulation bit error rate (BER) under collision conditions compared with baselines, including CoRa and SIC-based receivers. Under the condition of a two-signal collision with SNR = −9 dB and SF = 8, the BER of the proposed method is 21% that of CoRa and 28% that of the SIC-based method. Full article

This paper presents the design, optimization, and performance evaluation of a Satellite-Based Augmentation System (SBAS) ground segment tailored to Colombia’s air navigation infrastructure, with emphasis on ionospheric anomalies in equatorial latitudes. The configuration comprises six Reference Stations (RIMS), strategically sited via geometric dilution of precision (GDOP) minimization and airspace demand models from ADS-B data. A simulation suite—integrating STK^®, Radio Mobile™, and Stanford-ESA certified monitors—quantifies service volume, link margins, and protection level compliance. Ionospheric threat characterization uses regional scintillation datasets (σln ≈ 0.36, ROTI₉₅ ≈ 85 mm/km), informing GIVE inflation and dual-frequency pseudorange integrity validation. Simulations confirm the system sustains ≥ 99.8% APV-I availability over the CAR/SAM FIR, with Horizontal and Vertical Protection Levels (HPL/VPL) bounded below 28 m and 46 m. Uplink integrity and GEO broadcast continuity are modelled under worst-case masking and multipath, confirming ICAO Annex 10 SARPs compliance. The architecture achieves a high performance-to-cost ratio, enabling nationwide SBAS coverage with a 65% cost reduction versus legacy navaids. The system is forward-compatible with dual-frequency multi-constellation SBAS (DFMC), supporting future APV-II scalability. These results position Colombia as a regional node for GNSS augmentation, fostering safety, efficiency, and procedural harmonization. Full article

Resident Space Objects (RSOs) are a collection of both man-made and natural objects in near-Earth space. Given their large orbital velocities and rapidly increasing quantity, they pose a collision threat to space assets, necessitating better Space Situational Awareness (SSA). SSA begins with detecting these objects in the first place and can be accomplished by using space-based optical images, such as images from the Fast Auroral Imager (FAI) on the CASSIOPE satellite. However, these short-exposure images are low in resolution and contain various artifacts and noise, posing challenges to traditional source detection methods. Furthermore, the background stars and RSOs both move due to the satellite’s non-constant attitude, posing a challenge for tracking algorithms. Nevertheless, these images are a valuable source of SSA data, which can be used to develop algorithms to ultimately augment the capabilities of current SSA systems. Such augmentations include performing RSO detection as a simultaneous function on existing spacecraft or allowing dedicated SSA payloads to detect RSOs during slew maneuvers, where background stars will similarly move. This paper proposes a rules-based RSO tracking algorithm tailored for low-resolution, short-exposure, space-based imagery with non-constant spacecraft attitude, addressing the challenge of distinguishing RSOs from background stars that are also in motion. This method consists of a custom thresholding algorithm, along with the Iterative Closest Point (ICP) algorithm to correct the motion of the background stars, followed by a tracking algorithm to finally detect the RSOs within the imagery, returning their pixel positions. The algorithm was tested on an 878-image dataset, achieving 79% precision and 71% recall, while detecting 87% of all RSOs at least once. These results prove that the algorithm is a feasible method for detecting RSOs in non-constant-attitude imagery, providing a means to develop current SSA systems. Full article

All existing Satellite-Based Augmentation Systems (SBAS) are regional, leading to discontinuous global coverage and significant service gaps. To overcome the inherent coverage limitations of SBAS caused by regional ground station distribution, this study integrates Low Earth Orbit (LEO) satellites with ground-based networks to enable global SBAS coverage. Using real observational data from eight LEO satellites, we demonstrate their feasibility as space-based monitoring stations. The results show that incorporating LEO satellites reduces the dual-frequency range error (DFRE) and significantly increases the number of available augmented satellites within the service region. Compared with Standard Point Positioning (SPP), this augmentation reduces the 95th-percentile horizontal and vertical positioning errors by approximately 51.4% and 44.2%, respectively. Furthermore, all stations satisfy the Approach with Vertical guidance I (APV-I) availability requirements, and no Hazardous Misleading Information (HMI) events are observed. Based on the observation data from eight LEO satellites, we construct an eight-satellite simulated constellation that matches the real satellites’ orbital characteristics, thereby validating the consistency between real-data findings and simulation-based assessments. Subsequently, we built a hybrid LEO constellation (108 Walker + 60 polar) as space-based monitoring stations integrated with ground stations to evaluate global SBAS service performance. The results show that with LEO satellite augmentation, the global number of available augmented satellites remains above nine. The 95th-percentile horizontal and vertical positioning accuracies are better than 0.75 m and 1.6 m. All global evaluation stations achieve APV-I availability above 99%. In addition, sensitivity analysis reveals that dissemination delay is a critical factor affecting protection levels and service availability, particularly at high latitudes. Overall, both real-data experiments and global simulations validate the significant benefit of LEO augmentation in improving global SBAS service performance. Full article

The rapid development of low earth orbit (LEO) satellite constellations for navigation augmentation represents significant challenges in optimizing coverage performance while minimizing system complexity. A hybrid optimization algorithm based on pelican optimization algorithm and quantum particle swarm optimization (POA-QPSO) is proposed in this paper for multi-objective optimization design of dual-layer Walker constellations. The algorithm integrates the global search capability of the POA and the local exploitation ability of QPSO, effectively balancing exploration and exploitation through a probability-driven dual-phase search mechanism, a three-tier adaptive parameter adjustment strategy, and a pareto frontier maintenance mechanism. Probability factor and quantum tunneling facilitate low-cost deep search in complex non-convex environments. Experiments demonstrate that the algorithm outperforms MOPOA and MOPSO on ZDT test functions, with an 18.5% improvement in IGD metrics. In LEO constellation optimization, the designed dual-layer configuration (800 km/144 satellites in the first layer and 1426 km/56 satellites in the second layer) achieves a 92.7% global coverage, with an average PDOP of 1.78 and 5.8 visible satellites in polar regions. Furthermore, comparative benchmark tests show that the proposed solution outperforms most mainstream algorithms and performs better than traditional medium Earth orbit satellite systems in mid-to-high latitude regions. This research provides an efficient solution for LEO navigation augmentation system design. Full article

Producing high-quality analytical reports for the environmental domain is typically time-consuming and requires significant human expertise. This paper describes MeteoChat, a semi-automatic framework for efficiently generating specialized environmental reports from heterogeneous environmental data. MeteoChat utilizes a Large Language Model (LLM) fine-tuned and integrated with Retrieval-Augmented Generation (RAG). The system’s core is its plug-and-play philosophy, which separates analytical reasoning from the data source and the report’s intended audience. The fine-tuning phase uses data-agnostic, parameterized question–context–answer triples defined by an environmental expert to teach the LLM domain-specific analytical logic and audience-appropriate communication styles. Subsequently, the RAG phase integrates the model with actual datasets, which are processed via an Extract–Transform–Load (ETL) workflow to generate statistical summaries. This architectural separation ensures that the same reporting engine can operate on different sources, such as meteorological time series, satellite imagery, or geographical data, without additional training. Users interact with the system via a web-based conversational interface, where responses are tailored for either technical experts (using explicit calculations and tables) or the general public (using simplified, narrative language). MeteoChat has been tested with real data extracted from the micrometeorological network of ARPA Lazio. Full article

Wildfires are environmental hazards with severe ecological, social, and economic impacts. Wildfires devastate ecosystems, communities, and economies worldwide, with rising frequency and intensity driven by climate change, human activity, and environmental shifts. Analyzing wildfire insights such as detection, predictive patterns, and risk assessment enables proactive response and long-term prevention. However, most of the existing approaches have been focused on isolated processing of data, making it challenging to orchestrate cross-modal reasoning and transparency. This study proposed a novel orchestrator-based multi-agent system (MAS), with the aim of transforming multimodal environmental data into actionable intelligence for decision making. We designed a framework to utilize Large Multimodal Models (LMMs) augmented by structured prompt engineering and specialized Retrieval-Augmented Generation (RAG) pipelines to enable transparent and context-aware reasoning, providing a cutting-edge Visual Question Answering (VQA) system. It ingests diverse inputs like satellite imagery, sensor readings, weather data, and ground footage and then answers user queries. Validated by several public datasets, the system achieved a precision of 0.797 and an F1-score of 0.736. Thus, powered by Agentic AI, the proposed, human-centric solution for wildfire management, empowers firefighters, governments, and researchers to mitigate threats effectively. Full article

Accurate and robust positioning is a critical enabler for Unmanned Aerial Vehicle (UAV) applications, ranging from mapping and inspection to emerging Urban Air Mobility (UAM). While Global Navigation Satellite Systems (GNSS) remain the backbone of absolute positioning, their performance is severely constrained by UAV platform characteristics and complex low-altitude environments. This paper presents a system-level review of GNSS-based UAV positioning. Instead of treating GNSS in isolation, we first link mission requirements and platform constraints, such as aggressive dynamics and Size, Weight, and Power (SWaP) limitations, to specific positioning challenges. We then critically evaluate the spectrum of GNSS techniques, from standalone and Satellite-Based Augmentation System (SBAS) modes to high-precision carrier-phase methods including Real-Time Kinematic (RTK), Post-Processed Kinematic (PPK), Precise Point Positioning (PPP), and PPP-RTK. Furthermore, we discuss multi-sensor fusion with inertial, visual, and Light Detection and Ranging (LiDAR) sensors to mitigate vulnerabilities in urban canyons and GNSS-denied conditions. Finally, we outline key challenges and future directions, highlighting integrity-aware architectures, Artificial Intelligence (AI)-enhanced signal processing, and multi-layer Positioning, Navigation, and Timing (PNT) concepts. The review provides a structured framework and system-level insights to guide resilient navigation for UAV operations in low-altitude airspace. Full article

The Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) technique enables cislunar spacecraft to obtain accurate position and velocity information, allowing full state estimation of two vehicles using only inter-satellite range (ISR) measurements when both their dynamical states are unknown. However, its stand-alone use leads to significantly increased orbit determination errors when the orbital planes of the two spacecraft are nearly coplanar, and is characterized by long initial convergence times and slow recovery following dynamical disturbances. To mitigate these issues, this study introduces an integrated navigation method that augments inter-satellite range measurements with line-of-sight vector angles relative to background stars. Additionally, an enhanced Adaptive Robust Cubature Kalman Filter (ARCKF) incorporating a chi-square test-based adaptive forgetting factor (AFF-ARCKF) is developed. This algorithm performs adaptive estimation of both process and measurement noise covariance matrices, improving convergence speed and accuracy while effectively suppressing the influence of measurement outliers. Numerical simulations involving spacecraft in Earth–Moon L4 planar orbits and distant retrograde orbits (DRO) confirm that the proposed method significantly enhances system observability under near-coplanar conditions. Comparative evaluations demonstrate that AFF-ARCKF achieves faster convergence compared to the standard ARCKF. Further analysis examining the effects of initial state errors and varying initial forgetting factors clarifies the operational boundaries and practical applicability of the proposed algorithm. Full article

In the Arctic region, the navigation and positioning accuracy of shipborne and autonomous underwater vehicle (AUV) integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) solutions is severely degraded due to poor satellite geometry, frequent ionospheric disturbances, non-Gaussian measurement noise, and strong multipath effects, as well as long-term INS-based dead-reckoning for AUVs when GNSS is unavailable underwater. In addition, the sparse ground-based augmentation infrastructure and the lack of reliable reference trajectories and dedicated test ranges in polar waters hinder the validation and performance assessment of existing marine navigation systems, further complicating the achievement of accurate and reliable navigation in this region. To improve the positioning accuracy of the GNSS/INS shipborne navigation system, this paper adopts a tightly coupled GNSS/INS navigation approach. To further enhance the accuracy and robustness of tightly coupled GNSS/INS positioning, this paper proposes an improved Adaptive Robust Extended Kalman Filter (IAREKF) algorithm to effectively suppress the effects of gross errors and non-Gaussian noise, thereby significantly enhancing the system’s robustness and positioning accuracy. First, the residuals and Mahalanobis distance are calculated using the Adaptive Robust Extended Kalman Filter (AREKF), and the chi-square test is used to assess the anomalies of the observations. Subsequently, the observation noise covariance matrix is dynamically adjusted to improve the filter’s anti-interference capability in the complex Arctic environment. However, the state estimation accuracy of AREKF is still affected by GNSS signal degradation, leading to a decrease in navigation and positioning accuracy. To further improve the robustness and positioning accuracy of the filter, this paper introduces a sliding window mechanism, which dynamically adjusts the observation noise covariance matrix using historical residual information, thereby effectively improving the system’s stability in harsh environments. Field experiments conducted on an Arctic survey vessel demonstrate that the proposed improved adaptive robust extended Kalman filter significantly enhances the robustness and accuracy of Arctic integrated navigation. In the Arctic voyages at latitudes 80.3° and 85.7°, compared to the Loosely coupled EKF, the proposed method reduced the horizontal root mean square error by 61.78% and 21.7%, respectively. Full article

The article presents an approach to designing a low-orbit remote Earth sensing spacecraft. The low operational orbit of the satellite is maintained using a corrective electric propulsion system. The comprises an optical imaging system based on the Richey-Cretien telescope design augmented with an additional swivel reflection mirror. The optical system’s layout was optimized to minimize the spacecraft’s midsection area. This reduction in the frontal cross-sectional area decreases the aerodynamic drag forces exerted by the upper atmosphere, thereby reducing the propellant mass required for orbit maintenance. The article presents a model of constraints imposed by the satellite’s power supply system on the operating modes of the electric propulsion system and the orbit correction modes. Finally, a preliminary design of a low-orbit satellite, derived from the proposed approach, is presented. Full article

The Yangtze River Basin, one of China’s most vital watersheds, sustains both ecological balance and human livelihoods through its extensive lake systems. However, since the 1980s, these lakes have experienced significant ecological degradation, particularly in terms of aquatic vegetation decline. To acquire reliable aquatic vegetation data during the peak growing season (July–September), when clear-sky conditions are scarce, we employed Chinese domestic satellite imagery—Gaofen-1/6 (GF-1/6) Wide Field of View (WFV) and Huanjing-2A/B (HJ-2A/B) Charge-Coupled Device (CCD)—with approximately one-day revisit frequency after constellation networking, 16 m spatial resolution, and excellent spectral consistency, in combination with deep learning algorithms, to monitor aquatic vegetation across the basin. Comparative experiments identified the near-infrared, red, and green bands as the most informative input features, with an optimal input size of 256 × 256. Through visual interpretation and dataset augmentation, we generated a total of 5016 labeled image pairs of this size. The U-Net++ model, equipped with an EfficientNet-B5 backbone, achieved robust performance with an mIoU of 90.16% and an mPA of 95.27% on the validation dataset. On independent test data, the model reached an mIoU of 79.10% and an mPA of 86.42%. Field-based assessment yielded an overall accuracy (OA) of 75.25%, confirming the reliability of the model. As a case study, the proposed model was applied to satellite imagery of Lake Taihu captured during the peak growing season of aquatic vegetation (July–September) from 2020 to 2025. Overall, this study introduces an automated classification approach for aquatic vegetation using 16 m resolution Chinese domestic satellite imagery and deep learning, providing a reliable framework for large-scale monitoring of aquatic vegetation across lakes in the Yangtze River Basin during their peak growth period. 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

Accurate and timely precipitation forecasting is critical for climate risk management, agriculture, and hydrological regulation. However, this task remains challenging due to the dynamic evolution of atmospheric systems, heterogeneous environmental factors, and frequent missing data in multi-source observations. To address these issues, we propose DAHG, a novel long-term precipitation forecasting framework based on dynamic augmented heterogeneous graphs with reinforced graph generation, contrastive representation learning, and long short-term memory (LSTM) networks. Specifically, DAHG constructs a temporal heterogeneous graph to model the complex interactions among multiple meteorological variables (e.g., precipitation, humidity, wind) and remote sensing indicators (e.g., NDVI). The forecasting task is formulated as a dynamic spatiotemporal regression problem, where predicting future precipitation values corresponds to inferring attributes of target nodes in the evolving graph sequence. To handle missing data, we present a reinforced dynamic graph generation module that leverages reinforcement learning to complete incomplete graph sequences, enhancing the consistency of long-range forecasting. Additionally, a self-supervised contrastive learning strategy is employed to extract robust representations of multi-view graph snapshots (i.e., temporally adjacent frames and stochastically augmented graph views). Finally, DAHG integrates temporal dependency through long short-term memory (LSTM) networks to capture the evolving precipitation patterns and outputs future precipitation estimations. Experimental evaluations on multiple real-world meteorological datasets show that DAHG reduces MAE by $\stackrel{\sim }{3}$% and improves R² by $\stackrel{\sim }{0}$.02 over state-of-the-art baselines (p < 0.01), confirming significant gains in accuracy and robustness, particularly in scenarios with partially missing observations (e.g., due to sensor outages or cloud-covered satellite readings). Full article