Search Results (317)

This paper presents a two-layer consistency control framework for the collaborative assembly of multiple aircraft in complex environments, comprising a low-level control layer and a high-level guidance layer. The control layer develops a robust anti-interference law by integrating an extended state observer (ESO) with Backstepping for attitude control and employing constrained Backstepping for velocity regulation. The guidance layer ensures safe and coordinated assembly. A time-varying communication topology is adopted to guarantee collision-free maneuvers. An assembly trajectory is generated for each aircraft based on a position allocation strategy and the Dubins path planning method. To achieve time-coordinated arrival, a speed consensus protocol is designed, guiding the aircraft into a sparse formation. Subsequently, consensus-based control laws for both attitude and velocity are implemented to transition into a tight formation. The effectiveness of the proposed framework is validated through aircraft six-degree-of-freedom (6-DoF) simulations, which confirm that it significantly improves the safety and robustness of the multi-aircraft assembly process. Full article

Maritime emergency communication facilities are essential for establishing land-sea connectivity and supporting disaster rescue operations. However, current systems often struggle with slow deployment, link instability, and insufficient coverage. To overcome these limitations, this paper proposes a method utilizing aircraft equipped with communication payloads for rapid network construction in target sea areas, aiming to satisfy the dual demands of quick response and stable transmission. A critical component of this framework is the optimal selection of aircraft bases. Addressing the distinct coverage capabilities of different platforms, we construct a multi-objective optimization model for base location. This model integrates a hierarchical coverage mechanism involving multiple aircraft types and is solved using the proposed Hybrid Adaptive Multi-objective Optimization (HAMO) algorithm. Experimental validation in the Bohai Sea region demonstrates the feasibility and effectiveness of the proposed model. Full article

As global airspace operations grow increasingly complex, the risk of near-mid-air collisions (NMACs) poses a persistent and critical challenge to aviation safety. Traditional collision-avoidance systems, while effective in many scenarios, are limited by rule-based logic and reliance on transponder data, particularly in environments featuring diverse aircraft types, unmanned aerial systems (UAS), and evolving urban air mobility platforms. This paper introduces a novel, integrative machine learning framework designed to analyze NMAC incidents using the rich, contextual information contained within the NASA Aviation Safety Reporting System (ASRS) database. The methodology is structured around three pillars: (1) natural language processing (NLP) techniques are applied to extract latent topics and semantic features from pilot and crew incident narratives; (2) cluster analysis is conducted on both textual and structured incident features to empirically define distinct typologies of NMAC events; and (3) supervised machine learning models are developed to predict pilot decision outcomes (evasive action vs. no action) based on integrated data sources. The analysis reveals seven operationally coherent topics that reflect communication demands, pattern geometry, visibility challenges, airspace transitions, and advisory-driven interactions. A four-cluster solution further distinguishes incident contexts ranging from tower-directed approaches to general aviation pattern and cruise operations. The Random Forest model produces the strongest predictive performance, with topic-based indicators, miss distance, altitude, and operating rule emerging as influential features. The results show that narrative semantics provide measurable signals of coordination load and acquisition difficulty, and that integrating text with structured variables enhances the prediction of maneuvering decisions in NMAC situations. These findings highlight opportunities to strengthen radio practice, manage pattern spacing, improve mixed equipage awareness, and refine alerting in short-range airport area encounters. Full article

A considerable number of people are exposed to noise from military aircraft daily, but its health effects have not been sufficiently examined. This study assessed the association of exposure to such noise with mental well-being and sleep disturbance among people living in Okinawa prefecture, where there are two U.S. military air bases. In 2024, data were collected from 394 residents in high-, low-, and no-exposure communities using the WHO-5 Well-being Index and the Athens Insomnia Scale. Among respondents, 55.8% were female; the largest age groups were 70’s (25.4%) and 60’s (23.6%). Poor mental well-being and sleep disturbance were most prevalent in the high-exposure community (poor mental well-being: 38.2%, sleep disturbance: 46.6%), followed by low-exposure (36.1%, 46.3%) and no-exposure (21.9%, 29.0%) communities. Multivariate logistic regression analyses showed that compared to no-exposure community, the high-exposure and low-exposure communities were significantly more likely to have poor mental well-being (odds ratio (OR): 1.84, 95% confidence interval (CI): 1.05–3.23; OR: 1.94, 95% CI: 1.05–3.56), as well as sleep disturbance (OR: 1.98, 95% CI: 1.17–3.35; OR: 2.04; 95% CI: 1.16–3.59, respectively). The results suggest that there is a substantial need to address the noise from military aircraft in Okinawa. Full article

Accurate estimation of aircraft operations, such as takeoffs and landings, is critical for airport planning and resource allocation, yet it remains particularly challenging at non-towered airports, where no dedicated surveillance infrastructure exists. Existing solutions, including video analytics, acoustic sensors, and transponder-based systems, are often costly, incomplete, or unreliable in environments with mixed traffic and inconsistent radio usage, highlighting the need for a scalable, infrastructure-free alternative. To address this gap, this study proposes a novel dual-pipeline machine learning framework that classifies pilot radio communications using both textual and spectral features to infer operational intent. A total of 2489 annotated pilot transmissions collected from a U.S. non-towered airport were processed through automatic speech recognition (ASR) and Mel-spectrogram extraction. We benchmarked multiple traditional classifiers and deep learning models, including ensemble methods, long short-term memory (LSTM) networks, and convolutional neural networks (CNNs), across both feature pipelines. Results show that spectral features paired with deep architectures consistently achieved the highest performance, with F1-scores exceeding 91% despite substantial background noise, overlapping transmissions, and speaker variability These findings indicate that operational intent can be inferred reliably from existing communication audio alone, offering a practical, low-cost path toward scalable aircraft operations monitoring and supporting emerging virtual tower and automated air traffic surveillance applications. Full article

Directional frequency analysis and recording (DIFAR) is a widely used sonobuoy in modern underwater acoustic monitoring and surveillance. The sonobuoy is installed in the area of interest, collects underwater data, and transmits the data to nearby aircraft for data analysis. In this process, transmission of a large volume of raw data poses significant challenges due to limited communication bandwidth. To address this problem, existing studies on autoencoder-based methods have drastically reduced amounts of information to be transmitted with moderate data reconstruction errors. However, the information bottleneck inherent in these autoencoder-based methods often leads to significant fidelity degradation. To overcome these limitations, this paper proposes a novel autoencoder method focused on the reconstruction fidelity. The proposed method operates with two key components: Gated Fusion (GF), proven critical for effectively fusing multi-scale features, and Squeeze and Excitation (SE), an adaptive Channel Attention for feature refinement. Quantitative evaluations on a realistic simulated sonobuoy dataset demonstrate that the proposed model achieves up to a 90.36% reduction in spectral mean squared error for linear frequency modulation signals compared to the baseline. Full article

High-Frequency (HF) geolocation is crucial for emergency search and rescue operations and for re-geolocation of missing targets. This paper proposes a two-stage (Receiver selection then geolocation with Random Spatial Spectrum (RSS)) framework with HF skywave propagation as the main link, which is suitable for scenarios where the electric Vertical Take-off and Landing (eVTOL) aircraft loses contact, crashes, or has communication interruption after a malfunction. First, stage A employs two receiver selection paths. One is selection with unknown biases, which combines geometric observability to determine receiver selection. The other is selection with bias priors, which introduces non-line-of-sight bias priors and robust weighting to improve availability. Secondly, stage B constructs RSS-based geolocation using grid objective function matching to alleviate the sensitivity of explicit time difference estimation to noise and model mismatch, thereby maintaining robustness under non-line-of-sight (NLOS) conditions. Finally, simulation and actual measurements demonstrate that the “select first, geolocation later” approach achieves superior overall performance compared to direct geolocation without receiver selection. This study provides a methodological basis and initial field evidence for HF skywave-based emergency eVTOL geolocation. Full article

There is growing interest in uncrewed aircraft system (UAS) technology to supplement coastal restoration monitoring, yet it’s unclear how UAS data products compare to traditional field monitoring data that are fundamental to restoration programs. In this study, wetland vegetation classifications were generated from UAS imagery, lidar data, and supervised methods at restoration sites (LaBranche and Spanish Pass, Louisiana) and compared to traditional field survey data. Analyses examined model factors, method (maximum likelihood and random forest), data source (5- and 10-band imagery plus lidar data), and plot, on classification performance for (1) taxa richness: factors did not affect model comparisons, except for method at Spanish Pass; (2) community assemblage: LaBranche models were more similar to field data, though plot was a factor at both sites and method was a factor at Spanish Pass; (3) species presence identification: LaBranche models performed moderately better, but were species dependent; and (4) percent cover: plot was a factor at both sites, though underestimations were more frequent. Data source did not affect performance, method had variable influence on select metrics, and plots with higher taxa richness or complex canopy structure showed reduced model performance at LaBranche and Spanish Pass, respectively. Capabilities and limitations of UAS technology for wetland vegetation classification are highlighted, offering an understanding of its utility in assessing restoration outcomes related to vegetation. Full article

In this article, an approach to balance the State of Charge (SoC) of two batteries connected to the DC bus of a fuel cell (FC) electric aircraft by Droop-controlled converters is described. The proposed algorithm is based on shifting the Droop reference voltages and prevents the simultaneous charging and discharging of the batteries. This approach is not only practical but also highly versatile, as it is compatible with all converters as long as the Droop voltage can be changed remotely, and a current measurement is provided to a central controller. No further programming access to the DC/DCs is necessary. There is no need for nonlinear or different-valued Droop resistances for charging and discharging. The balancing approach is validated via simulation in MATLAB/Simulink 2024a.The results show that the proposed approach achieves SoC balancing without degrading the dynamic performance of the grid. The delays added by the slower communication with the central controller have a minimal impact on performance. Full article

Aircraft emissions are a growing environmental concern due to their contribution to local air pollution and potential health risks, particularly around rapidly expanding airports. In Egypt, rapid urban growth and tourism have driven the construction of new airports, underscoring the need to assess their environmental impacts, particularly those related to aircraft emissions in the surrounding areas. Few studies have assessed aircraft emissions across multiple Egyptian airports, particularly under future capacity and climate scenarios, using dispersion models. This study evaluates the environmental impact of aircraft emissions at four Egyptian airports using the Graz Lagrangian Dispersion Model (GRAL). The analysis accounts for projected increases in airport capacity through 2030 and 2035 and examines how climate change may influence pollutant dispersion. Emissions from 2021 served as a baseline, while future meteorological conditions were simulated with the RegCM4 regional climate model under the RCP4.5 scenario. Results show that maximum daily average carbon monoxide concentrations at Administrative Capital Airport increased from ~24.5 µg/m³ in 2021 to ~100.3 µg/m³ in 2035, while nitrogen dioxide concentrations at El-Meliz Airport rose from ~20.3 to ~47.6 µg/m³. Similar upward trends were observed for sulfur dioxide and particulate matter (PM₁₀), although all simulated values remained below the thresholds established by Egyptian Environmental Law. These findings highlight that continued growth in aviation activity, even without breaching national standards, may contribute to long-term health risks for nearby communities. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

Roof damage caused by hurricanes and other storms needs to be rapidly identified and repaired to help communities recover from catastrophic events and support the well-being of residents. Traditional, ground-based inspections are time-consuming but have recently been expedited via manual interpretation of remote sensing imagery. To potentially accelerate the process, automated methods involving artificial intelligence (i.e., deep learning) can be applied. Here, we present an end-to-end workflow for training and evaluating deep learning image segmentation models that detect and delineate two classes of post-storm roof damage: roof decking and roof holes. Mask2Former models were trained using 2500 roof decking and 2500 roof hole samples from drone RGB orthomosaics (0.02–0.08 m ground sample distance [GSD]) captured in Sint Maarten and Dominica following Hurricanes Irma and Maria in 2017. The trained models were evaluated using 1440 reference samples from 10 test images, including eight drone orthomosaics (0.03–0.08 m GSD) acquired outside of the training areas in Sint Maarten and Dominica, one drone orthomosaic (0.05 m GSD) from the Bahamas, and one orthomosaic (0.15 m GSD) captured in the US Virgin Islands with a crewed aircraft and different sensor. Accuracies increased with a single-class modeling approach (instead of training one dual-class model) and expansion of the training datasets with 500 roof decking and 500 roof hole samples from external areas in the Bahamas and US Virgin Islands. The best-performing models reached overall F1 scores of 0.88 (roof decking) and 0.80 (roof hole). In this study, we provide: our end-to-end deep learning workflow; a detailed accuracy assessment organized by modeling approach, damage class, and test location; discussion of implications, limitations, and future research; and access to all data, tools, and trained models. Full article

Intelligent reflecting surface-assisted unmanned aerial vehicles (IRS-UAVs) have been widely applied in various communication scenarios. This paper addressed the uplink communication problem in wireless sensor networks (WSNs) by proposing a novel double IRS-UAVs assisted framework to improve the pairwise sum rate. Specifically, nodes with relatively short signal transmission distances upload signals via a single-reflection link, while nodes with relatively long distances upload signals through a dual-reflection link involving two IRSs. Within each work cycle, the IRS-UAVs followed a fixed service sequence to cyclically assist all sensor node pairs. We designed a joint optimization algorithm that simultaneously optimized the UAV trajectories and IRS phase shifts to maximize the pairwise sum rate while guaranteeing each node’s transmission rate meets a minimum quality of service (QoS) constraint. Specifically, we introduce slack variables to linearize the inherently nonlinear constraints arising from interdependent variables, thereby transforming each subproblem into a more manageable form. These subproblems are then solved iteratively within a coordinated optimization framework: in each iteration, one subproblem is optimized while keeping variables of others fixed, and the solutions are alternately updated to refine the overall performance. The numerical results show that this algorithm can effectively optimize the flight trajectory of the unmanned aircraft and significantly improve the pairwise total rate of the system. Compared with the two traditional schemes, the average optimization rates are 11.91% and 16.36%. Full article

To address the key issues in cooperative collision avoidance of multiple aircraft, such as unknown dynamics, external disturbances, and limited communication resources, this paper proposes a reference correction method based on the Artificial Potential Field-Control Barrier Function (APF-CBF) and combines it with a dynamic event-triggered mechanism to achieve efficient cooperative control. This paper adopts a Fuzzy Wavelet Neural Network (FWNN) to design a finite-time disturbance observer. By leveraging the advantages of FWNN, which integrates fuzzy logic reasoning and the time-frequency locality of wavelet basis functions, this observer can synchronously estimate system states and unknown disturbances, to ensure the finite-time uniformly ultimate boundedness of errors and break through the limitation of insufficient robustness in traditional observers. Meanwhile, the APF is embedded in the CBF framework. On the one hand, APF is utilized to intuitively describe spatial interaction relationships, thereby reducing reliance on prior knowledge of obstacles; on the other hand, CBF is used to strictly construct safety constraints to overcome the local minimum problem existing in APF. Additionally, the reference correction mechanism is combined to optimize trajectory tracking performance. In addition, this paper introduces a dynamic event-triggered mechanism, which adjusts the triggering threshold by real-time adaptation to error trends and mission phases, realizing “communication on demand”. This mechanism can reduce communication resource consumption by 49.8% to 69.8% while avoiding Zeno behavior. Theoretical analysis and simulation experiments show that the proposed method can ensure the uniformly ultimate boundedness of system states and effectively achieve safe collision avoidance and efficient formation tracking of multiple aircraft. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article