Search Results (307)

Unmanned Aircraft Systems (UAS) have seen a significant growth in civilian space over the past decade. The number one ranked challenge in UAS operations in Europe is regulatory obstacles such as the Specific Operations Risk Assessment (SORA) for 2023–2025. Existing approaches have focused on individual technical solutions (radio technologies, redundancy schemes, or cryptographic protections) or on high-level safety analysis, but have not integrated regulatory compliance, risk assessment, and repeatable systems models that directly support SORA artifact generation and rapid adaptation across BVLOS operational contexts. Thus, the current state-of-the-art apparatus lacks a systematic Model-Based Systems Engineering (MBSE) approach that can cater to Command and Control (C2) data-link design for Beyond Visual Line-of-Sight (BVLOS) missions. In this work, we propose an MBSE methodology designed to assist engineers in designing a C2 data link for BVLOS drone operations that complies with SORA regulations in the Netherlands and Europe. To validate the use of MBSE in a wide range of complex drone operations, we demonstrate how subtle modifications in the proposed engineering models can be made without any major overhaul of new SORA applications, and this is validate these changes through laboratory software tests and simulations. Full article

Efficient communication infrastructure is essential for Unmanned Aircraft Systems (UASs) operating beyond visual line of sight (BVLOS). Both terrestrial and non-terrestrial networks struggle with coverage gaps and are susceptible to disruptions. This paper analyzes integrated terrestrial–non-terrestrial network (TN-NTN) architectures for UAS communications in 6G, focusing on three connectivity methods: terrestrial connectivity, indirect satellite connectivity, and direct UAS–satellite links. We provide the assessment of different connectivity options. Major challenges are discussed, including antenna limitations, reliability, channel modeling, and regulatory alignment. Full article

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below $-20$ dB with main-beam peaks within $0.1$° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a $36\times 36$ impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70 $\lambda$. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering $0$°–$60$° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work. Full article

Aircraft conceptual design is an iterative process that seeks to obtain a feasible design that meets a series of mission and configuration requirements. Starting with several guesses regarding the initial sizing and aerodynamics of the future aircraft, a first resulting general layout is found, which is then subjected to trade studies where initial assumptions are altered in search of a refined design. With the aim of enhancing design solutions and reducing time costs derived from calculations, the authors of the present paper have developed ARCADE (AiRcraft ConceptuAl DEsign Tool), a framework that automates, in multiple thematic modules, the steps and calculations needed for the conceptual design process of fixed-wing aircraft. This work presents the basis for the early architecture of ARCADE, developed in Python and focused on the use of data retrieved from existing aircraft for the first design hypotheses. Initial findings of the use of ARCADE show a small relative error between the first parameter guesses, made based on similar aircraft, and the results of the next design iteration, which are independent of reference aircraft. This suggests that the design parameters of the target aircraft are accurately guessed when using existing aircraft information for the initial estimations of this process. Full article

The rapid growth of civilian unmanned aircraft systems (UAS) for various applications, such as logistics, inspection and surveillance has enabled increasingly complex UAS operations in shared airspace and in close proximity to third parties. European regulations for civilian UAS provide a comprehensive framework for operational approval, based on operational rules, risk-based approval processes, and airspace management concepts. While regulatory frameworks and current international standards provide detailed guidance for operational authorization for UAS, they do not prescribe how UAS should be developed and verified at a system and software level to support safety assurance in a structured and traceable manner. This paper addresses this gap by proposing a method for extracting system-level and software-level safety requirements from regulatory artifacts. The method interprets regulatory safety intent–expressed through operational constraints, mitigation measures, and robustness expectations–and translates it into development-relevant safety requirements under explicit operational assumptions. Building on these requirements, the paper introduces a software-centered system lifecycle for UAS development. The proposed lifecycle integrates regulatory safety intent, risk-proportionate assurance, and staged verification. Finally, through a cross-domain analysis, the paper positions the proposed approach relative to established practices from the automotive and the avionics domains, aiming to identify transferable and necessary adaptations for the development of unmanned aircraft systems. Full article

Unmanned Aerial Systems (UAS) offer sustainable solutions for urban last-mile logistics, yet existing navigation algorithms struggle with the complexity of dynamic metropolitan environments. This study optimises a reinforcement learning (RL)-based guidance, navigation, and control (GNC) algorithm using a Proximal Policy Optimisation (PPO) model within a high-fidelity simulation of Bristol City Centre. The primary contribution is training the RL model to autonomously detect and avoid dynamic obstacles, specifically manned aircraft, to ensure safe and legal drone operations. Additionally, flight operations are continuously monitored via a Structured Query Language (SQL) database to verify compliance with low airspace regulations. Simulation results demonstrate that the proposed framework achieves high obstacle detection accuracy under nominal conditions, while the implementation of curriculum learning significantly enhances the system’s adaptability and recovery capabilities during high-speed, dynamic encounters. Full article

Foreign Object Debris (FOD) poses critical risks to aircraft during takeoff and landing, resulting in billions of dollars in losses annually due to infrastructure damage and flight delays. Advancements in automated inspection technologies have enabled the use of Unmanned Aerial Systems (UAS) combined with Artificial Intelligence (AI) for rapid FOD identification. While prior research has extensively evaluated optical sensors such as RGB imaging and radar, limited work has investigated the potential of thermal imaging for improved FOD visibility under challenging environmental conditions. This study proposes DualFOD, a dual-modality detection framework that integrates a supervised YOLO12-based RGB detector with an unsupervised thermal anomaly extraction pipeline for identifying debris on runway surfaces. A decision-level fusion algorithm combines detections from both branches using spatial proximity matching to produce a unified FOD inventory. The RGB branch achieves a precision of 0.954 and mAP@0.5 of 0.890 on the held-out test set. Cross-site validation at the Cobb County Sport Aviation Complex demonstrates that thermal detection recovers debris missed by RGB at higher altitudes, with the fused output consistently outperforming either single-modality branch. This research contributes toward scalable autonomous FOD monitoring that enhances operational safety in aviation environments. Full article

The decentralisation of autonomous Unmanned Aircraft Systems (UASs) introduces significant challenges in terms of establishing secure communication and consensus in contested, resource-constrained environments. This research addresses these challenges by conducting a comprehensive performance evaluation of two cryptographic technologies: Messaging Layer Security (MLS) for group key exchange, and threshold signatures (FROST and BLS) for decentralised consensus. Seven leading open-source libraries were methodically assessed through a series of static, network-simulated, and novel bulk-signing benchmarks to measure their computational efficiency and practical resilience. This paper confirms that MLS is a viable solution, capable of supporting the group sizes and throughput requirements of a UAS swarm. It corroborates prior work by identifying the Cisco MLSpp library as unsuitable for dynamic environments due to poorly scaling group management functions, while demonstrating that OpenMLS is a highly performant and scalable alternative. Furthermore, the findings show that operating MLS in a ‘key management’ mode offers a dramatic increase in performance and resilience, a critical trade-off for UAS operations. For consensus, the benchmarks reveal a range of compromises for developers to consider, while identifying the Zcash FROST implementation as the most effective all-around performer for sustained, high-volume use cases due to its balance of security features and efficient verification. Full article

The paper presents a method for generating a pseudo-panchromatic (PPAN) orthophotomosaic that is spectrally compatible with the multispectral (MS) orthophotomosaic, and it targets the fusion of unmanned aircraft system (UAS) RGB–MS orthophotomosaics when no true panchromatic band is available. In typical UAS imaging systems, RGB and multispectral sensors operate independently and exhibit different spectral responses and spatial resolutions, making the construction of a spectrally compatible substitution intensity a critical challenge for component substitution fusion. The conventional RGB-derived PPAN preserves spatial detail but is constrained by RGB–MS spectral incompatibility, expressed as reduced corresponding-band similarity. The proposed hybrid intensity (PPAN_E) increases the mean corresponding-band correlation from 0.842 (PPAN_A) to 0.928 (PPAN_E) and reduces the across-site mean SAM from 5.782° to 4.264°, while maintaining spatial sharpness comparable to the RGB-derived intensity. It is proposed that the PPAN_E orthophotomosaic be produced as a hybrid intensity (single band) image. Specifically, a multispectral-visible-derived intensity is resampled onto the RGB grid and statistically integrated with RGB spatial detail, followed by mild high-frequency enhancement to produce the final PPAN_E orthophotomosaic. Principal Component Analysis (PCA) fusion is applied to seven archaeological sites in Northern Greece. Spectral quality is evaluated on the MS grid using band-wise (corresponding-band) correlation and the Spectral Angle Mapper (SAM), while the spatial sharpness of the fused NIR orthophotomosaic is assessed using Tenengrad and Laplacian variance. The PPAN_E orthophotomosaic consistently increases correlations relative to PPAN_A (especially in Red Edge/NIR) and reduces the mean site-mean SAM. PPAN_C yields the lowest SAM but also the lowest spatial sharpness/clarity, whereas PPAN_E maintains spatial sharpness/clarity comparable to PPAN_A, supporting a balance between spectral consistency and spatial detail, as also confirmed through comparative evaluation against established component substitution fusion methods. The approach is reproducible and avoids full histogram matching; instead, it relies on explicitly defined linear standardization steps (mean–std normalization) and controlled spatial sharpening, and performs consistently across different scenes. Full article

Unmanned Aircraft Systems (UASs) offer a promising future for aviation operations, even though it suffers larger cyber-related challenges. As such, cyber-resilience becomes a core property for drones’ operations. This paper presents a systematic meta-review of the scientific literature on Unmanned Aircraft Systems cyber-resilience, starting from 28 literature reviews and surveys in the field. This study examines three areas: the typologies of cyber threats being investigated, the cyber-resilience aspects and functions, and how proposed mitigation strategies align with and support these resilience functions. Overall, 69 cyber threats were identified, where Global Positioning System (GPS) spoofing and jamming were the most frequent ones, underscoring the vulnerability of GPS-based navigation systems in UAS. In terms of cyber-resilience functions, the largest focus remains on the identification, protection, and detection of cyber threats, while limited attention emerges to incident handling and post-event recovery. This is confirmed by the higher frequency of preventive, rather than recovery-oriented, mitigation strategies. Overall, the findings point towards a still limited cyber-resilience implementation for Unmanned Aircraft Systems, witnessing the need for more systemic efforts to guarantee truly resilient UAS operations. Full article

The safe and efficient integration of small unmanned aircraft systems (sUAS) into the National Airspace System (NAS) requires a systems-based understanding of the interrelations among human, technological, and regulatory components. Existing Federal Aviation Administration (FAA) guidelines restrict most operations to visual line of sight (VLOS), which constrains operational scalability and underscores the need for system-level innovations supporting beyond-visual-line-of-sight (BVLOS) operations. This study adopted a socio-technical systems approach to evaluate how first-person view (FPV) technologies influence operator workload and situational awareness (SA), key human performance elements within the broader sUAS safety system. Participants meeting FAA Part 107 eligibility criteria were assigned to one of three visual configurations: (a) traditional VLOS, (b) FPV using a 21-inch monitor, or (c) FPV with immersive goggles. Workload was measured with the NASA Task Load Index (NASA-TLX), and Level 1 SA was assessed via post-task recall. ANOVA results revealed no statistically significant differences across visual conditions, indicating no evidence that FPV integration either increased cognitive load or impaired perceptual awareness compared to traditional methods. Complementary analysis of NASA’s Aviation Safety Reporting System (ASRS) identified SA as the most recurrent human-factor issue, suggesting system-level implications for human–machine interaction and training design. These findings contribute to the systemic understanding of human factors in UAS operations, supporting FPV’s potential as a viable subsystem for achieving safe and effective BVLOS integration within complex socio-technical aviation systems. Full article

The rapid growth of Unmanned Aircraft Systems (UASs) and Advanced Air Mobility (AAM) presents significant integration and safety challenges for the National Airspace System (NAS), often relying on disconnected Air Traffic Management (ATM) and Unmanned Aircraft System Traffic Management (UTM) practices that contribute to airspace incidents. This study evaluates Geographic Information Systems (GISs) as a unified, data-driven framework to enhance shared airspace safety and efficiency. A comprehensive, multi-phase methodology was developed using GIS (specifically Esri ArcGIS Pro) to integrate heterogeneous aviation data, including FAA aeronautical data, Automatic Dependent Surveillance–Broadcast (ADS-B) for crewed aircraft, and UAS Flight Records, necessitating detailed spatial–temporal data preprocessing for harmonization. The effectiveness of this GIS-based approach was demonstrated through a case study analyzing a critical interaction between a University UAS (Da-Jiang Innovations (DJI) M300) and a crewed Piper PA-28-181 near Purdue University Airport (KLAF). The resulting two-dimensional (2D) and three-dimensional (3D) models successfully enabled the visualization, quantitative measurement, and analysis of aircraft trajectories, confirming a minimum separation of approximately 459 feet laterally and 339 feet vertically. The findings confirm that a GIS offers a centralized, scalable platform for collating, analyzing, modeling, and visualizing air traffic operations, directly addressing ATM/UTM integration deficiencies. This GIS framework, especially when combined with advancements in sensor technologies and Artificial Intelligence (AI) for anomaly detection, is critical for modernizing NAS oversight, improving situational awareness, and establishing a foundation for real-time risk prediction and dynamic airspace management. Full article

Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, flight altitudes, and image overlaps are limited, restricting guidance for optimizing flight strategies. This study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation. UAS imagery was collected at four altitudes (30–120 m, corresponding to 0.5–2.0 cm ground sampling distance, GSD) with multiple side and front overlaps (67–94%) over a 2–ha field planted with corn, cotton, sorghum, and soybean on three dates across two growing seasons, producing 90 datasets. Orthomosaics, point clouds, and DSMs were generated using Pix4Dmapper, and plant height estimates were extracted from both DSMs and point clouds. Results showed that point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types. Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R² = 0.92–0.95) in 2019 and 0.07–0.08 m (R² = 0.80–0.89) in 2022. Across multiple side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps, indicating that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy. Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R² = 0.67–0.95). These findings provide practical guidance for selecting UAS flight and processing parameters to achieve accurate, efficient 3D modeling and plant height estimation. By balancing flight altitude, image side and front overlap, and photogrammetric processing settings, users can improve operational efficiency while maintaining high-accuracy plant height measurements, supporting faster and more cost-effective phenotyping and precision agriculture applications. Full article

Meteorological measurements from Unmanned Aircraft Systems (UASs) increase the volume of observations available for validating and improving high-spatiotemporal-resolution models. Accurate model forecasts for UAS operations are essential to the successful development of the low-altitude economy (LAE). In this study, two UAS test flights were analyzed to assess the consistency between UAS measurements and Regional Atmospheric Modeling System model outputs, thereby evaluating model forecast skill. UAS measurements were compared with ground-based anemometer and radiosonde observations to meet the World Meteorological Organization observational requirements at both the Threshold and Goal levels. Model-forecast turbulence exhibited strong agreement with atmospheric turbulence derived from high-frequency UAS wind data. The numerical weather prediction model at high spatial and temporal resolution is found to have sufficiently accurate forecasts to support UAS operation. A computational fluid dynamics model was also tested for high-resolution wind and turbulence forecasting; however, it did not yield improvements over the meteorological model. This work represents the first study of its kind for LAE applications in Hong Kong, and further statistical analyses are planned. 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