Search Results (131)

Air Traffic Control Officers (ATCOs) are responsible for controlling air traffic and ensuring the safety of the aircraft. Capacity, understood as the maximum number of aircraft that can be safely managed for one hour, is calculated based on the workload of ATCOs. This calculation normally is based on a manual and tedious data collection process that demands a high consumption of human resources. To improve and relieve human re-sources, automation tools that automatically generate a preliminary annotation of Air Traffic Control (ATC) activity have been developed. This paper focuses on the feasibility of employing data-driven approaches using neural networks to classify ATC events, as well as if it is possible to improve the performance of these ATC-activity tools. Particularly, this approach seeks to infer ATC intent for separation actions, which are the most critical in terms of ATC workload. A modular methodology has been developed to include information from different sources: flight plans, radar trajectories, trajectory prediction, conflict detection and rule-based knowledge. Different experiments are evaluated based on the different input’s combination, as well as three neural networks (Multilayer Perceptron, Convolutional Neural Network and TabNet). Results show that TabNet is the best neural network option, reaching a similar performance in task classification than current ATC tools and improving classification metrics around 4% by employing the outputs of ATC tool metrics as inputs. Full article

Efficient multi-hop prediction over large-scale spatio-temporal knowledge graphs of aircraft taxiing trajectories remains challenging, as existing methods focus either on static multi-hop relations or on accuracy improvement for spatio-temporal single-hop predictions, leading to computational inefficiency. This paper proposes a vector-index-supported multi-hop prediction method. First, a knowledge graph embedding technique that integrates spatio-temporal features maps the trajectory graph into a low-dimensional complex vector space. Then, a hierarchical query acceleration structure based on IndexIVFFlat is constructed. A clustering strategy guided by the distribution of trajectory data partitions the vector space into subspaces, and approximate nearest neighbor search within those subspaces rapidly prunes the candidate set to accelerate multi-hop retrieval. Experiments on real aircraft taxiing trajectory datasets and general benchmarks show that the proposed method substantially improves prediction efficiency while maintaining competitive accuracy. The results demonstrate that the vector index mechanism effectively balances accuracy and efficiency, and the efficiency has been improved by at least 56.65%. This work provides a key technical foundation for real-time analysis and intelligent prediction of large-scale aircraft taxiing trajectories. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

This paper presents an Improved Range Equation (IRE) for fully fueled aircraft that incorporates climb, cruise, and descent phases within a single analytical framework. Unlike the classical Bréguet equation, which assumes steady cruise and neglects non-cruise segments, the proposed formulation introduces phase-dependent fuel fractions to account for mission-wide fuel consumption. The model is validated against (i) a forward–backward iterative numerical method based on BADA data and (ii) real flight trajectories obtained via waypoint reconstruction (which is also the ground truth). Three aircraft types—ATR72-600, B737-800, and B777-300—were analyzed across nine routes ranging from 155 km to 7538 km. Results show that the IRE reduces the relative error compared to measured waypoint distance/s by approximately 26–77% compared with the classical Bréguet equation, depending on aircraft class. Here, the reported percentages represent the reduction in percentage error relative to the Bréguet-based estimates using waypoint-reconstructed trajectories as ground truth. For short-haul flights (ATR72-600),, improvements of nearly 60–73% were observed, while for medium- and long-haul aircraft, improvements of 26–77% were observed. The proposed model also closely matches the numerical method, with differences typically below 70–80 km from the original value, again depending on aircraft class. These results demonstrate that incorporating climb and descent phases significantly improves range prediction accuracy, particularly for short-haul missions where non-cruise segments represent a substantial portion of total flight distance. The IRE retains the analytical simplicity of the Bréguet formulation while achieving accuracy comparable to computationally intensive numerical methods. Full article

A new class of modified minimum state error variance (MSEV) state-space based optimal linear quadratic Gaussian (LQG) regulators for closed-loop structures with estimated feedback has been proposed in this article. The negative feedback path is designed as the cascade of the digital LQG regulator and discrete Kalman state observer. The proposed design enables tracking of a time-varying reference input using the predictive control approach. Moreover, the proposed tracking method utilizes a multivariable continuous-time Cauchy state-space model of nonlinear, nonstationary dynamic systems. The resulting control strategy is approximately optimal, as the optimality of the LQG design holds locally for each linearized model around the respective operating point and does not extend to the global nonlinear system. In this sense, starting from the prespecified nominal state trajectory to be tracked, a numerical optimization procedure minimizing the squared tracking error at each step by using the Nelder–Mead direct search simplex algorithm under the required constraints on the input signal has been developed. The LQG regulator and Kalman state observer are designed by utilizing the linear discrete-time state variable models that properly approximate the nonlinear system dynamics across the nominal state trajectory. The performance of the proposed design is validated by simulating a six-degree-of-freedom nonlinear aircraft model across typical flight regimes. Full article

This paper presents the Performance Prediction Tool developed within the SATERA project. The tool evaluates the performance of a space-based composite ADS-B and multilateration system for independent aircraft localization. It uses receivers deployed onboard a constellation of LEO satellites. Multilateration can be evaluated using time-based measurements, as well as additional measurements such as, frequency and angle of arrival of the received signals. The tool is based on the evaluation of the Cramér–Rao lower bound and it is implemented in MATLAB with a user-friendly graphical interface. The tool allows the user to define the satellite constellation, link budget, measurement types and errors, and to simulate the system performance over an aircraft trajectory or an area. Moreover, the outputs include DOP, number of visible satellites and system availability, which can be visualized and exported for further analysis. Full article

Accurate prediction of store trajectories at the point of release from an unmanned/manned aircraft is an essential requirement for safety and precision. Captive Trajectory System (CTS) is a well-known feature of wind-tunnel testing to simulate the dynamics of store separation. To accurately replicate real-world aerodynamic conditions based on measured forces and moments, it utilizes a six-degree-of-freedom (6-DOF) robotic arm controlled by a closed-loop control system that solves the store’s equations of motion. In this study, a wing–pylon–store configuration is used as a sample case, and published experimental trajectories are used as input. A 6-DOF robotic arm named ROBO-S is designed to follow these trajectories in a CTS setup. The kinematic analysis of ROBO-S is performed in this study. The Denavit–Hartenberg (DH) method is used for the calculation of forward kinematics, whereas geometric techniques are used for inverse kinematics calculations. A simulation of kinematic analysis is performed in MATLAB R2021a. The mechanical design of ROBO-S is carried out in PTC CREO 9.0. MATLAB simulations confirm that the robotic arm can follow the trajectory obtained from published experimental results. To demonstrate the feasibility of the design, the robotic arm is fabricated using 3D printing. The results demonstrate the potential of the developed system in accurately following trajectories for wind-tunnel testing applications. Full article

The evolution towards “more-electric” aircraft has accelerated in the last decade, motivated by environmental concerns and the development of new market frontiers such as urban air mobility. This transition is affecting both propulsion and aircraft systems, with electro-mechanical brakes (E-Brakes) representing a promising alternative to traditional hydraulic solutions. While E-Brakes offer advantages such as reduced system complexity and elimination of hydraulic leakage issues, they remain a relatively unproven technology in civil aviation. In this context, the development of Prognostics and Health Management (PHM) solutions aligns with the need for continuous monitoring of novel components while also providing the benefits typically associated with prognostic techniques. This paper presents the preliminary stages of the development of a PHM framework for an E-Brake intended for future executive-class aircraft. Since experimental activities are not yet available, the analysis was carried out on simulated data generated through a high-fidelity model of the system. The study focuses on brake pad wear as the primary degradation mechanism and proposes a particle-filtering approach to estimate the health state and predict the Remaining Useful Life (RUL). Early results obtained from simulated fault-to-failure trajectories prove the ability of the algorithm to track degradation and to provide reliable prognostic forecasts, paving the way for future validation with real-world data. Full article

In the aviation industry the so-called ballistic impact of small accidental or human-made sources on aircraft elements during their service life encompasses several scenarios of practical interest. The experimental assessment of ballistic impact requires dedicated infrastructures (such as the light-gas gun system utilized in this study) and exhibits intrinsic difficulties, mainly concerning the proper acceleration of a projectile and the accurate measurement by a high-speed camera of its (inlet and outlet) velocity. As a first objective, this study aimed at characterizing the dynamic response of fiber metal laminates, manufactured ad hoc by the authors with two different stacking sequences currently not available in commerce. The layups included aluminum 2024 T3 and aramid fiber-reinforced prepregs, leading through specific treatments to excellent specific properties. The collision of the laminate with a 25 g, 9 mm radius steel sphere, traveling at speeds ranging from 90 to 145 m/s, caused a variety of scenarios: partial or complete penetration, with the projectile passing through and continuing its trajectory, remaining stuck in the sample (embedment) or even being bounced back (ricochet). The experimental information led to the estimation, for each typology of sample, of a conventional ballistic limit according to the Lambert-Jonas approximation, as a second objective, these data were utilized to validate an accurate heterogeneous model of the samples developed in the ABAQUS^® platform, discretized by finite elements in explicit dynamics and including geometric nonlinearity and contact. We describe plasticity and damage of the metal layers by the Johnson–Cook phenomenological model, progressive failure in the fiber-reinforced plies through a 2D Hashin criterion with damage evolution, and interlaminar debonding at multiple cohesive interfaces governed by the Benzeggagh–Kenane criterion. The outlet speed of the bullet measured during the experiments was retrieved correctly by this model, and a satisfactory agreement of the finite element predictions was found with the deformation patterns and the damage mechanisms identified by post mortem visual inspection. Finally, several discussion points are raised, concerning the robustness of the numerical analyses, the reliability of the constitutive modeling and the identification of the governing parameters. Full article

Ground incidents on airport aprons can cause substantial operational disruption and economic loss, while conventional surveillance (e.g., Surface Movement Radar (SMR), Closed-Circuit Television (CCTV)) often lacks the resolution and proactive decision support required for close-proximity operations. This study proposes a UAV-deployable, camera-agnostic Computer Vision (CV) framework for collision-risk warning from elevated viewpoints. An optimised YOLOv8-Seg backbone performs multi-class aircraft segmentation (airplane, wing, nose, tail, and fuselage) and is integrated with four MOT algorithms under identical evaluation settings. For quantitative tracker benchmarking, DeepSORT provides the strongest overall performance on the airplane-only MOTChallenge-format ground truth (MOTA 92.77%, recall 93.27%). To mitigate the scarcity of annotated apron-incident data, a labelled 997-frame MOT dataset is created via an MSFS simulation-based reenactment inspired by the 2018 Asiana–Turkish Airlines wing-to-tail event at Istanbul Ataturk Airport. The framework further introduces a dual-module warning mechanism that can operate independently: (i) a reactive module using image-plane proximity derived from segmentation masks, and (ii) a proactive module that predicts short-horizon conflicts via trajectory extrapolation and IoU-based future overlap analysis. The approach is evaluated on multiple simulated incident scenarios and assessed on a real apron video from Hong Kong International Airport; additionally, laboratory-scale UAV experiments using diecast aircraft models provide end-to-end feasibility evidence on unmanned-platform imagery. Overall, the results indicate timely warnings and practical feasibility for low-overhead UAV-enabled apron monitoring. Full article

Precise Estimated Time of Arrival (ETA) prediction in Terminal Maneuvering Areas (TMA) constitutes a prerequisite for efficient arrival sequencing and airspace capacity management. While data-driven approaches outperform kinematic models, conventional Recurrent Neural Networks (RNNs) exhibit limitations in modeling complex multi-aircraft spatial interactions and lack the capability to quantify predictive uncertainty. Conversely, Spiking Neural Networks (SNNs) enable energy-efficient event-driven computation, yet their applicability to continuous trajectory regression is hindered by “input starvation,” where normalized state vectors fail to induce sufficient neural firing rates. This study proposes a Probabilistic Spiking Transformer (PST) architecture to integrate neuromorphic sparsity with global attention mechanisms. An Adaptive Spiking Temporal Encoding mechanism incorporating learnable linear projections is introduced to resolve the regression-spiking incompatibility, facilitating the autonomous mapping of continuous trajectory dynamics into sparse spike trains without heuristic scaling. Concurrently, a Distance-Biased Multi-Aircraft Cross-Attention (MACA) module models air traffic conflicts by weighting spatial interactions according to physical proximity, thereby embedding separation constraints into the feature extraction process. Evaluation on large-scale real-world ADS-B datasets demonstrates that the PST yields a Mean Absolute Error (MAE) of 49.27 s, representing a 60% error reduction relative to standard LSTM baselines. Furthermore, the model generates well-calibrated probabilistic distributions (Prediction Interval Coverage Probability > 94%), offering quantifiable uncertainty metrics for risk-based decision support while ensuring real-time inference suitable for operational deployment. Full article

Electromechanical Actuators (EMAs) are critical components in More-Electric Aircraft (MEA) and Reusable Launch Vehicles (RLVs), yet they remain vulnerable to jamming and fatigue failures under high-stress flight maneuvers. Existing Health-Aware Flight Control approaches often treat failure prediction and control allocation as separate processes, leading to suboptimal sortie generation rates. This paper presents a reliability-adaptive control framework that unifies trajectory tracking with online health management. Empowered by a hierarchical mission-to-control architecture, the system employs stochastic Model Predictive Control (SMPC) to actively modulate control surface deflection profiles in real time. A comparative case study on a coupled EMA drivetrain demonstrates that the proposed controller extends useful life by 65% compared to fixed-gain baselines, achieves 23% higher mission performance than reactive PID controllers, and it maintains zero constraint violations throughout the mission by optimally distributing the health budget across mission phases. Full article

Air transport, acknowledged as the safest and most efficient mode for long-haul travel, is confronted with diverse challenges aimed at improving its environmental performance. A notable aspect of this effort involves the formation of contrails, arising from the emission of water vapor and condensation nuclei in a cold, ice-supersaturated atmosphere, which represents one of the most difficult-to-predict yet physically quantifiable environmental impacts of air traffic. Adopting the bottom-up principle to evaluate individual contrails for trajectory optimization introduces uncertainties in calculating the radiative forcing of contrails and modeling their life cycle. Former studies for modeling the microphysical life cycle of individual contrails based on a 2D Gaussian plume model could be validated with a photographic contrail tracking method in the mid-latitudes. However, contrails rarely form individually over Central Europe; rather, they form as an accumulation behind many aircraft flying through an ice-supersaturated region. For this reason, the 3D Gaussian plume model has been extended for the co-existence of several contrails. The greater the overlap of the contrails, the greater the competition in ice supersaturation between the contrails and therefore the greater the reduction in lifetime compared to single contrails. Furthermore, with increasing overlap, the number density of ice crystals increases, resulting in smaller ice crystals with shorter lifetimes. The overlap effect is also reflected in the angle between non-parallel contrails. The results can be used for further studies on the optical properties of real co-existing contrails. Full article

Electric-powered unmanned guidance vehicles provide surface taxiing guidance for arriving and departing aircraft within the airport movement area, enabling sustained safety under complex operational conditions and improving overall operational efficiency, particularly under low-visibility scenarios. In this context, how to design scientifically rigorous operational trajectories for the three phases of unmanned guidance vehicle operations—dispatch, guidance, and recovery—remains an open and important research problem. This study proposes a three-stage trajectory-planning method for unmanned guidance vehicles, including initial trajectory planning, conflict prediction, and conflict resolution. First, the Guidance Unit—composed of the unmanned guidance vehicle and the guided aircraft—is defined, and a standard speed-profile design model is established for this unit. Then, considering airport operational-safety constraints, a conflict prediction algorithm for the guidance process is developed, which identifies potential conflicts in guidance trajectory planning based on time-window overlap analysis. Subsequently, under operational safety constraints, an optimization model aiming to minimize the maximum guidance time is formulated, and a trajectory planning algorithm for unmanned guidance vehicles based on the improved A* algorithm is designed to generate conflict-free operational trajectories. Finally, a simulation study is conducted using a major airport in Southwest China as a case study. The results show that (1) the speed-profile design and airport operational-rule constraints affect the operational trajectories of unmanned guidance vehicles; (2) the proposed algorithm enables coordinated planning of both speed control and path selection, thereby improving overall operational efficiency by 43.65% compared with conventional operations, while ensuring conflict-free airport surface taxiing, due to the adoption of an improved A* trajectory-planning algorithm for unmanned guidance vehicles; (3) under the electric-powered guidance-vehicle scheme proposed in this study, the method achieves a 34.52% reduction in total energy consumption during the guidance phase compared with traditional Follow-Me guidance, enabling the simultaneous optimization of operational efficiency and energy consumption. Full article

With the rapid development of the low-altitude economy, the deployment of unmanned aircraft vehicles (UAVs) in many fields is increasing continuously, and the demand for collaborative flights is also growing. However, the issue of flight safety in complex airspace remains a pressing concern. Precise flight path prediction, collision detection, and avoidance are paramount for secure collaborative operations. This study proposes an integrated framework that combines an EKF-LSTM model for trajectory prediction, a Trajectory Dispersion Cone (TDC) method for probabilistic collision risk assessment, and a Velocity Obstacle-Model Predictive Control (VO-MPC) strategy for dynamic collision avoidance. Experimental results demonstrate the advantages of our approach: the EKF-LSTM model reduces prediction errors in complex flight states. Furthermore, the VO-MPC method achieves a 99.8% collision avoidance success rate under low-noise conditions—an 8.6% improvement over traditional MPC—while reducing the average collision probability by 66.7%. It also maintains stable performance under medium- and high-noise conditions, reducing the collision probability to only 27.7% and 34.2% of that of conventional MPC, respectively. The proposed framework offers a solution for safe manned–unmanned collaboration in complex environments. Future work will extend these methods to multi-aircraft cooperative scenarios. Full article