Search Results (519)

High-speed near-ground flight presents critical challenges for large-inertia UAVs carrying payloads, including complex obstacles and communication-denied environments. Unlike agile small drones, these platforms require both rapid path planning and strict adherence to trajectory tracking constraints for safe obstacle avoidance. This paper proposes a two-stage autonomous navigation framework tailored for large-inertia UAVs. The framework integrates: (1) an enhanced LiDAR model with physical optical noise for improved simulation fidelity; (2) an ESDF + OctoMap dual-map construction method supporting global search and local optimization; and (3) a global BIT* planner combined with a B-spline local optimizer embedding dynamic, smoothness, and tracking accuracy constraints to ensure path feasibility and trackability. Simulation results demonstrate an average planning time of 0.86 ms, outperforming NAVIGATION, Informed RRT*, MPC Planner, and ESDF Optimization by 29.6–52.0%, with a 100% obstacle avoidance success rate and trajectory tracking RMSE of 0.28 m over a 350 m flight distance, along with strong parameter and noise robustness. Actual flight tests on a 9.4 kg quadrotor UAV confirm the algorithm’s effectiveness in map construction, path planning, and obstacle avoidance in environments with 15 obstacles, while maintaining computational overhead suitable for onboard deployment. These results establish the proposed framework as an effective solution for high-speed autonomous navigation of large-inertia UAVs in complex near-ground environments. Full article

The safe release of external stores from aircraft is a complex aerodynamic problem governed by strong interactions between the store and the carrier. During separation, the store is subjected to rapidly varying pressure fields, strong aerodynamic interference, and inertial effects that collectively determine the trajectory and stability of the body in the critical milliseconds following release. This study presents a numerical investigation of the separation of an external store from the high-wing configuration aircraft. Both gravitational and impulse-based release mechanisms are examined across multiple suspension stations and a wide range of flight conditions. Computational fluid dynamics (CFD) methods were employed using a density-based, compressible solver with SST k–ω turbulence modeling, combined with a fully coupled six-degree-of-freedom (6DOF) solver and dynamic mesh deformation techniques. The study considers a wide range of Mach numbers from 0.6 to 0.9 and angles-of-attack between −2° and 4°, and three different suspension stations located at the inner wing pylon, outer wing pylon, and fuselage centerline. These conditions strongly influence the aerodynamic environment around the store and therefore affect its initial motion after release and flight path. The impulse ejection forces used in the analysis come from experimental data and are applied through a user-defined function (UDF) at each time step, allowing the simulation to reproduce the ejection event as realistically as possible. Numerical results confirm that the flight paths of external store are highly non-symmetrical, requiring the employment of complex computational models for their successful resolution, and that they gravely depend on the operating conditions, carrier geometry as well as the suspension location. Full article

Despite the widespread deployment of quadrotor unmanned aerial vehicles (UAVs), ensuring their flight stability under asymmetric environmental disturbances, such as concurrent wind and rain, remains a significant challenge. To address the trajectory tracking problem under these severe conditions, this paper proposes a Composite Continuous Rapid Nonsingular Adaptive Terminal Sliding Mode (CCRNATSM) control strategy. First, a composite dynamic model is developed, integrating wind aerodynamics with rain impact characteristics to accurately simulate realistic flight environments. A High-Order Sliding Mode Observer (HOSMO) is then employed for the real-time, accurate estimation of these lumped disturbances. Subsequently, this observer is integrated with an adaptive control law to ensure rapid and precise system stabilization. Comparative simulations conducted under strong disturbance conditions demonstrate that the proposed method exhibits superior performance over existing strategies, reducing roll angle deviation by 75% and shortening the recovery time to 1.5 s. Ultimately, this control strategy significantly enhances the robustness and safety of quadrotor UAVs operating in harsh, asymmetric environments. Full article

In low-altitude complex environments, accurately reconstructing the three-dimensional (3D) flight trajectories of small unmanned aerial vehicles (UAV) without onboard positioning modules remains challenging. To address this issue, this paper proposes a multi-view ground camera-based UAV 3D trajectory detection method founded on an optical imaging geometric model. Multiple ground cameras are used to synchronously observe UAV flight, enabling stable 3D trajectory reconstruction without relying on onboard Global Navigation Satellite System (GNSS). At the two-dimensional (2D) observation level, a lightweight object detection model is employed for rapid UAV detection. Foreground segmentation is further introduced to extract accurate UAV contours, and geometric centroids are computed to obtain precise image plane coordinates. At the 3D reconstruction stage, camera extrinsic parameters are estimated using a back intersection method with ground control points, and the UAV spatial position in the world coordinate system is recovered via multi-view forward intersection. Field experiments demonstrate that the proposed method achieves stable 3D trajectory reconstruction in real urban environments, with a median error of 4.93 m and a mean error of 5.83 m. The mean errors along the X, Y, and Z axes are 2.28 m, 4.58 m, and 1.09 m, respectively, confirming its effectiveness for low-cost UAV trajectory monitoring. Full article

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications. Full article

To address the issues of safety risk analysis and conflict assessment for integrated flight of manned aircraft and fixed-wing unmanned aerial vehicles (UAVs) in low-altitude mixed-operation airspace, this study enhances the foundational Event model. By incorporating UAV characteristics such as geometric features and aerodynamic mechanisms, alongside design dimensions and onboard performance metrics, an improved collision risk model is developed—the Enhanced Event-Based Framework for Multidimensional Geometry and Quasi-Monte Carlo Analysis of Flight Performance (EMGF-M). This enhancement rectifies the limitations of the basic model regarding parameter coverage and scenario adaptability, thereby improving the reliability and validity of the computational results. Experimental results demonstrate that, in accordance with the target safety level for airspace conflicts set by the International Civil Aviation Organization (ICAO), the application of the improved Event collision model yields quantifiable assessments of safety risks and safe separation distances for integrated operations in low-altitude mixed-use airspace. Utilizing these computational results for integrated flight procedure design at a general airport in Southwest China, the study shows that the air traffic flow in the low-altitude mixed-operation airspace increased from 9.2 to 20.9 operations per hour. The practical significance of this method lies in its guidance for accurately assessing safety risks in mixed airspace operations and for determining quantifiable separation minima for integrated flight trajectory planning. Full article

Dragonflies exhibit remarkable flight stability in unsteady environments, largely due to aerodynamic interaction between their forewings and hindwings. This study investigates gust response in dragonfly-inspired micro-aerial vehicles (MAVs) from a system dynamics perspective, with emphasis on the aerodynamic role of tandem-wing interaction rather than control compensation. A six-degree-of-freedom (6DOF) rigid-body framework is developed and coupled with a quasi-steady aerodynamic model that includes explicit phase-dependent interaction between forewing and hindwing forces. Gusts are introduced as time-varying inflow perturbations, allowing physically consistent analysis of how disturbances propagate through aerodynamic loading into vehicle motion. Simulations are performed for representative flight conditions, including gliding, hovering, and gust-perturbed ascent. The results show bounded trajectory, velocity, and attitude responses under sustained gust excitation, even with conservative baseline control. Force and energy analyses indicate that wing–wake interaction redistributes aerodynamic loads in time and reduces peak force and moment fluctuations before they reach the rigid-body dynamics. This behavior is interpreted as passive aerodynamic filtering of gust disturbances inherent to the tandem-wing configuration. Comparative simulations using backstepping control and Active Disturbance Rejection Control (ADRC) further show that the dominant gust attenuation arises from aerodynamic configuration rather than from control action. Although the aerodynamic model is quasi-steady, the framework reproduces key trends reported in biological and CFD-based studies and provides a numerical foundation for future wind-tunnel and free-flight experiments on configuration-level gust attenuation. Full article

Aircraft noise monitoring systems deployed at major airports typically rely on scalar energy-based indicators, which primarily describe integrated sound energy but provide limited representation of the spectral–temporal structure and perceptual attributes of aircraft noise. To address this limitation, this study proposes a sensor-based psychoacoustic feature extraction and spatiotemporal analysis framework for continuous aircraft noise monitoring under high-density operational conditions. An automatic noise monitoring system compliant with ISO 20906 was deployed to synchronously acquire acoustic waveforms and ADS-B trajectory data. A cascaded spatiotemporal fusion algorithm was developed to associate noise events with aircraft flight paths, followed by a model-stratified multidimensional IQR-based data cleaning strategy to suppress environmental interference and non-stationary outliers. Based on the cleaned dataset, a suite of psychoacoustic features—including loudness, sharpness, roughness, fluctuation strength, and tonality—was extracted to characterize the perceptual structure of aircraft noise beyond conventional energy metrics. Experimental results demonstrate that, under equivalent sound exposure levels, psychoacoustic features retain substantial discriminative information that is lost in scalar energy indicators. The coefficients of variation for fluctuation strength and tonality reach 43.2% and 22.1%, respectively, corresponding to 15–69 times higher sensitivity compared to traditional energy-based metrics. Furthermore, nonlinear manifold mapping using UMAP reveals clear topological separation between new-generation and legacy aircraft models in the psychoacoustic feature space, whereas severe overlap persists in energy-based representations. Correlation analysis further indicates decoupling between macro-level physical design parameters (e.g., bypass ratio, thrust) and perceptual feature dimensions, highlighting the limitations of energy-centric monitoring schemes. The proposed framework demonstrates the feasibility of integrating psychoacoustic feature extraction into continuous sensor-based aircraft noise monitoring systems. It provides a scalable signal processing pipeline for enhancing the resolution and interpretability of aircraft noise measurements in complex operational environments. Full article

Structural health monitoring (SHM) technology enables the monitoring and assessment of the condition of various materials and structures. Lamb-guided waves (LW) are widely used to detect damage in large-scale plate structures. One of the parameters used for these purposes is the time-of-flight (ToF) of ultrasonic LW signals. In the presented feasibility study, the ToF was determined based on the idea that the zero-crossings of this signal, filtered by several filters, are concentrated around the maximum of the signal envelope. This ToF detection method, unlike threshold- and peak-based methods, avoids uncertainties in signal and noise levels and does not require a signal detection threshold. Compared to the correlation method, no reference signal is required. It has been established that the curves of signal propagation times with varying distance depend on the group and phase velocities of signal propagation and have phase jumps. The proposed methodology for assessing plate inhomogeneities involves comparing signal propagation time curves with and without damage. This methodology has been verified both through theoretical modeling and experimental research. The experimental studies used a 6 mm thick steel specimen with artificial defects of various diameters (10–35 mm). The A₀ mode of Lamb waves with a central frequency of 150 kHz was excited in the steel plate. For experimentally obtained B-scans, the ToF distributions of signals along the scan trajectories were calculated. By comparing the defective and defect-free ToF curves, critical points of the experimental curves were determined, which were used to estimate the dimensions of the defects. Both in the case of theoretical modeling and in the result of experimental measurements, it was determined that the proposed methodology can be used to determine the inhomogeneities of plates. Full article

High−Altitude wind is a critical factor affecting the recovery safety of reusable rockets, significantly altering aerodynamic loads, flight attitudes, and trajectories—especially during the aerodynamic deceleration phase (engine shutdown) of reentry, posing severe challenges to high-precision guidance and stable control. Currently, accurate advance prediction of landing site wind fields is difficult with poor real-time performance, necessitating a real-time estimation and prediction method independent of additional measurement equipment. This study addresses this gap by proposing a deep learning-based approach for wind field estimation and prediction, using directly measurable attitude angles and apparent acceleration deviations of the rocket as inputs to train a dedicated deep neural network. Furthermore, to solve the attitude control problem of Reusable Launch Vehicles (RLVs) during recovery, a non-recursive simplified high-order sliding mode control method with online wind disturbance compensation is designed to achieve finite-time convergence. First, a dynamic model for the attitude control of RLVs during recovery is established; second, based on homogeneity theory, a non-recursive simplified homogeneous high-order sliding mode controller is developed to realize finite-time tracking control during RLV recovery with uncertainties, effectively suppressing the chattering inherent in sliding mode control; finally, simulation results verify the effectiveness and engineering feasibility of the proposed method. The combined approach significantly reduces wind-induced disturbance torque and required control torque, enhancing the adaptability and control robustness of vertically recoverable rockets to wind fields. Full article

Maneuver pattern classification is fundamental for understanding and predicting the dynamic behaviors of aerial vehicles operating in increasingly complex airspace environments. However, existing rule-based and data-driven approaches are constrained by the scarcity, imbalance, and limited maneuver diversity of real-world flight data, leading to a restricted generalization capability and a reduced robustness to noise. To address these challenges, we construct a standardized Maneuver Pattern Library, a curated dataset of simulated flight trajectories encompassing five representative maneuver primitives: climb, descent, left turn, right turn, and loiter. Trajectories are generated using the X-Plane 12 flight simulator under controlled conditions to ensure maneuver diversity and label consistency, refined through noise reduction and cubic spline interpolation, and rendered from synchronized top and side views with time-encoded color gradients to preserve temporal continuity. Building upon this dataset, we propose DualView-LiteNet, a lightweight Siamese convolutional network designed to jointly learn complementary spatial and temporal cues from dual-view trajectory representations through parameter sharing and feature fusion. In addition to comprehensive comparisons with multiple baseline models on the simulated benchmark, we further evaluate the trained model via direct inference on a real-world ADS-B dataset collected from ADS-B Exchange, without any fine-tuning. The consistent performance observed in this sim-to-real setting demonstrates the practical feasibility and generalization capability of the proposed approach. The experimental results show that DualView-LiteNet achieves an accuracy of 97.64%, with its precision, recall, and F1-score all reaching 0.98 on the benchmark dataset, validating its effectiveness for aerial maneuver pattern classification and establishing a reliable reference for future research. Full article

Accurate prediction of air traffic noise is critical for advancing environmentally sustainable operations in high density terminal areas. Conventional noise prediction models often exhibit significant limitations due to discrepancies between actual and nominal flight trajectories. To overcome this challenge, this study introduces a probabilistic framework that integrates real air-traffic-flow data to generate realistic flight trajectory distributions. The proposed methodology extracts key operational features—including trajectory distribution probabilities, and essential trajectory operation features—within a machine learning architecture. Furthermore, we develop a dedicated air traffic noise prediction model for clustered flight paths that explicitly incorporates traffic flow patterns, enabling high-fidelity simulation of noise propagation under actual air traffic operation. The framework is validated using a QAR (Quick Access Recorder) dataset from the terminal area of Changsha Huanghua International Airport. Experimental results demonstrate the model’s high predictive accuracy for both air traffic noise distribution and its influence, coupled with computational efficiency and practical applicability. The findings indicate that the proposed approach successfully addresses the challenge of predicting air traffic noise from divergent, real-world flight trajectories, offering a robust method for supporting noise-abatement strategies and sustainable aviation-planning initiatives. Full article

This paper presents a sensitivity analysis of positioning accuracy in a localization system based on signal time-of-flight measurements, intended for operation in underground roadway workings. The underground environment is characterized by limited installation space, numerous obstacles causing multipath propagation, and the presence of sections with non-uniform geometry, which in practice leads to a “flattening” of the transmitter constellation and a deterioration of the conditioning of the trilateration problem. As a result, even small changes in input parameters (e.g., related to infrastructure geometry, distance-measurement quality, or the adopted model) may cause a significant change in the position-estimation error, thereby reducing the reliability of roadheader localization across the entire working area. In this study, a local sensitivity analysis is employed to identify the parameters that dominate the positioning outcome. Sensitivity coefficients are defined in a normalized form and are determined numerically using a perturbation approach (changing a given input parameter by a prescribed percentage), which avoids analytical differentiation of the complex relationships arising from the trilateration equations. The analysis is performed for a roadway scenario supported by an ŁP10 steel arch yielding support, with transmitters installed under the support arch and the roadheader trajectory represented by a sequence of consecutive position vectors. The obtained results allow the solution’s susceptibility to errors and uncertainties in the parameters to be assessed and indicate which parameters require priority control in practical implementation. On this basis, recommendations are formulated for the design and maintenance of the localization infrastructure, including transmitter placement and reconfiguration rules (relocation or adding an additional transmitter), to maintain stable positioning quality under operational mining conditions. 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

Clarifying migration pathways and the source area–destination relationships of the domestic and foreign beet webworm Loxostege sticticalis (Linnaeus) populations, as well as understanding the meteorological mechanisms shaping these processes, is pivotal for remote, accurate, and location-specific pest early warning and forecasting. Based on light trap data from northern China and field survey data from Mongolia in 2022, we simulated the migration trajectories, source regions, and primary landing areas of L. sticticalis by using the HYSPLIT model and analyzed the synoptic systems, processes and conditions during its migration. The results indicate the frequent exchange of L. sticticalis populations between China and Mongolia in 2022. The L. sticticalis migrants initiating their flights from Mongolia primarily undertook a southeastward migration pathway, supplemented by eastward ‘cyclonic’ and southwestward paths. The main landing areas were located in North China and Northeast China, with migration events potentially extending to the Shandong, Heilongjiang, and Xinjiang provinces. Populations originating from North China exhibited a capacity for migrating into Northeast China and Mongolia through 1–5 consecutive nights of flight. During this period, the Northeast China Cold Vortex (NCCV) and the Mongolian Cyclone alternately regulated the synoptic circulation pattern governing the migration of L. sticticalis. The spatiotemporal distributions and intensities of these systems were key determinants of the transboundary migration routes and distances of L. sticticalis. The NCCV dominated, and the precipitation and downdrafts it induced were crucial for the massive landing of L. sticticalis in northern China. Full article