Search Results (131)

In recent years, the large-scale deployment of Low Earth Orbit (LEO) constellations has made autonomous time synchronization and reference maintenance within constellations a critical enabling technology. Achieving high-precision synchronization with low cost and low power consumption, without relying on onboard atomic clocks or Global Navigation Satellite System (GNSS) signals, remains a significant challenge. This paper proposes an autonomous time synchronization method for LEO constellations that relies solely on high-stability crystal oscillators as local oscillators. By leveraging satellite-to-ground and inter-satellite measurement links, the proposed approach enables constellation-wide time synchronization without external timing references. A satellite-to-ground link visibility time model is established based on orbital parameters and ground station visibility geometry. On this basis, a discrete state-space model is constructed, incorporating temperature-induced frequency perturbation compensation, frequency offset estimation, and control voltage regulation. A combined Kalman filtering and Linear Quadratic Regulator (LQR) control framework is employed to achieve precise time offset synchronization and long-term maintenance. Experimental results demonstrate that, under a Walker-Delta constellation configuration with an orbital altitude of 800 km and an inclination of 55°, the proposed method introduces a time synchronization performance better than 5 ns (1$\sigma$), with a peak-to-peak error below 30 ns. This level of performance satisfies the timing requirements of typical LEO constellation applications, including communication scheduling, high-rate modulation, and critical infrastructure timing services. Moreover, the proposed scheme supports decentralized deployment and provides local physical time signal outputs, making it well suited for large-scale satellite networks requiring high-precision autonomous time synchronization. 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

Unmanned aerial vehicle (UAV) imagery poses persistent challenges for object detection, including dense small objects, large-scale variation, cluttered backgrounds, and stringent localization requirements, where conventional two-stage detectors often fall short in fine-grained small-object representation, efficient global context modeling, and classification–localization consistency. We specifically target low-altitude UAV-captured imagery with highly flexible viewpoints (near-nadir to oblique) and frequent platform-induced motion blur, which makes dense small-object localization substantially more challenging than in conventional remote-sensing imagery. To address these issues, we propose CASA-RCNN, a context-adaptive and scale-aware two-stage detection framework tailored to UAV scenarios. CASA-RCNN introduces a shallow-level enhancement module, ConvSwinMerge, which strengthens position-sensitive cues and suppresses background interference by combining coordinate attention with channel excitation, thereby improving discriminative high-resolution features for small objects. For deeper semantic features, we incorporate an adaptive sequence modeling module based on MambaBlock to capture long-range dependencies and support context reasoning in crowded or occluded scenes with practical computational overheadon a desktop GPU. In addition, we adopt Varifocal Loss for quality-aware classification to better align confidence scores with localization quality, and we design a ScaleAdaptiveLoss to dynamically reweight regression objectives across object scales, compensating for the reduced gradient contribution of small targets during training. Experiments on the VisDrone2021 validation benchmark show that CASA-RCNN achieves 22.9% mAP, improving Faster R-CNN by 9.0 points; it also reaches 36.6% mAP₅₀ and 25.7% mAP₇₅. Notably, performance on small objects improves to 12.5% mAP_s (from 6.9%), and ablation studies confirm the effectiveness and complementarity of the proposed components. Full article

Online visual object tracking is a critical component of remote sensing-based aerial vehicle physical tracking, enabling applications such as environmental monitoring, target surveillance, and disaster response. In real-world remote sensing scenarios, the inherent processing delay of tracking algorithms results in the tracker’s output lagging behind the actual state of the observed scene. This latency not only degrades the accuracy of visual tracking in dynamic remote sensing environments but also impairs the reliability of UAV physical tracking control systems. Although predictive trackers have shown promise in mitigating latency impacts by forecasting target future states, existing methods face two key challenges in remote sensing applications: weak correlation between trackers and predictors, where predictions rely solely on motion information without leveraging rich remote sensing visual features; and inadequate modeling of continuous historical memory from discrete remote sensing data, limiting adaptability to complex spatiotemporal changes. To address these issues, we propose TPC-Tracker, a Tracker-Predictor Correlation Framework tailored for latency compensation in remote sensing-based aerial tracking. A Visual Motion Decoder (VMD) is designed to fuse high-dimensional visual features from remote sensing imagery with motion information, strengthening the tracker-predictor connection. Additionally, the Visual Memory Module (VMM) and Motion Memory Module (M3) model discrete historical remote sensing data into continuous spatiotemporal memory, enhancing predictive robustness. Compared with state-of-the-art predictive trackers, TPC-Tracker reduces the Mean Squared Error (MSE) by up to 38.95% in remote sensing-oriented physical tracking simulations. Deployed on VTOL drones, it achieves stable tracking of remote sensing targets at 80 m altitude and 20 m/s speed. Extensive experiments on public UAV remote sensing datasets and real-world remote sensing tasks validate the framework’s superiority in handling latency-induced challenges in aerial remote sensing scenarios. Full article

This study presents the design and implementation of a real-time flight-path tracking control system for a quadrotor unmanned aerial vehicle (UAV) capable of accurately following a mobile ground target under dynamic and uncertain environmental conditions. The proposed framework integrates classical fixed-gain PID regulation executed on Pixhawk with its built-in adaptive mechanisms, namely autotuning, hover-throttle learning, and dynamic harmonic notch filtering, to enhance robustness under communication latency and disturbances. No machine learning PID tuning is used on Pixhawk; adaptive features are estimator based rather than ML based. The proposed system addresses critical challenges in trajectory tracking, including real-time delay compensation between the UAV and rover, external perturbations, and the requirement to maintain stable six-degree-of-freedom (DOF) control of altitude, yaw, pitch, and roll. A dynamic mathematical model, formulated using ordinary differential equations with embedded delay elements, is developed to emulate real-world flight behavior and validate control performance. Experimental evaluation demonstrates robust path-tracking accuracy, attitude stability, and responsiveness across diverse terrains and weather conditions, achieving a mean positional error below one meter and effective resilience against an 8.2 ms communication delay. Overall, this work establishes a scalable, computationally efficient, and high-precision control framework for UAV guidance and cooperative ground-target tracking, with potential applications in autonomous navigation, search-and-rescue operations, infrastructure inspection, and intelligent surveillance. The term “delay-aware” in this work refers to the explicit modeling of the measured 8.2 ms end-to-end delay and the use of Pixhawk’s estimator-based adaptive mechanisms, without any machine learning-based PID tuning. Full article

The autonomous operation of unmanned gyroplanes is constrained by the limited fidelity of aerodynamic models and control challenges posed by unique flight characteristics. To address these issues, a comprehensive methodology for unmanned gyroplane modeling and autonomous flight control is proposed. High-fidelity aerodynamic models were developed through a modified parameter identification structure, and the longitudinal and lateral modal characteristics of the prototype gyroplane were subsequently analyzed. Targeting the control coupling, delayed pitch response, and throttle-airspeed nonlinearities, a novel autonomous flight control strategy is proposed for unmanned gyroplanes. Precise energy management and longitudinal-lateral decoupling were achieved through feedforward trim compensation, pitch-damping augmentation, and coordinated allocation of throttle and rotor tilt. Comparative analysis verified the high accuracy of the identified aerodynamic models, with the coefficient of determination between measured and simulated attitude responses exceeding 0.92. Furthermore, flight tests were conducted on an unmanned gyroplane prototype, including climb and descent maneuvers, climb to level flight transitions, and turning trajectory tracking. The results show that the proposed autonomous control strategy achieves precise tracking of altitude, airspeed, and trajectory, with airspeed errors remaining within 1.5 m/s. Full article

Unmanned Aerial Vehicles (UAVs) are integral components of future 6G networks, offering rapid deployment, enhanced line-of-sight communication, and flexible coverage extension. However, UAV communications in low-altitude environments face significant challenges, including rapid link variations due to attitude instability, severe signal blockage by urban obstacles, and critical sensitivity to transmitter–receiver alignment. While traditional planar reconfigurable intelligent surfaces (RIS) show promise for mitigating these issues, they exhibit inherent limitations such as angular sensitivity and beam squint in wideband scenarios, compromising reliability in dynamic UAV scenarios. To address these shortcomings, this paper proposes and evaluates a spherical-cap reflective intelligent surface (ScRIS) specifically designed for dynamic low-altitude communications. The intrinsic curvature of the ScRIS enables omnidirectional reflection capabilities, significantly reducing sensitivity to UAV attitude variations. A rigorous analytical model founded on Generalized Sheet Transition Conditions (GSTCs) is developed to characterize the electromagnetic scattering of the curved metasurface. Three distinct 1-bit RIS unit cell coding arrangements, namely alternate, chessboard, and random, are investigated via numerical simulations utilizing CST Microwave Studio and experimental validation within a mechanically stirred reverberation chamber. Our results demonstrate that all tested ScRIS coding patterns markedly enhance electromagnetic field uniformity within the chamber and reduce the lowest usable frequency (LUF) by approximately 20% compared to a conventional metallic spherical reflector. Notably, the random coding pattern maximizes phase entropy, achieves the most uniform scattering characteristics and substantially reduces spatial field autocorrelation. Furthermore, the combined curvature and coding functionality of the ScRIS facilitates simultaneous directional focusing and diffuse scattering, thereby improving multipath diversity and spatial coverage uniformity. This effectively mitigates communication blind spots commonly encountered in UAV applications, providing a resilient link environment despite UAV orientation changes. To validate these findings in a practical context, we conduct link-level simulations based on a reproducible system model at 3.5 GHz, utilizing electromagnetic scale invariance to bridge the fundamental scattering properties observed in the RC to the application band. The results confirm that the ScRIS architecture can enhance link throughput by nearly five-fold at a 10 km range compared to a baseline scenario without RIS. We also propose a practical deployment strategy for urban blind-spot compensation, discuss hybrid planar-curved architectures, and conduct an in-depth analysis of a DRL-based adaptive control framework with explicit convergence and complexity analysis. Our findings validate the significant potential of ScRIS as a passive, energy-efficient solution for enhancing communication stability and coverage in multi-band 6G networks. Full article

This paper investigates bearing-based formation control of multiple unmanned aerial vehicles (UAVs) flying in low-altitude wind fields. In such environments, time-varying wind disturbances can distort the formation geometry, enlarge bearing errors, and even induce potential collisions among neighboring UAVs, yet they also contain components that can be beneficial for the formation motion. Conventional disturbance compensation methods treat wind as a purely harmful factor and aim to reject it completely, which may sacrifice responsiveness and energy efficiency. To address this issue, we propose a pure bearing-based formation control framework with Conditional Disturbance Utilization (CDU). First, a real-time disturbance observer is designed to estimate the wind-induced disturbances in both translational and rotational channels. Then, based on the estimated disturbances and the bearing-dependent potential function, CDU indicators are constructed to judge whether the current disturbance component is beneficial or detrimental with respect to the formation control objective. These indicators are embedded into the bearing-based formation controller so that favorable wind components are exploited to accelerate formation convergence, whereas adverse components are compensated. Using an angle-rigid formation topology and a Lyapunov-based analysis, we prove that the proposed CDU-based controller guarantees global asymptotic stability of the desired formation. Simulation results on triangular and hexagonal formations under complex wind disturbances show that the proposed method achieves faster convergence and improved responsiveness compared with traditional disturbance observer-based control, while preserving formation stability and safety. Full article

The accurate execution of aeroengine flight environment simulation tests relies on the electro-hydraulic servo valve control system in the altitude test facility. However, time delays arising from various factors, such as friction or sensor latency, impose significant constraints on system responsiveness and control precision. To address this challenge, an enhanced active disturbance rejection control has been developed. The proposed method employs an improved output prediction constructed by tracking differentiator to mitigate delay effects, introduces the Taylor compensator to more accurately capture future signal trends, and incorporates a dynamic adjustment mechanism based on error variation to optimize the parameters of the extended state observer in real time, thereby enhancing robustness under varying operating conditions. The simulation results demonstrate that under fixed-delay conditions, the proposed algorithm exhibits fast response characteristics; under varying-delay conditions, unlike model-dependent approaches, it remains less affected by delay fluctuations and maintains superior response speed and stability, thereby ensuring the accuracy of flight environment simulation tests. Full article

As global climate change intensifies, hydrological processes in arid inland river basins are undergoing profound transformations, posing severe challenges to regional water security and ecological stability. This study aims to develop a coupled SWAT-LSTM model integrating glacier melt processes to simulate runoff dynamics in the Keria River basin under climate change, providing a basis for local water resource management. Based on natural monthly runoff observations from the Langgan hydrological station (1961–2015), glacier data extracted from Landsat 8 remote sensing imagery (2013–2019), and downscaled data from the CMIP6 Multi-Model Ensemble (MME), this study constructed a SWAT-LSTM coupled model to simulate future scenarios (2026–2100). Research indicates that this hybrid model significantly enhances the accuracy of hydrological simulations in high-altitude glacier-fed catchments. The Nash efficiency coefficient (NSE) during the validation period reached 0.847, representing a 15% improvement over the SWAT model. SSP5-8.5 is identified as a high-risk scenario, underscoring the urgency of emissions reduction; SSP1-2.6 represents the most desirable pathway, with its relatively stable pattern offering sustained advantages for long-term water resource management in the basin. The study further reveals a negative feedback mechanism between glacier ablation and runoff increase, validating the regulatory role of Jiyin Reservoir’s “store during floods to compensate for droughts” operation strategy in balancing basin water resources. This study explores the coupling path between the physical model and the deep learning model, and provides an effective integration scheme for the hydrological simulation of the global watershed with ice–snow meltwater as the main recharge runoff, especially for the adaptive management of water resources in inland river basins in arid areas. Full article

This paper focuses on the issue of unmodeled dynamics and large-range parametric uncertainties in air-breathing hypersonic vehicles (AHV), proposing an adaptive dynamic surface control method based on radial basis function (RBF) neural networks. First, the hypersonic longitudinal model is transformed into a strict-feedback control system with model uncertainties. Then, based on backstepping control theory, adaptive dynamic surface controllers incorporating RBF neural networks are designed separately for the velocity and altitude channels. The proposed controller achieves three key functions: (1) preventing “differential explosion” through low-pass filter design; (2) approximating uncertain model components and unmodeled dynamics using RBF neural networks; (3) enabling real-time adjustment of controller parameters via adaptive methods to accomplish online estimation and compensation of system uncertainties. Finally, stability analysis proves that all closed-loop system signals are semi-globally uniformly bounded (SGUB), with tracking errors converging to an arbitrarily small residual set. The simulation results indicate that the proposed control method reduces steady-state error by approximately 20% compared to traditional controllers. Full article

A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article

Due to the complexity of the marine environment and the uncertainty of ship movements, altitude control of UAV is particularly important when approaching and landing on the deck of a ship. This paper focuses on unmanned helicopters as its research subject. Conventional altitude control systems may have difficulty in ensuring fast and stable landings under certain extreme conditions. Therefore, designing a new UAV altitude control method that can adapt to complex sea conditions has become a current problem to be solved. Designing a reinforcement learning based rotational speed compensator for UAV as a redundant controller to optimise UAV altitude control performance for the above problem. The compensator is capable of adjusting the UAV’s rotational speed in real time to compensate for altitude deviations due to external environmental disturbances and the UAV’s own dynamic characteristics. By introducing reinforcement learning algorithms, especially the DDPG algorithm, this compensator is able to learn the optimal RPM adjustment strategy in a continuous trial-and-error process, which improves the UAV’s rapidity and stability during the landing process. Full article

As the Pamir Plateau is known as the “Water Tower of Central Asia”, accurate precipitation dataset is essential for the study of climate and hydrology in this region. Based on the monthly precipitation observations from 268 meteorological stations in the Eastern Pamir Plateau (EPP) during the April-to-September warm season of 2010–2024, this paper comprehensively evaluates the applicability of eight multi-source precipitation datasets in complex terrains by using statistical indicators, constructs a skill-weighted ensemble mean dataset (Skill-Ens), and analyzes the elevation-dependent characteristics of precipitation in the EPP. The research findings are as follows: (1) The warm-season precipitation in the EPP shows a significant elevation-dependent feature, with the maximum precipitation altitude (MPA) in the range of 2400–2800 m. Precipitation is reduced above this elevation range, but a second MPA may appear in the glacier area above 4000 m. (2) Among the studied eight datasets, the first-generation Chinese Global Land-surface Reanalysis (CRA40/Land) performs the best overall. A long-term (1979–2020) high-resolution (1/30°) precipitation dataset for the Third Pole region (TPHiPr) can most accurately capture the elevation-dependent characteristics of precipitation, while the satellite datasets are relatively poor in this respect. (3) The skill-weighted ensemble mean dataset (Skill-Ens) constructed in this study can significantly improve precipitation estimation (DISO = 0.35), especially in the MPA region, and can accurately depict the elevation-dependent characteristics of precipitation as well (CC = 0.92). In a word, this paper provides the applicable options for precipitation data in complex terrain areas. With the Skill-Ens, the limitation of the individual dataset has been compensated for, which is of significant application value in improving the accuracy of hydrological simulations in high-elevation mountainous areas. Full article

Current simulation training for pilots wearing night vision goggles (NVGs) (e.g., night landings and tactical reconnaissance) faces fidelity limitations from conventional displays. This study proposed a novel dynamic NIR-LED direct-view display system for authentic nighttime scene simulation. Through comparative characterization of NVG response across LED wavelengths under ultra-low-current conditions, 940 nm was identified as the optimal wavelength. Quantification of inherent nonlinear response in NVG observation enabled derivation of a mathematical model that provides the foundation for inverse gamma correction compensation. A prototype NIR-LED display was engineered with 1.25 mm pixel pitch and 1280 × 1024 resolution at 60 Hz refresh rate, achieving >90% uniformity and >2000:1 contrast. Subjective evaluations confirmed exceptional simulation fidelity. This system enables high-contrast, low-power NVG simulation for both full-flight simulators and urban low-altitude reconnaissance training systems, providing the first quantified analysis of NVG-LED nonlinear interactions and establishing the technical foundation for next-generation LED-based all-weather visual displays. Full article