Search Results (2,929)

We present a CFD-driven surrogate modeling framework that integrates a Convolutional Autoencoder (CAE) with a Deep Neural Network (DNN) for the rapid prediction of urban wind environments and their subsequent use in UAV trajectory planning. A Reynolds-Averaged Navier–Stokes (RANS) CFD database is generated, parameterized by boundary-condition descriptors, to train the surrogate for velocity magnitude and turbulent kinetic energy (TKE). The CAE compresses horizontal flow fields into a low-dimensional latent space, providing an efficient representation of complex flow structures. The DNN establishes a mapping from input descriptors to the latent space, and flow reconstructions are obtained through the frozen decoder. Validation against CFD demonstrates that the surrogate captures velocity gradients and TKE distributions with mean absolute errors below 1% in most of the domain, while residual discrepancies remain confined to near-wall regions. The approach yields a computational speed-up of approximately 4000× relative to CFD, enabling deployment on embedded or edge hardware. For path planning, the domain is discretized as a k-Non-Aligned Nearest Neighbors (k-NANN) graph, and an A* search algorithm incorporates heading constraints and surrogate-based TKE thresholds. The integrated pipeline produces turbulence-aware, dynamically feasible trajectories, advancing the integration of high-fidelity flow predictions into urban air mobility decision frameworks. Full article

Heterogeneous unmanned aerial vehicle (UAV) swarms are becoming critical components of next-generation non-terrestrial networks, enabling tasks such as communication relay, spectrum monitoring, cooperative sensing, and navigation. Yet, their heterogeneity and multifunctionality bring severe challenges in task allocation and resource scheduling, where traditional multi-agent reinforcement learning methods often suffer from high algorithmic complexity, lengthy training times, and deployment difficulties on resource-constrained nodes. To address these issues, this paper proposes a low-complexity multi-agent soft actor–critic (MASAC) framework that combines parameter sharing (shared actor with device embeddings and shared-backbone twin critics), lightweight network design (fixed-width residual MLP with normalization), and robust training mechanisms (minimum-bias twin-critic updates and entropy scheduling) within the CTDE paradigm. Simulation results show that the proposed framework achieves more than 14-fold parameter compression and over a 93% reduction in training time, while maintaining or improving performance in terms of the delay–energy utility function. These advances substantially reduce computational overhead and accelerate convergence, providing a practical pathway for deploying multi-agent reinforcement learning in large-scale heterogeneous UAV clusters and supporting diverse mission scenarios under stringent resource and latency constraints. Full article

In disaster relief operations, Unmanned Aerial Vehicles (UAVs) equipped with base stations (UAV-BS) are vital for re-establishing communication networks where conventional infrastructure has been compromised. Optimizing their trajectories and coverage to ensure equitable service delivery amidst obstacles, wind effects, and energy limitations remains a formidable challenge. This paper proposes an innovative reinforcement learning framework leveraging a Long Short-Term Memory (LSTM)-based Advantage Actor–Critic (A2C) model enhanced with an attention mechanism. Operating within a grid-based disaster environment, our approach seeks to maximize fair coverage for randomly distributed ground users under tight energy constraints. It incorporates a nine-direction movement model and a fairness-focused communication strategy that prioritizes unserved users, thereby improving both equity and efficiency. The attention mechanism enhances adaptability by directing focus to critical areas, such as clusters of unserved users. Simulation results reveal that our method surpasses baseline reinforcement learning techniques in coverage fairness, Quality of Service (QoS), and energy efficiency, providing a scalable and effective solution for real-time disaster response. Full article

Polarimetric radar offers strong potential for UAV detection, but time-varying polarization induced by rotor rotation leads to unstable echoes, degrading feature consistency and recognition accuracy. This paper proposes a unified framework that combines rotor phase compensation, adaptive polarization filtering, and a multi-branch polarization aware fusion network (MPAF-Net) to enhance micro-Doppler features. The compensation scheme improves harmonic visibility through rotation-angle-based phase alignment and polarization optimization, while MPAF-Net exploits complementary information across polarimetric channels for robust classification. The framework is validated on both simulated and measured UAV radar data under varying SNR conditions. Results show an average harmonic SNR gain of approximately 1.2 dB and substantial improvements in recognition accuracy: at 0 dB, the proposed method achieves 66.7% accuracy, about 10% higher than Pauli and Sinclair decompositions, and at 20 dB, it reaches 97.2%. These findings confirm the effectiveness of the proposed approach for UAV identification in challenging radar environments. Full article

UAV object detection is still difficult to achieve due to large-scale variation, dense small objects, a complicated background, and resource constraints from onboard computing. To solve these problems, we develop a diffusion-enhanced detection network, DL-DEIM, tailored for aerial images. The proposed scheme generalizes the DEIM baseline across three orthogonal axes. First, we propose a lightweight backbone network called DCFNet, which utilizes a DRFD module and a FasterC3k2 module to maintain spatial information and reduce computational complexity. Second, we propose a LFDPN module, which can conduct bidirectional multi-scale fusion via frequency-spatial self-attention and deep feature refinement and largely enhance cross-scale contextual propagation for small objects. Third, we propose LAWDown, an adaptive-content-aware downsampling to preserve the discriminative representation with higher accuracy at lower resolutions, which can effectively capture the spatially-variant weights and group channel interactions. On the VisDrone2019 dataset, DL-DEIM achieves a mAP@0.5 of 34.9% and a mAP@0.5:0.95 of 20.0%, outperforming the DEIM baseline by +4.6% and +2.9%, respectively. The model maintains real-time inference speed (356 FPS) with only 4.64 M parameters and 11.73 GFLOPs. Ablation studies validate the fact that DCFNet, LFDPN, and LAWDown collaboratively contribute to the accuracy and efficiency. Visualizations also display clustered and better localized activation in crowded scenes. These results show that DL-DEIM achieves a good tradeoff between detection probability and computation burden and it can be used in practice on resource-limited UAV systems. Full article

The pine wood nematode (PWN), one of the globally significant forest diseases, has driven the demand for precise detection methods. Recent advances in satellite remote sensing technology, particularly ultra-high-resolution optical imagery, have opened new avenues for identifying PWN-infected trees. In order to systematically evaluate the ability of ultra-high-resolution optical remote sensing and the influence of spatial and spectral resolution in detecting PWN-infected trees, this study utilized a U-Net network model to identify PWN-infected trees using three remote sensing datasets of the ultra-high-resolution multispectral imagery from Beijing 3 International Cooperative Remote Sensing Satellite (BJ3N), with a panchromatic band spatial resolution of 0.3 m and six multispectral bands at 1.2 m; the high-resolution multispectral imagery from the Beijing 3A satellite (BJ3A), with a panchromatic band resolution of 0.5 m and four multispectral bands at 2 m; and unmanned aerial vehicle (UAV) imagery with five multispectral bands at 0.07 m. Comparison of the identification results demonstrated that (1) UAV multispectral imagery with 0.07 m spatial resolution achieved the highest accuracy, with an F1 score of 89.1%. Next is the fused ultra-high-resolution BJ3N satellite imagery at 0.3 m, with an F1 score of 88.9%. In contrast, BJ3A imagery with a raw spatial resolution of 2 m performed poorly, with an F1 score of only 28%. These results underscore that finer spatial resolution in remote sensing imagery directly enhances the ability to detect subtle canopy changes indicative of PWN infestation. (2) For UAV, BJ3N, and BJ3A imagery, the identification accuracy for PWN-infected trees showed no significant differences across various band combinations at equivalent spatial resolutions. This indicates that spectral resolution plays a secondary role to spatial resolution in detecting PWN-infected trees using ultra-high-resolution optical imagery. (3) The 0.3 m BJ3N satellite imagery exhibits low false-detection and omission rates, with F1 scores comparable to higher-resolution UAV imagery. This indicates that a spatial resolution of 0.3 m is sufficient for identifying PWN-infected trees and is approaching a point of saturation in a subtropical mountain monsoon climate zone. In conclusion, ultra-high-resolution satellite remote sensing, characterized by frequent data revisit cycles, broad spatial coverage, and balanced spatial-spectral performance, provides an optimal remote sensing data source for identifying PWN-infected trees. As such, it is poised to become a cornerstone of future research and practical applications in detecting and managing PWN infestations globally. Full article

In geophysics, aeromagnetic surveying based on unmanned aerial vehicles (UAV) is a widely employed exploration technique, that can analyze underground structures by conducting data acquisition, processing, and inversion. This method is highly efficient and covers large areas, making it widely applicable in mineral exploration, oil and gas surveys, geological mapping, and engineering and environmental studies. However, during flight, interference from the aircraft’s engine, electronic systems, and metal structures introduces noise into the magnetic data. To ensure accuracy, mathematical models and calibration techniques are employed to eliminate these aircraft-induced magnetic interferences. This enhances measurement precision, ensuring the data faithfully reflect the magnetic characteristics of subsurface geological features. This study focuses on aeromagnetic data processing methods, conducting numerical simulations of magnetic interference for aeromagnetic surveys of UAVs with the Tolles–Lawson (T-L) model. Recognizing the temporal dependencies in aeromagnetic data, we propose a Transformer neural network algorithm for aeromagnetic compensation. The method is applied to both simulated and measured flight data, and its performance is compared with the classical Multilayer Perceptron neural networks (MLP). The results demonstrate that the Transformer neural networks achieve better fitting capability and higher compensation accuracy. Full article

With the development of unmanned aerial vehicle (UAV) technology, multimachine collaborative operations have become the core model for increasing mission effectiveness. However, large-scale UAV clusters face challenges such as dynamic security threats, heterogeneous data fusion difficulties, and resource-constrained decision-making delays. Traditional single-machine intelligent architectures have limitations when addressing new threats, such as insufficient real-time response capabilities. To address these issues, this paper presnts an LLM-layered collaborative security architecture (LLM-LCSA) for multimachine collaborative security. This architecture optimizes the spatiotemporal fusion efficiency of multisource asynchronous data through cloud–edge–end collaborative deployment, combining an end lightweight LLM, an edge medium LLM, and a cloud-based foundation LLM. Additionally, a Mixture of Experts (MoEs) intelligent algorithm that dynamically activates the most relevant expert models by leveraging a threat–expert association matrix is introduced, thereby increasing the accuracy of complex threat identification and dynamic adaptability. Moreover, a resource-aware multi-objective optimization model is constructed to generate optimal decisions under resource constraints. Simulation results indicate that compared with traditional methods, LLM-LCSA achieves an average 7.92% improvement in the threat detection accuracy, reduces the system’s total response time by 44.52%, and enables resource scheduling during off-peak periods. This architecture provides an efficient, intelligent, and scalable solution for secure collaboration among UAV swarms. Future research should further explore its application potential in 6G network integration and large-scale swarm environments. Full article

The conventional OLSR protocol faces substantial challenges in highly dynamic and interference-intensive UAV environments, including high mobility, frequent topology changes, and insufficient adaptability to electromagnetic interference. This paper proposes a cross-layer improved OLSR protocol, OLSR-LCN, that integrates three evaluation metrics—link lifetime (LL), channel interference index (CII), and node load (NL)—to enhance communication stability and network performance. The proposed protocol extends the OLSR control message structure and employs enhanced MPR selection and routing path computation algorithms. LL prediction enables proactive selection of stable communication paths, while the CII helps avoid heavily interfered nodes during MPR selection. Additionally, the NL metric facilitates load balancing and prevents premature node failure due to resource exhaustion. Simulation results demonstrate that across different UAV flight speeds and network scales, OLSR-LCN protocol consistently outperforms both the OLSR and the position-based OLSR in terms of end-to-end delay, packet loss rate, and network efficiency. The cross-layer optimization approach effectively addresses frequent link disruptions, interference, and load imbalance in dynamic environments, providing a robust solution for reliable communication in complex FANETs. Full article

Benefiting from the advantages of unmanned aerial vehicle (UAV) platforms such as low cost, rapid deployment capability, and miniaturization, the application of UAV-borne synthetic aperture radar (SAR) has developed rapidly. Utilizing a self-developed monostatic Miniaturized SAR (MiniSAR) system and a bistatic MiniSAR system, our team conducted multiple imaging missions over the same vehicle equipment display area at different times. However, system disparities and time-varying factors lead to a mismatch between the distributions of the training and test data. Additionally, small ground vehicle targets under complex background clutter exhibit limited size and weak scattering characteristics. These two issues pose significant challenges to the precise detection of small ground vehicle targets. To address these issues, this article proposes a domain-unified adaptive target detection framework (DUA-TDF). The approach consists of two stages: image-to-image translation and feature extraction and target detection. In the first stage, a multi-scale detail-aware CycleGAN (MSDA-CycleGAN) is proposed to align the source and target domains at the image level by achieving unpaired image style transfer while emphasizing both global structure and local details of the generated images. In the second stage, a cross-window axial self-attention target detection network (CWASA-Net) is proposed. This network employs a hybrid backbone centered on the cross-window axial self-attention mechanism to enhance feature representation, coupled with a convolution-based stacked cross-scale feature fusion network to strengthen multi-scale feature interaction. To validate the effectiveness and generalization capability of the proposed algorithm, comprehensive experiments are conducted on both self-developed monostatic/bistatic SAR datasets and public dataset. Experimental results demonstrate that our method achieves an mAP50 exceeding 90% in within-domain tests and maintains over 80% in cross-domain scenarios, demonstrating exceptional and robust detection performance as well as cross-domain adaptability. Full article

The paradigm of cellular vehicle-to-everything (C-V2X) communications assisted by unmanned aerial vehicles (UAVs) is poised to revolutionize the future of sixth-generation (6G) intelligent transportation systems, as outlined by the international mobile telecommunication (IMT)-2030 vision. This integration of UAV-assisted C-V2X communications is set to enhance mobility and connectivity, creating a smarter and reliable autonomous transportation landscape. The UAV-assisted C-V2X networks enable hyper-reliable and low-latency vehicular communications for 6G applications including augmented reality, immersive reality and virtual reality, real-time holographic mapping support, and futuristic infotainment services. This paper presents a Markov chain model to study a third-generation partnership project (3GPP)-specified C-V2X network communicating with a flying UAV for task offloading in a Federated Learning (FL) environment. We evaluate the impact of various factors such as model update frequency, queue backlog, and UAV energy consumption on different types of communication latency. Additionally, we examine the end-to-end latency in the FL environment against the latency in conventional data offloading. This is achieved by considering cooperative perception messages (CPMs) that are triggered by random events and basic safety messages (BSMs) that are periodically transmitted. Simulation results demonstrate that optimizing the transmission intervals results in a lower average delay. Also, for both scenarios, the optimal policy aims to optimize the available UAV energy consumption, minimize the cumulative queuing backlog, and maximize the UAV’s available battery power utilization. We also find that the queuing delay can be controlled by adjusting the optimal policy and the value function in the relative value iteration (RVI). Moreover, the communication latency in an FL environment is comparable to that in the gross data offloading environment based on Kullback–Leibler (KL) divergence. Full article

Detecting corroded metal fittings in UAV-based transmission line inspections is challenging due to the small object size and environmental interference, causing high false and missed detection rates. To address these, this paper proposes a novel knowledge-distilled lightweight YOLO model, integrating a densely-connected convolutional network and spatial pixel-aware self-attention mechanism in the teacher model training stage to enhance feature transfer and structured feature utilization for reducing environmental interference, while employing the lightweight MobileNet as the feature extractor in the student model training stage and optimizing candidate box migration via the teacher model’s efficient intersection-over-union non-maximum suppression (EIoU-NMS). This model overcomes the challenges of small-object fitting detection in complex environments, improving fault identification accuracy and reducing manual inspection costs and missed detection risks, while its lightweight design enables rapid deployment and real-time detection on UAV terminals, providing a reliable technical solution for unmanned smart grid operation. Experimental results on actual UAV inspection images demonstrate that the model significantly enhances detection accuracy, reduces false and missed detections, and achieves faster speeds with substantially fewer parameters, highlighting its outstanding effectiveness and practicality in power system maintenance scenarios. Full article

Natural disasters often compromise telecommunications infrastructure, leading to unstable services or complete communication blackouts that hinder rescue operations and exacerbate victims’ distress. Rapidly deployable alternatives are, therefore, critical to sustaining reliable connectivity in affected regions. This work proposes a self-organizing multi-Unmanned Aerial Vehicle (UAV) swarm network capable of providing stand-alone and temporary coverage to both victims and emergency personnel in areas with compromised infrastructure through access points installed onboard UAVs. To address the challenges of partial observability in decentralized coordination, we introduce the Soft Transformer Recurrent Graph Network (STRGN), a novel encoder–decoder architecture inspired by the transformer model and extending the Soft Deep Recurrent Graph Network (SDRGN). By leveraging multi-head and cross-attention mechanisms, the STRGN captures higher-order spatiotemporal relationships, enabling UAVs to integrate information about neighbor proximity and ground user density when selecting actions. This facilitates adaptive positioning strategies that enhance coverage, fairness, and connectivity under dynamic conditions. Simulation results show that transformer-based approaches, including STRGN, the Soft Transformer Graph Network, and the Transformer Graph Network, consistently outperform SDRGN, and the Soft Deep Graph Network, and Deep Graph Network baselines by approximately $16\%$ across core metrics, while also demonstrating improved scalability across diverse terrains and swarm sizes. These findings highlight STRGN’s potential as a resilient framework for UAV-assisted communications in disaster response. Full article

Drones are increasingly being used in maritime engineering for ship maintenance, emergency rescue, and safety monitoring tasks. In these tasks, action recognition is important for human–drone interaction and for detecting abnormal situations such as falls or distress signals. However, the maritime environment is highly challenging, with illumination variations, water spray, and dynamic backgrounds often leading to ambiguity between similar actions. To address this issue, we propose MSG-GCN, a multi-semantic guided graph convolutional network for human action recognition. Specifically, MSG-GCN integrates structured prior semantic information and further introduces a textual–semantic alignment mechanism to improve the consistency and expressiveness of multimodal features. Benefiting from its lightweight hierarchical design, our model offers excellent deployment flexibility, making it well suited for resource-constrained UAV applications. Experimental results on large-scale benchmark datasets, including NTU60, NTU120 and UAV-human, demonstrate that MSG-GCN surpasses state-of-the-art methods in both classification accuracy and computational efficiency. Full article

Cooperative pursuit using multi-UAV systems presents significant challenges in dynamic task allocation, real-time coordination, and trajectory optimization within complex environments. To address these issues, this paper proposes a reinforcement learning-based task planning framework that employs a distributed Actor–Critic architecture enhanced with bidirectional recurrent neural networks (BRNN). The pursuit–evasion scenario is modeled as a multi-agent Markov decision process, enabling each UAV to make informed decisions based on shared observations and coordinated strategies. A multi-stage reward function and a BRNN-driven communication mechanism are introduced to improve inter-agent collaboration and learning stability. Extensive simulations across various deployment scenarios, including 3-vs-1 and 5-vs-2 configurations, demonstrate that the proposed method achieves a success rate of at least 90% and reduces the average capture time by at least 19% compared to rule-based baselines, confirming its superior effectiveness, robustness, and scalability in cooperative pursuit missions. Full article