Search Results (2,123)

Unmanned aerial vehicle (UAV) swarms have shown great potential in large-scale IoT (Internet of Things) and smart agriculture applications, particularly for cooperative monitoring and multi-target tracking in field environments. However, most existing coordination strategies assume ideal communication conditions, overlooking realistic network impairments such as congestion, packet loss, and latency. These impairments disrupt the timely exchange of information between UAVs and the ground base station, leading to delayed or lost control signals. As a result, coordination quality deteriorates and tracking performance is severely degraded in real-world deployments. To address this gap, we propose CSOOC (Communication-State Driven Online–Offline Coordination with Congestion Control), a hybrid control architecture that integrates centralized learning-based decision-making with decentralized rule-based policies to adapt UAV behaviors according to real-time network states. CSOOC consists of three key components: (1) an online module that enables centralized coordination under reliable communication, (2) an offline profit-driven mobility strategy based on local Gaussian maps for autonomous target tracking during communication loss, and (3) a congestion control mechanism based on STAR(Stratified Transmission and RTS/CTS), which combines temporal transmission desynchronization and RTS/CTS handshaking to enhance uplink reliability. We establish a unified co-simulation paradigm that connects network communication with swarm control and swarm coordination behavior. Experiments demonstrate that CSOOC achieves an average observation rate of 39.7%, surpassing baseline algorithms by 4.4–11.13%, while simultaneously improving network stability through significantly higher packet delivery ratios under congested conditions. These results demonstrate that CSOOC effectively bridges the gap between algorithmic performance in simulation and practical UAV swarm operations in communication-constrained environments. Full article

Reliable navigation in cooperative unmanned aerial vehicle (UAV) networks requires adaptively managing measurement degradations within Kalman-filter-based estimation frameworks. This paper introduces a learning-based Kalman approach for real-time detection of degraded measurements in mesh-network-based multi-UAV navigation. The method incorporates a data-driven pre-filtering module that assesses measurement reliability prior to the Kalman update, thereby improving the robustness of the estimation process under communication-induced degradations. Within this approach, four measurement fault detection strategies—Innovation Filter (IF), Deep Q-Network (DQN), Multi-Layer Perceptron (MLP), and Long Short-Term Memory (LSTM)—were implemented and comparatively evaluated through Monte Carlo simulations combining inertial sensors, time-of-arrival, and Doppler-based inter-agent observations. Additional statistical analyses, including ±1σ error bars and a Wilcoxon rank-sum test, were conducted to verify the significance of the performance differences among the methods. The results show that the proposed approach significantly enhances navigation reliability, particularly under degraded or intermittent GNSS and communication conditions. The MLP-based configuration achieved the best balance between fault-detection accuracy and overall filter consistency. These findings confirm the effectiveness of learning-augmented Kalman filtering architectures for robust and scalable cooperative UAV navigation. Full article

Small object detection in unmanned aerial vehicle (UAV) imagery poses significant challenges due to insufficient feature representation, complex background interference, and extremely small target sizes. These factors collectively degrade the performance of conventional detection algorithms, leading to low accuracy, frequent missed detections, and false alarms. To address these issues, we propose YOLO-DMF, which is a novel detection framework specifically designed for drone-based scenarios. Our approach introduces three key innovations from the perspectives of feature extraction and information fusion: (1) a Detail-Semantic Adaptive Fusion (DSAF) module that employs a multi-branch architecture to synergistically enhance shallow detail features and deep semantic information, thereby significantly improving feature representation for small objects; (2) a Multi-Scale Residual Spatial Attention (MSRSA) mechanism incorporating scale-adaptive spatial attention to improve robustness against background clutter while enabling a more precise localization of critical target regions; and (3) a Feature Pyramid Reuse and Fusion Network (FPRFN) that introduces a dedicated $160\times 160$ detection head and hierarchically combines multi-level shallow features with high-level semantic information through cross-scale fusion, effectively enhancing sensitivity to both small and tiny objects. Comprehensive experiments on the VisDrone2019 dataset demonstrate that YOLO-DMF outperforms state-of-the-art lightweight detection models. Compared to the baseline YOLOv8s, our method achieves improvements of 3.9% in mAP@0.5 and 2.5% in mAP@0.5:0.95 while reducing model parameters by 66.67% with only a 2.81% increase in computational cost. The model achieves a real-time inference speed of 34.1 FPS on the RK3588 NPU, satisfying the latency requirements for real-time object detection. Additional validation on both the AI-TOD and WAID datasets confirms the method’s strong generalization capability and promising potential for practical engineering applications. Full article

In autonomous operations of unmanned aerial vehicles (UAVs), accurate pose estimation is a core prerequisite for achieving autonomous navigation, obstacle avoidance, and task execution. To address the challenge of localization in GNSS-denied environments, Visual–Inertial Odometry (VIO) has emerged as a mainstream solution due to its outstanding performance. However, existing deep learning-based VIO methods exhibit limitations in their multi-modal fusion mechanisms. These methods typically employ simple concatenation or attention mechanisms for feature fusion. Furthermore, enhancements in accuracy are often accompanied by significant computational overhead. This makes it difficult for models to effectively handle complex, dynamic scenes while remaining lightweight. To this end, this paper proposes TSFNet (Two-stage Sequential Fusion Network), an efficient two-stage sequential fusion network. In the first stage, the network employs a lightweight visual backbone and a bidirectional recurrent network in parallel to extract spatial and motion features, respectively. A gated fusion unit is employed to achieve adaptive intra-frame feature fusion, dynamically balancing the contributions of different modalities. In the second stage, the fused features are organized into sequences and fed into a dedicated temporal network to explicitly model inter-frame motion dynamics. This decoupled fusion architecture significantly enhances the model’s representational capacity. Experimental results demonstrate that TSFNet achieves superior performance on both the EuRoC and Zurich Urban MAV datasets. Notably, on the Zurich Urban MAV dataset, it reduces the localization Root Mean Square Error (RMSE) by 62% compared to the baseline model, while simultaneously reducing the number of parameters and computational load by 76.65% and 24.30%, respectively. This research confirms that the decoupled two-stage fusion strategy is an effective approach for realizing high-precision, lightweight VIO systems. Full article

The rapid expansion of wireless communications has led to increasing demand and interference in the electromagnetic spectrum, raising the question of how to achieve reliable and adaptive monitoring in complex and dynamic environments. This study aims to investigate whether groups of unmanned aerial vehicles (UAVs) can provide an effective alternative to conventional, static spectrum monitoring systems. We propose a cooperative monitoring system in which multiple UAVs, integrated with software-defined radios (SDRs), conduct energy measurements and share their observations with a data fusion center. The fusion process is based on Dempster–Shafer theory (DST), which models uncertainty and combines partial or conflicting data from spatially distributed sensors. A simulation environment developed in MATLAB emulates UAV mobility, communication delays, and propagation effects in various swarm formations and environmental conditions. The results confirm that cooperative spectrum monitoring using UAVs with DST data fusion improves detection robustness and reduces susceptibility to noise and interference compared to single-sensor approaches. Even under challenging propagation conditions, the system maintains reliable performance, and DST fusion provides decision-supporting results. The proposed methodology demonstrates that UAV groups can serve as scalable, adaptive tools for real-time spectrum monitoring and contributes to the development of intelligent monitoring architectures in cognitive radio networks. Full article

This paper investigates the problem of maximizing the average achievable rate in an unmanned aerial vehicle (UAV)-assisted vehicular network, where UAVs and ground base stations (GBSs) jointly serve vehicular users through integrated sensing and communication (ISAC) technology. To balance communication and sensing performance, we maximize the average achievable rate under radar sensing constraints by jointly optimizing UAV trajectory planning, vehicle association, and subchannel allocation. The resulting problem is a challenging mixed-integer nonlinear program (MINLP) due to the strong coupling among decision variables. To address this, we propose an iterative algorithm based on block coordinate descent (BCD), which decomposes the original problem into three subproblems—vehicle association, UAV trajectory planning, and subchannel allocation—by fixing certain variables. These subproblems are solved alternately using successive convex approximation (SCA) and convex optimization techniques. Simulation results verify the effectiveness of the proposed algorithm, demonstrating superior average achievable rate performance compared with conventional methods under radar sensing constraints. Full article

In this paper, we investigate a UAV-assisted wireless powered communication network (WPCN) where UAVs act as access points (APs) to periodically serve a group of ground sensor nodes (SNs). Unlike fixed APs in traditional WPCNs, UAV-assisted WPCNs can leverage UAV mobility to maximize system throughput by optimizing the UAV trajectory and wireless resource allocation. However, due to the limited data buffer capacity of the SNs, UAVs may fail to provide timely services, leading to data overflow. Therefore, UAVs must offer efficient and timely services to the SNs. Our objective was to maximize the total energy efficiency of all ground SNs by jointly optimizing UAV transmit power, downlink (DL) wireless energy transfer (WET) time, uplink (UL) wireless information transfer (WIT) time, and SN transmit power under minimal quality-of-service (QoS) constraints. However, the formulated optimization problem is non-convex and difficult to solve directly. To address this, we applied fractional programming theory to transform the non-convex problem into a tractable form. Subsequently, a block coordinate descent-based algorithm was proposed to obtain a near-optimal resource allocation scheme. Extensive simulation results show that our proposed method achieved significantly better performance in terms of system throughput and energy efficiency compared to other benchmark solutions. Full article

Unmanned aerial vehicles (UAVs) have emerged as a pivotal technology for enhancing the agility and resilience of future wireless networks. However, conventional optimization approaches remain predominantly reactive, relying solely on current network conditions for decision making. This proves to be inadequate for handling sudden traffic surges in dynamic environments, resulting in suboptimal service quality. To address this limitation, this paper proposes a novel joint optimization framework integrating spatiotemporal traffic prediction. This equips UAVs with predictive capabilities, thereby facilitating a paradigm shift from passive response to proactive service provision. The main contributions of this work are fourfold: First, a novel closed-loop optimization framework is introduced, deeply integrating an advanced traffic-forecasting module with a communication resource optimization module to provide a systematic, forward-looking decision-making solution for UAV-assisted communications. Second, a cellular traffic predictor based on Gaussian mixture model meta-learning (GMM-ML) is designed. This model effectively captures the periodicity and heterogeneity of traffic data, enabling the precise prediction of future hotspot areas and resolving the challenge of accurate forecasting under small-sample conditions. Third, a long-term discounted mixed-integer nonlinear programming (MINLP) problem model is formulated. This innovatively incorporates a “service readiness reward” for predicted hotspots within the objective function to achieve long-term performance optimization. Fourth, an efficient and convergent predictive iterative association and location optimization (P-IALO) algorithm is developed. Utilizing block coordinate descent and continuous convex approximation techniques, this algorithm decomposes the original complex problem to alternately optimized subproblems of user association and trajectory planning, guaranteeing algorithmic convergence. To validate the effectiveness of the proposed framework, large-scale simulation experiments were conducted using real-world traffic data. The results demonstrate that compared to traditional reactive algorithms, the proposed scheme significantly enhances the overall system throughput by 12%, improves user QoS satisfaction by 9.4%, and reduces service interruptions by 34.2%. Concurrently, the algorithm exhibits favorable convergence speed and robustness, maintaining performance advantages even under predictive errors. Extensive experimentation thoroughly demonstrates the efficacy of this research in enhancing the performance of drone-assisted networks. Full article

Multi-Unmanned Aerial Vehicle (Multi-UAV) cooperative search represents a cutting-edge research direction in the field of unmanned aerial vehicle applications. The use of multi-UAV systems for low-altitude target search and area surveillance has become an effective means of enhancing security capabilities. In practical scenarios, UAVs rely on onboard sensors to acquire environmental information; however, due to the limited perceptual range of these sensors, their observation capabilities are inherently local and constrained. This paper investigates the problem of multi-UAV cooperative search in partially observable low-altitude environments, where each UAV possesses a circular sensing range with a finite radius. Target location information is only obtained when a target enters the field of view of any UAV. The objective is to achieve cooperative search and sustain continuous surveillance while ensuring safety among UAVs and with the environment. To address this challenge, we propose a novel multi-agent deep reinforcement learning (MADRL) algorithm named Normalizing Graph Attention Soft Actor-Critic (NGASAC). This algorithm integrates a normalizing flow (NL) layer and a multi-head graph attention network (MHGAT). The normalizing flow technique maps traditional Gaussian sampling to a more complex action distribution, thereby enhancing the expressiveness and flexibility of the policy. Simultaneously, by constructing a multi-head graph attention network that captures “obstacle–target” relationships, the algorithm improves the UAVs’ ability to learn and reason about complex spatial topologies, leading to significantly better performance in cooperative search and stable surveillance of hidden targets. Simulation results demonstrate that the NGASAC algorithm markedly outperforms baseline methods such as Multi-Agent Soft Actor-Critic (MASAC), Multi-Agent Proximal Policy Optimization (MAPPO), and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) across multiple evaluation metrics, including success rate, task time, and obstacle avoidance capability. Furthermore, it exhibits strong generalization performance and robustness. Full article

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R² = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management. Full article

For unmanned aerial vehicle (UAV) ground crew marshalling tasks, the accuracy of skeleton-based action recognition is often limited by the high similarity of motion patterns across action categories as well as variations in individual performance. To address this issue, we propose an adaptive refined graph convolutional network with enhanced features for action recognition. First, a multi-order and motion feature modeling module is constructed, which integrates joint positions, skeletal structures, and angular encodings for multi-granularity representation. Static-domain and dynamic-domain features are then fused to enhance the diversity and expressiveness of the input representations. Second, a data-driven adaptive graph convolution module is designed, where inter-joint interactions are dynamically modeled through a learnable topology. Furthermore, an adaptive refinement feature activation mechanism is introduced to optimize information flow between nodes, enabling fine-grained modeling of skeletal spatial information. Finally, a frame-index semantic temporal modeling module is incorporated, where joint-type semantics and frame-index semantics are introduced in the spatial and temporal dimensions, respectively, to capture the temporal evolution of actions and comprehensively exploit spatio-temporal semantic correlations. On the NTU-RGB+D 60 and NTU-RGB+D 120 benchmark datasets, the proposed method achieves accuracies of 89.4% and 94.2% under X-Sub and X-View settings, respectively, as well as 81.7% and 83.3% on the respective benchmarks. On the self-constructed UAV Airfield Ground Crew Dataset, the proposed method attains accuracies of 90.71% and 96.09% under X-Sub and HO settings, respectively. Environmental robustness experiments demonstrate that under complex environmental conditions including illumination variations, haze, rain, shadows, and occlusions, the adoption of the Test + Train strategy reduces the maximum performance degradation from 3.1 percentage points to within 1 percentage point. Real-time performance testing shows that the system achieves an end-to-end inference latency of 24.5 ms (40.8 FPS) on the edge device NVIDIA Jetson Xavier NX, meeting real-time processing requirements and validating the efficiency and practicality of the proposed method on edge computing platforms. Full article

Unmanned aerial vehicle (UAV) intrusions cause flight delays and disrupt airport operations, so accurate monitoring is essential for safety. To address the scarcity and mismatch of real-world training data in small-target detection, an anti-UAV approach is proposed that integrates style transfer learning (STL) with deep learning. An airport monitoring platform is established to acquire a real UAV dataset, and a Cycle-Consistent Generative Adversarial Network (CycleGAN) is employed to synthesize multi-scene images that simulate diverse airport backgrounds, thereby enriching the training distribution. Using these simulated scenes, a controlled comparison of YOLOv5/YOLOv6/YOLOv7/YOLOv8 is conducted, in which YOLOv5 achieves the best predictive performance with AP values of 93.95%, 98.09%, and 97.07% across three scenarios. On public UAV datasets, the STL-enhanced model (YOLOv5_STL) is further compared with other small-object detectors and consistently exhibits superior performance, indicating strong cross-scene generalization. Overall, the proposed method provides an economical, real-time solution for airport UAV intrusion prevention while maintaining high accuracy and robustness. Full article

Driven by the proliferation of the Internet of Things (IoT), Mobile Edge Computing (MEC) is a key technology for meeting the low-latency and high-computational demands of future wireless networks. However, ground-based MEC servers suffer from limited coverage and inflexible deployment. Unmanned Aerial Vehicles (UAVs), with their high mobility, can serve as aerial edge servers to extend this coverage. This paper addresses the multi-user serial task offloading problem in cache-assisted UAV-MEC systems by proposing a joint optimization algorithm for service caching, UAV positioning, task offloading, and serial processing order. Under the constraints of physical resources such as UAV cache capacity, heterogeneous computing capabilities, and wireless channel bandwidth, an optimization problem is formulated to minimize the weighted sum of task completion time and user cost. The method first performs service caching based on task popularity and then utilizes the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to optimize the UAV’s position, task offloading decisions, and serial processing order. The MADDPG algorithm consists of two collaborative agents: a UAV position agent responsible for selecting the optimal UAV position, and a task scheduling agent that determines the serial processing order and offloading decisions for all tasks. Simulation results demonstrate that the proposed algorithm can converge quickly to a stable solution, significantly reducing both task completion time and user cost. Full article

Unmanned aerial vehicle (UAV) vision systems have been widely deployed for aerial monitoring applications, yet small-target detection in UAV imagery remains a significant challenge due to minimal pixel representation, substantial scale variations, complex background interference, and varying illumination conditions. Existing object detection algorithms struggle to maintain high accuracy when processing small targets with fewer than 32 × 32 pixels in UAV-captured scenes, particularly in complex environments where target-background confusion is prevalent. To address these limitations, this study proposes RMH-YOLO, a refined multi-scale architecture. The model incorporates four key innovations: a Refined Feature Module (RFM) that fuses channel and spatial attention mechanisms to enhance weak feature representation of small targets while maintaining contextual integrity; a Multi-scale Focus-and-Diffuse (MFFD) network that employs a focus-diffuse transmission pathway to preserve fine-grained spatial details from high-resolution layers and propagate them to semantic features; an efficient CS-Head detection architecture that utilizes parameter-sharing convolution to enable efficient processing on embedded platforms; and an optimized loss function combining Normalized Wasserstein Distance (NWD) with InnerCIoU to improve localization accuracy for small targets. Experimental validation on the VisDrone2019 dataset demonstrates that RMH-YOLO achieves a precision and recall of 53.0% and 40.4%, representing improvements of 8.8% and 7.4% over the YOLOv8n baseline. The proposed method attains mAP50 and mAP50:95 of 42.4% and 25.7%, corresponding to enhancements of 9.2% and 6.4%, respectively, while maintaining computational efficiency with only 1.3 M parameters and 16.7 G FLOPs. Experimental results confirm that RMH-YOLO effectively improves small-target detection accuracy while maintaining computational efficiency, demonstrating its broad application potential in diverse UAV aerial monitoring scenarios. Full article

Recent advances in UAV (Unmanned Aerial Vehicle)-based remote sensing have significantly enhanced the efficiency of monitoring and managing agricultural and forested areas. However, the low-altitude and narrow-field-of-view characteristics of UAVs make robust image mosaicking essential for generating large-area composites. A TIN (triangulated irregular network)-based mosaicking framework is herein proposed to address this challenge. A TIN-based mosaicking method constructs a TIN from extracted tiepoints and the sparse point clouds generated by bundle adjustment, enabling rapid mosaic generation. Its performance strongly depends on the quality of tiepoint extraction. Traditional matching combinations, such as SIFT with Brute-Force and SIFT with FLANN, have been widely used due to their robustness in texture-rich areas, yet they often struggle in homogeneous or repetitive-pattern regions, leading to insufficient tiepoints and reduced mosaic quality. More recently, deep learning-based methods such as LightGlue have emerged, offering strong matching capabilities, but their robustness under UAV conditions involving large rotational variations remains insufficiently validated. In this study, we applied the publicly available LightGlue matcher to a TIN-based UAV mosaicking pipeline and compared its performance with traditional approaches to determine the most effective tiepoint extraction strategy. The evaluation encompassed three major stages—tiepoint extraction, bundle adjustment, and mosaic generation—using UAV datasets acquired over diverse terrains, including agricultural fields and forested areas. Both qualitative and quantitative assessments were conducted to analyze tiepoint distribution, geometric adjustment accuracy, and mosaic completeness. The experimental results demonstrated that the hybrid combination of SIFT and LightGlue consistently achieved stable and reliable performance across all datasets. Compared with traditional matching methods, this combination detected a greater number of tiepoints with a more uniform spatial distribution while maintaining competitive reprojection accuracy. It also improved the continuity of the TIN structure in low-texture regions and reduced mosaic voids, effectively mitigating the limitations of conventional approaches. These results demonstrate that the integration of LightGlue enhances the robustness of TIN-based UAV mosaicking without compromising geometric accuracy. Furthermore, this study provides a practical improvement to the photogrammetric TIN-based UAV mosaicking pipeline by incorporating a LightGlue matching technique, enabling more stable and continuous mosaicking even in challenging low-texture environments. Full article