Drones

Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude environments, small target proportions, irregular target movement, and frequent occlusions complicate the multi-object tracking task. This paper proposes a cross-scene multi-object tracking (CST) method to address these challenges. Firstly, a lightweight object detection framework is proposed to optimize key sub-tasks by integrating multi-dimensional temporal and spatial information. Secondly, trajectory prediction is achieved through the implementation of Model-Agnostic Meta-Learning, enhancing adaptability to dynamic environments. Thirdly, re-identification is facilitated using Dempster–Shafer Theory, which effectively manages uncertainties in target recognition by incorporating aircraft state information. Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields. Full article

The Tibetan Plateau is a critical ecological habitat where the overpopulation of plateau pika (Ochotona curzoniae), a keystone species, accelerates grassland degradation through excessive burrowing and herbivory, threatening ecological balance and human activities. To address the inefficiency and high costs of traditional pika burrow monitoring, this study proposes an intelligent monitoring solution that integrates drone remote sensing with deep learning. By combining the lightweight visual Transformer architecture EfficientViT with the hybrid attention mechanism CBAM, we develop an enhanced YOLOv11-AEIT algorithm: (1) EfficientViT is employed as the backbone network, strengthening micro-burrow feature representation through a multi-scale feature coupling mechanism that alternates between local window attention and global dilated attention; (2) the integration of CBAM (Convolutional Block Attention Module) in the feature fusion neck reduces false detections through dual-channel spatial attention filtering. Evaluations on our custom PPCave2025 dataset show that the enhanced model achieves a 98.6% mAP@0.5, outperforming the baseline YOLOv11 by 3.5 percentage points, with precision and recall improvements of 4.8% and 7.2%, respectively. The algorithm enhances efficiency by a factor of 15 compared to manual inspection, while seamlessly meeting real-time drone detection requirements. This approach provides high-precision yet lightweight technical support for plateau ecological conservation and serves as a valuable methodological reference for similar ecological monitoring tasks. Full article

In this work, we develop a novel approach for designing the trajectory and controlling the velocity for an unmanned aerial vehicle (UAV) to achieve energy-efficient visual coverage of multiple terrestrial regions. Unlike previous works, our proposed approach allows the UAV to flexibly change both its velocity and its flight altitude during its task tour. To minimize the UAV’s total flight energy consumption during its task tour, we propose a novel four-step approach. The first step devises a simulated annealing (SA)-based searching algorithm to optimize the UAV’s photographing altitude for each region, considering various image resolution requirements and safety requirements across regions. Based on the identified photographing altitudes of all regions, the second step formulates a traveling salesman problem (TSP) and uses an efficient approximate method to determine the visiting order of each region. The third step generates all candidate intra-region trajectories used for visual coverage of each region, of which the optimal one will be decided together with the inter-region trajectory used for transitioning between neighboring regions during the fourth step. Finally, the fourth step employs dynamic programming (DP) and geometry to jointly determine the UAV’s velocity control and complete trajectory during its task tour. Extensive experiments validate the effectiveness and superiority of the proposed approach, compared with several existing methods. Full article

This paper presents a redesigned YOLO-based model tailored for small-object detection in drone applications. To enhance its performance in detecting small and blurry targets, this study introduces the C3_CAA module to refine feature maps, integrates the CPA module and SI-IoU to improve detection accuracy, and incorporates channel and spatial attention mechanisms to further enhance target localization and identification performance.The experimental results indicated that the proposed method performs well on multiple datasets. The mAP value increases by 2% on the VISDRONE dataset, 1.6% on the UAVDT dataset, 0.9% on the CARPK dataset, and 1% on the UAVROD data set. Full article

In recent decades, forests have experienced an increasing trend in the number of pest outbreaks worldwide, apparently driven by strong annual variability in precipitation, higher air temperatures, and strong winds. Pest outbreaks have negative ecological, economic, and environmental impacts on forest ecosystems, such as reduced biodiversity, carbon sequestration, and overall forest health. Traditional monitoring methods of these disturbances, while accurate, are time-consuming and limited in scope. Remote sensing, particularly UAV (Unmanned Aerial Vehicle)-based technologies, offers a precise and cost effective alternative for monitoring forest health. This study evaluates the temporal and spatial progression of bark beetle damage in a fir-dominated forest in the Zao Mountains, Japan, using UAV RGB imagery and DL (Deep Learning) models (YOLO - You Only Look Ones), over a four-year period (2021–2024). Trees were classified into six health categories: Healthy, Light Damage, Medium Damage, Heavy Damage, Dead, and Fallen. The results revealed a significant decline in healthy trees, from 67.4% in 2021 to 25.6% in 2024, with a corresponding increase in damaged and dead trees. Light damage emerged as a potential early indicator of forest health decline. The DL model achieved an accuracy of 74.9% to 82.8%. The results showed the effectiveness of DL in detecting severe damage but highlighted that challenges in distinguishing between healthy and lightly damaged trees still remain. The study highlights the potential of UAV-based remote sensing and DL for monitoring forest health, providing valuable insights for targeted management interventions. However, further refinement of the classification methods is needed to improve accuracy, particularly in the precise detection of tree health categories. This approach offers a scalable solution for monitoring forest health in similar ecosystems in other subalpine areas of Japan and the world. Full article

In collaborative operations of multiple autonomous underwater vehicles (AUVs), the complexity of underwater environments and limited onboard energy make environmental adaptation and energy efficiency critical metrics for evaluating path quality. This paper addresses path conflict resolution in multi-AUV path planning by proposing an equal-time waypoint planning method. The approach involves randomly selecting equal-time waypoints in free space and generating path encoding sequences for each AUV. These path encodings are then optimized through four modules, considering both path smoothness and adaptability to ocean currents. The resulting paths comply with kinematic constraints while achieving reduced energy consumption. The method enables velocity adjustments across different segments to prevent conflicts. Simulation results demonstrate the feasibility of this approach in resolving multi-AUV path conflicts with low energy expenditure. Full article

Unmanned aerial vehicles (UAVs) are promising and powerful aerial platforms that can execute a variety of complex tasks. However, the increasing complexity of tasks and number of UAV nodes pose significant challenges for UAV sensing networks, such as limiting the spectral resources and increasing device complexity. A potential solution is to implement full-duplex (FD) technology in UAV sensor network transceivers. Although appropriate self-interference (SI) cancellation techniques have been employed in the digital domain, the amplitude of the signal of interest (SoI) is relatively small and can be obscured by SI, especially over longer distances. Moreover, the introduction of phase offsets when filtering measurement signals can lead to signal distortion, resulting in estimation errors in the measurement results. To address these issues, this paper presents a joint transmit (TX) and receive (RX) beamforming algorithm based on the penalty dual decomposition (PDD) algorithm, which considers the constraints of transmission power, reception power, and residual SI power. The simulation analyses demonstrate that with a limited number of antennas, the proposed joint TX-RX beamforming algorithm can effectively suppress SI by up to 140 dB, yielding high-precision measurements in UAV sensor networks without compromising the accuracy of the control signals. Compared with that of the traditional frequency-division duplex (FDD) mode, the measurement accuracy is not decreased; compared with those of the time-division duplex (TDD) mode, the distance and speed measurement accuracies of the UAVs are increased by 10 m and 1.5 m/s, respectively, in the FD mode because there is no interruption of the tracking loop and no continuous retracking in the FD mode. Full article

Blockchain technology is a competitive solution to address the prevailing data security concerns in unmanned aerial vehicle (UAV) networks. However, wireless communication links between UAV nodes are vulnerable to electromagnetic interference and competition due to limited spectrum resources. Consequently, a comprehensive analysis of blockchain performance within UAV network environments is imperative for the effective deployment and optimization of blockchain technology. This paper presents two theoretical models. The first model assesses the impact of the Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) channel access protocol on the latency and throughput of the chained HotStuff consensus algorithm. The second model considers the movement characteristics of UAV nodes in three-dimensional space and the complexity of the communication environment. The aim of the second model is to calculate the consensus failure probability of UAV networks under electromagnetic interference. The results demonstrate that the theoretical values closely match the actual simulation outcomes. Full article

To guide the movement of a UAV swarm in an obstacle-dense environment, a curved regular virtual tube based on pigeon-inspired optimization (PIO) is planned in this paper. There is no obstacle within the virtual tube, which serves as a safe corridor for UAVs. Then, a distributed swarm controller based on a pigeon flocking hierarchical model is proposed, enabling all UAVs to pass through a virtual tube, guaranteeing safety between UAVs and keeping within the virtual tube. Numerical simulations demonstrate the effectiveness of the proposed virtual tube planning and UAV swarm passing-through methods. Full article

Electric vertical take-off and landing (eVTOL) aircraft are transforming urban air mobility (UAM) by providing efficient, low-emission, and rapid transit in congested cities. However, ensuring safe and stable landings remains a critical challenge, particularly in constrained urban environments with variable wind conditions. This study investigates the landing aerodynamics of a multi-rotor eVTOL using the lattice Boltzmann method (LBM), a computational approach well-suited to complex boundary conditions and parallel processing. This analysis examines the ground effect, descent speed, and crosswind influence on lift distribution and stability. A rooftop landing scenario is also explored, where half of the rotors operate over a rooftop while the rest remain suspended in open air. Results indicate that rooftop landings introduce asymmetric lift distribution due to crosswind and roof-induced flow circulation, significantly increasing rolling moment compared to ground landings. These findings underscore the role of descent speed, crosswinds, and landing surface geometry in eVTOL aerodynamics, particularly the heightened risk of rollover in rooftop scenarios. Full article

This study introduces a novel framework for intelligent unmanned BVR maneuver control within the context of adversarial games. The emphasis lies on three pivotal aspects: situational awareness, maneuver decision-making, and precise maneuver control. Within this paradigm, our unmanned aerial vehicles (UAVs) can assimilate crucial situational information through constructed situational vectors and execute sophisticated maneuvers, effectively addressing the intricacies of dynamic flight environments and various unpredictable scenarios within the game setting. To achieve granular maneuver control, this research introduces the Priority Heading Polling–Random Observation Weight (PHP-ROW) method, underpinned by deep reinforcement learning. This approach integrates two primary components: (1) the priority heading polling (PHP) mechanism, which governs the extent of flight trajectories while emphasizing heading control, and (2) the random observation weight (ROW) technique, which adeptly moderates the influence of roll angle rewards during the learning phase. The superiority of the PHP-ROW method is showcased by contrasting it against the conventional proximal policy optimization (PPO) algorithm. Conclusively, the utility and efficacy of the presented framework are corroborated through human–machine adversarial game simulations in a hyper-realistic environment. This investigation provides foundational theoretical and empirical contributions to the realm of intelligent unmanned aerial maneuver control, promising significant implications for the evolution of aviation technology in adversarial game contexts. Full article

The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to design controllers and DC/DC buck converters for optimizing the energy consumption and path following error of a PEM fuel cell-powered quadrotor system. Hence, the system consists of two PSO- and GWO-based optimizers. Optimizer I is used for determining the parameters of the PD controller, which is used for minimizing the route-tracking error. On the other hand, the I controller parameters and the values of the DC/DC buck converters’ components are determined by Optimizer II to minimize the voltage-tracking errors of the converters. Both optimizers work together in the system and try to minimize tracking errors while also minimizing power consumption by using suitable objective functions. Simulation results demonstrate the effectiveness of the PSO- and GWO-based design of the controllers and converters in enhancing energy efficiency and improving the quadrotor’s flight stability. For step inputs, the GWO-based optimized system shows better performance according to power consumption and the time domain criteria such as rise time and settling time. However, the PSO-based optimized system shows 24.707% better performance for overshoot. On the other hand, 10.8866% less power consumption is observed for the GWO-based optimized system. This power efficient performance of the GWO-based system increases to 18% for the complex route involving ramp and step inputs. Then, a 39 s route test was performed and the total power consumptions for the GWO-based optimized and PSO-based optimized systems were observed to be 168.0015 W/s and 179.9070 W/s, respectively. This means that GWO-based optimizers provide more energy-efficient performance for complex routes. On the other hand, it was determined that the tracking errors in the performance of the desired and actual values of both translational and rotational movement parameters and the forces and torques required for the quadrotor to follow this route were obtained at a maximum of 4% for systems optimized with both techniques. This shows that the full systems optimized with both GWO and PSO algorithms significantly increase their energy efficiency and provide maximum route-following performance. Full article

The flight controllability and safety of unmanned compound rotorcraft are closely related to their aerodynamic characteristics. During forward flight, complex aerodynamic interference effects arise among the rotor, propeller, wing, fuselage, and horizontal–vertical tail. These interactions change dramatically with variations in forward speed, which may have a substantial impact on flight performance. This paper investigates aerodynamic interference related to the rotor, propeller, and fuselage of a sample unmanned compound rotorcraft with a novel configuration. On this basis, a flight dynamics model that incorporates the identified aerodynamic interference is formulated. Firstly, an analysis of rotor/propeller/fuselage aerodynamic interference is performed using the momentum source method (MSM). Subsequently, the aerodynamic models for the wing, fuselage, and horizontal–vertical tail are updated by integrating aerodynamic interference factors, leading to the development of a nonlinear flight dynamics model for the sample unmanned compound rotorcraft. Finally, to validate the updated flight dynamics model, numerical simulation results are systematically compared against wind tunnel test results. The results reveal a significant correlation between the numerical simulation data and wind tunnel test results, which indicates that the updated flight dynamics model possesses high accuracy and reliability and can characterize the dynamic characteristics of the sample unmanned compound rotorcraft within the flight speed envelope. Full article

This work introduces a broad framework for the enhanced nonlinearity assessment of PAs for 5G FR1 in UAV–RIS networks with integration. This study employs LabVIEW-based double-frequency IMD3 testing methodology for a fast automatic test bed to quantify the nonlinearity and its EMI effect on the UAV–RIS system. The analysis includes the evaluation of the EMI effect on the TxRx link using the SNR model. In order to enhance the efficiency of the system, a 16 QAM modulator is incorporated into the system in order to modulate and demodulate the signal. Additionally, the system includes a Deep Q agent based on the DRL model that enhances the controllability and efficiency of amplifiers under the dynamic environment typical for UAV–RIS networks. This work offers a framework for enhancing amplifier design for EMI reduction and supports the sound engineering of future UAV–RIS-based communication networks. The outcomes prove that signal integrity and nonlinearity characterization are enhanced significantly, providing important information for future 5G and beyond wireless communication systems. Full article

To address challenges in hybrid rice seed production—specifically labor dependence, low uniformity of pollen distribution, and low operational efficiency—which collectively drive up large-scale production costs, technological innovations are critical. However, despite the demonstrated potential of UAV-assisted pollination, the quantitative relationships between its operational parameters (altitude, speed, flight patterns) and pollen dispersal dynamics remain poorly understood, impeding standardization efforts. In this study, guided by agronomic pollination requirements, we developed an integrated analytical framework linking “pollen density-yield” dynamics to elucidate the governing mechanisms of flight parameters on pollination quality. A DJI T50 UAV was used to carry out the assisted pollination test on two varieties of hybrid rice, Changtian You 405 and Wanxiang You 377, to explore the effects of different flight speeds, altitudes, and trajectories of the UAV on pollination quality and to evaluate the cost-effectiveness ratio, taking the yield and its composition as the evaluation indexes. The experimental results showed that the UAV flight operation parameters had a significant effect on the pollination quality, and the best pollination quality was obtained when the flight altitude was 4 m and the speed was 3 m/s, achieving yields of 2.64 and 3.15 t/hm²; the average yields of the UAV-assisted pollination were 2.10 and 2.61 t/hm², and the filled grain percentages were 15.76% and 34.2%, respectively. These increased the yields by 21.4% and 11.06%, respectively, and the filled grain percentages by 8.69% and 3.95%, compared with artificial pollination. The results also showed that the cost-effectiveness ratio of UAV-assisted pollination was 28.11% lower than that of artificial operation. The results indicate that UAVs have great application prospects in hybrid rice pollination. Full article

This paper proposes a Kalman filter-based cascaded PI-(1+PI) controller, optimized using an Improved Spider Wasp Optimizer (ISWO), to address the challenges of USV heading control in dynamic marine environments. Traditional PID controllers struggle with nonlinearities and noise in USV systems while existing metaheuristic algorithms face limitations in balancing exploration and exploitation. To overcome these issues, the ISWO integrates dynamic adaptive grouping, perturbation dimension-symmetric distance optimization, and nonlinear time-varying weights, enhancing convergence speed and optimization accuracy. A transfer function model of the USV heading system is established using voyage data, with ISWO optimizing its parameters, achieving a 5.67% reduction in mean squared error (MSE) compared to the original Spider Wasp Optimizer and outperforming classical algorithms like Arithmetic Optimization Algorithm (AOA), Crayfish Optimization Algorithm (COA), and Marine Predators Algorithm (MPA). The proposed KF-PI(1+PI) controller incorporates a Kalman filter to suppress noise and a cascaded structure to improve gain and response speed, reducing integrated time absolute error (ITAE) by 84% relative to traditional PID controllers. The hardware-in-the-loop simulation experiments further validate the proposed controller’s robustness. The study demonstrates that ISWO-optimized control systems significantly enhance USV navigation precision and adaptability, offering a viable solution for autonomous marine operations. Full article

The widespread application of drone technology has raised security concerns, as unauthorized drones can lead to illegal intrusions and privacy breaches. Traditional detection methods often fall short in balancing performance and lightweight design, making them unsuitable for resource-constrained scenarios. To address this, we propose the IASL-YOLO algorithm, which optimizes the YOLOv8s model to enhance detection accuracy and lightweight efficiency. First, we design the CFE-AFPN network to streamline the architecture while boosting feature fusion capabilities across non-adjacent layers. Second, we introduce the SIoU loss function to address the orientation mismatch issue between predicted and ground truth bounding boxes. Finally, we employ the LAMP pruning algorithm to compress the model. Experimental results on the Anti-UAV dataset show that the improved model achieves a 2.9% increase in Precision, a 6.8% increase in Recall, and 3.9% and 3.8% improvements in mAP50 and mAP50-95, respectively. Additionally, the model size is reduced by 75%, the parameter count by 78%, and computational workload by 30%. Compared to mainstream algorithms, IASL-YOLO demonstrates significant advantages in both performance and lightweight design, offering an efficient solution for drone detection tasks. Full article

Although deep learning (DL) methods are effective for detecting protocol attacks involving drones in sixth-generation (6G) nonterrestrial networks (NTNs), classifying novel attacks and identifying anomalous sequences remain challenging. The internal capture processes and matching results of DL models are useful for addressing these issues. The key challenges involve obtaining this internal information from DL-based anomaly detection methods, using this internal information to establish new classifications for uncovered protocol attacks and tracing the input back to the anomalous protocol sequences. Therefore, in this paper, we propose an interpretable anomaly classification and identification method for 6G NTN protocols. We design an interpretable anomaly detection framework for 6G NTN protocols. In particular, we introduce explainable artificial intelligence (XAI) techniques to obtain internal information, including the matching results and capture process, and design a collaborative approach involving different detection methods to utilize this internal information. We also design a self-evolving classification method for the proposed interpretable framework to classify uncovered protocol attacks. The rule and baseline detection approaches are made transparent and work synergistically to extract and learn from the fingerprint features of the uncovered protocol attacks. Furthermore, we propose an online method to identify anomalous protocol sequences; this intrinsic interpretable identification approach is based on a two-layer deep neural network (DNN) model. The simulation results show that the proposed classification and identification methods can be effectively used to classify uncovered protocol attacks and identify anomalous protocol sequences, with the precision increasing by a maximum of 32.8% and at least 26%, respectively, compared with that of existing methods. Full article

This paper explores unconventional configurations of rotary-wing unmanned aerial vehicles (UAVs), focusing on designs that transcend the limitations of traditional ones. Through innovative rotor arrangements, refined airframe structures, and novel flight mechanisms, these advanced designs aim to significantly enhance performance, versatility, and functionality. Rotary-wing UAVs that deviate markedly from conventional models in terms of mechanical topology, aerodynamic principles, and movement modalities are rigorously examined. These unique UAVs are categorized into four distinct groups based on their mechanical configurations and dynamic characteristics: (1) UAVs with tilted or tiltable propellers, (2) UAVs featuring expanded mechanical structures, (3) UAVs with morphing multirotor capabilities, and (4) UAVs incorporating groundbreaking aerodynamic concepts. This classification establishes a structured framework for analyzing the advancements in these innovative designs. Finally, key challenges identified in the review are summarized, and corresponding research outlooks are derived to guide future development in rotary-wing drone technology. Full article