DroneRFa:用于侦测低空无人机的大规模无人机射频信号数据集

为研究与发展反无人机检测识别技术,该文公开了一个名为DroneRFa的大规模无人机射频信号数据集。该数据集使用软件无线电设备探测无人机与遥控器相互通信的射频信号,包含城市户外场景下运动无人机信号9类、城市室内场景下信号15类以及背景参照信号1类。每类数据有不少于12个片段,每个片段包含1亿个以上的采样点。数据采集覆盖了3个ISM无线电频段,记录无人机多频通信的真实活动。该数据集具有详细的无人机户外飞行距离和工作频段标注,以前缀字符结合二进制编码的形式方便用户灵活访问所需数据。此外,该文提供了基于频谱可视统计特征和基于深度学习表征的两种无人机识别方案,以验证数据集的可靠和有效性。

A novel framework to intercept GPS-denied,bomb-carrying,nonmilitary,kamikaze drones:Towards protecting critical infrastructures

Protection of urban critical infrastructures(CIs)from GPS-denied,bomb-carrying kamikaze drones(G-BKDs)is very challenging.Previous approaches based on drone jamming,spoofing,communication interruption and hijacking cannot be applied in the case under examination,since G-B-KDs are uncontrolled.On the other hand,drone capturing schemes and electromagnetic pulse(EMP)weapons seem to be effective.However,again,existing approaches present various limitations,while most of them do not examine the case of G-B-KDs.This paper,focuses on the aforementioned under-researched field,where the G-B-KD is confronted by two defensive drones.The first neutralizes and captures the kamikaze drone,while the second captures the bomb.Both defensive drones are equipped with a net-gun and an innovative algorithm,which,among others,estimates the locations of interception,using a real-world trajectory model.Additionally,one of the defensive drones is also equipped with an EMP weapon to damage the electronics equipment of the kamikaze drone and reduce the capturing time and the overall risk.Extensive simulated experiments and comparisons to state-of-art methods,reveal the advantages and limitations of the proposed approach.More specifically,compared to state-of-art,the proposed approach improves:(a)time to neutralize the target by at least 6.89%,(b)maximum number of missions by at least 1.27%and(c)total cost by at least 5.15%.

Using Improved Particle Swarm Optimization Algorithm for Location Problem of Drone Logistics Hub

Drone logistics is a novel method of distribution that will become prevalent.The advantageous location of the logistics hub enables quicker customer deliveries and lower fuel consumption,resulting in cost savings for the company’s transportation operations.Logistics firms must discern the ideal location for establishing a logistics hub,which is challenging due to the simplicity of existing models and the intricate delivery factors.To simulate the drone logistics environment,this study presents a new mathematical model.The model not only retains the aspects of the current models,but also considers the degree of transportation difficulty from the logistics hub to the village,the capacity of drones for transportation,and the distribution of logistics hub locations.Moreover,this paper proposes an improved particle swarm optimization(PSO)algorithm which is a diversity-based hybrid PSO(DHPSO)algorithm to solve this model.In DHPSO,the Gaussian random walk can enhance global search in the model space,while the bubble-net attacking strategy can speed convergence.Besides,Archimedes spiral strategy is employed to overcome the local optima trap in the model and improve the exploitation of the algorithm.DHPSO maintains a balance between exploration and exploitation while better defining the distribution of logistics hub locations Numerical experiments show that the newly proposed model always achieves better locations than the current model.Comparing DHPSO with other state-of-the-art intelligent algorithms,the efficiency of the scheme can be improved by 42.58%.This means that logistics companies can reduce distribution costs and consumers can enjoy a more enjoyable shopping experience by using DHPSO’s location selection.All the results show the location of the drone logistics hub is solved by DHPSO effectively.

基于Raspberry Pi及Drone Kit的无人机飞行控制教学实践应用

基于开源硬件Raspberry Pi,Pixhawk及开源软件Drone Kit,设计了高校无人机飞行控制教学实践课程。课程中使用Raspberry Pi作为无人机的机载计算机与Pixhawk飞控芯片协同工作,通过使用MAVLink协议通信。学生在实践操作中需要掌握硬件各接口的功能属性进行无人机组装,在调测环境下,基于Drone Kit开发无人机飞行控制程序。所提的实践课程可以提高学生的动手实践能力,新环境下的创新应用能力能加深对飞行控制技术的理论理解。

Drone Usage in Civil Engineering—A Case Study of the Pristina-Gjilan Highway

The use of drones in construction engineering has gained increasing attention in recent years due to its potential to revolutionize the industry. Drones, offer the ability to capture high-resolution aerial imagery and collect data that was previously difficult or impossible to obtain. The integration drones in construction engineering presents opportunities for accurate data collection, analysis and visualization, which can improve decision-making processes and improve project outcomes. For example, drones equipped with GIS technology can be used to capture high-resolution aerial images of construction sites, allowing engineers to monitor progress, identify potential issues, and make informed adjustments as needed. By harnessing drones, civil engineers in the civil engineering field can potentially optimize project planning, design and execution while minimizing risks and costs. The work of this topic examines the case of the use of Drones combined with GIS in construction engineering. During this study, aerial photography of a certain segment of the Pristina-Gjilan Highway was taken. The results generated by the processing of aerial photos have been compared with the project. However, further research is needed to fully understand the capabilities and limitations of these technologies in this specific context, as well as to explore any potential challenges and barriers to their widespread adoption.

基于YOLOX-drone的反无人机系统抗遮挡目标检测算法

为解决现实场景下无人机目标被部分遮挡,导致不易检测问题,本文提出了基于YOLOX-S改进的反无人机系统目标检测算法YOLOX-drone.首先,建立无人机图像数据集;其次,搭建YOLOX-S目标检测网络,在此基础上引入坐标注意力机制,来增强无人机的目标图像显著度,突出有用特征抑制无用特征;然后,再去除特征融合层中自下而上的路径增强结构,减少网络复杂度,并设计了自适应特征融合网络结构,增强有用特征的表达能力,抑制干扰,提升检测精度.在DUT-AntiUAV数据集上的测试结果表明:YOLOX-drone与YOLOX-S、YOLOv5-S和YOLOX-tiny相比,平均准确率(IoU=0.5)提升了3.2%、4.7%和10.1%;在自建的无人机图像数据集上的测试结果表明:YOLOX-drone与原YOLOX-S目标检测模型相比,在无遮挡、一般遮挡、严重遮挡情况下,平均准确率(IoU=0.5)分别提高了2.4%、2.1%和6.4%,验证了改进的算法具有良好的抗遮挡检测能力.

A tree detection method based on trunk point cloud section in dense plantation forest using drone Li DAR data

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone Li DAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone Li DAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with undercanopy sections split into heights ranging from 1 to 7 m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha^(-1) and an average tree age of 42 years. Dense point cloud data were generated from the drone Li DAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m^(-2), respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy(F1-Score=0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.

Classification of birds and drones by exploiting periodical motions in Doppler spectrum series

With the rapidly growing abuse of drones, monitoring and classification of birds and drones have become a crucial safety issue. With similar low radar cross sections(RCSs), velocities, and heights, drones are usually difficult to be distinguished from birds in radar measurements. In this paper, we propose to exploit different periodical motions of birds and drones from highresolution Doppler spectrum sequences(DSSs) for classification.This paper presents an elaborate feature vector representing the periodic fluctuations of RCS and micro kinematics. Fed by the Doppler spectrum and feature sequence, the long to short-time memory(LSTM) is used to solve the time series classification.Different classification schemes to exploit the Doppler spectrum series are validated and compared by extensive real-data experiments, which confirms the effectiveness and superiorities of the proposed algorithm.

Efficient Remote Identification for Drone Swarms

With the advancement of unmanned aerial vehicle(UAV)technology,the market for drones and the cooperation of many drones are expanding.Drone swarms move together in multiple regions to perform their tasks.A Ground Control Server(GCS)located in each region identifies drone swarmmembers to prevent unauthorized drones from trespassing.Studies on drone identification have been actively conducted,but existing studies did not consider multiple drone identification environments.Thus,developing a secure and effective identification mechanism for drone swarms is necessary.We suggested a novel approach for the remote identification of drone swarms.For an efficient identification process between the drone swarm and the GCS,each Reader drone in the region collects the identification information of the drone swarmand submits it to the GCS for verification.The proposed identification protocol reduces the verification time for a drone swarm by utilizing batch verification to verify numerous drones in a drone swarmsimultaneously.To prove the security and correctness of the proposed protocol,we conducted a formal security verification using ProVerif,an automatic cryptographic protocol verifier.We also implemented a non-flying drone swarmprototype usingmultiple Raspberry Pis to evaluate the proposed protocol’s computational overhead and effectiveness.We showed simulation results regarding various drone simulation scenarios.

MF2-DMTD: A Formalism and Game-Based Reasoning Framework for Optimized Drone-Type Moving Target Defense

Moving-target-defense(MTD)fundamentally avoids an illegal initial compromise by asymmetrically increasing the uncertainty as the attack surface of the observable defender changes depending on spatial-temporal mutations.However,the existing naive MTD studies were conducted focusing only on wired network mutations.And these cases have also been no formal research on wireless aircraft domains with attributes that are extremely unfavorable to embedded system operations,such as hostility,mobility,and dependency.Therefore,to solve these conceptual limitations,this study proposes normalized drone-type MTD that maximizes defender superiority by mutating the unique fingerprints of wireless drones and that optimizes the period-based mutation principle to adaptively secure the sustainability of drone operations.In addition,this study also specifies MF2-DMTD(model-checkingbased formal framework for drone-type MTD),a formal framework that adopts model-checking and zero-sum game,for attack-defense simulation and performance evaluation of drone-type MTD.Subsequently,by applying the proposed models,the optimization of deceptive defense performance of drone-type MTD for each mutation period also additionally achieves through mixed-integer quadratic constrained programming(MIQCP)and multiobjective optimization-based Pareto frontier.As a result,the optimal mutation cycles in drone-type MTD were derived as(65,120,85)for each control-mobility,telecommunication,and payload component configured inside the drone.And the optimal MTD cycles for each swarming cluster,ground control station(GCS),and zone service provider(ZSP)deployed outside the drone were also additionally calculated as(70,60,85),respectively.To the best of these authors’knowledge,this study is the first to calculate the deceptive efficiency and functional continuity of the MTD against drones and to normalize the trade-off according to a sensitivity analysis with the optimum.

A Drone-Based Blood Donation Approach Using an Ant Colony Optimization Algorithm

This article presents an optimized approach of mathematical techniques in themedical domain by manoeuvring the phenomenon of ant colony optimization algorithm(also known as ACO).A complete graph of blood banks and a path that covers all the blood banks without repeating any link is required by applying the Travelling Salesman Problem(often TSP).The wide use promises to accelerate and offers the opportunity to cultivate health care,particularly in remote or unmerited environments by shrinking lab testing reversal times,empowering just-in-time lifesaving medical supply.

Drone for Dynamic Monitoring and Tracking with Intelligent Image Analysis

Traditional monitoring systems that are used in shopping malls or com-munity management,mostly use a remote control to monitor and track specific objects;therefore,it is often impossible to effectively monitor the entire environ-ment.Whenfinding a suspicious person,the tracked object cannot be locked in time for tracking.This research replaces the traditionalfixed-point monitor with the intelligent drone and combines the image processing technology and automatic judgment for the movements of the monitored person.This intelligent system can effectively improve the shortcomings of low efficiency and high cost of the traditional monitor system.In this article,we proposed a TIMT(The Intel-ligent Monitoring and Tracking)algorithm which can make the drone have smart surveillance and tracking capabilities.It combined with Artificial Intelligent(AI)face recognition technology and the OpenPose which is able to monitor the phy-sical movements of multiple people in real time to analyze the meaning of human body movements and to track the monitored intelligently through the remote con-trol interface of the drone.This system is highly agile and could be adjusted immediately to any angle and screen that we monitor.Therefore,the system couldfind abnormal conditions immediately and track and monitor them automatically.That is the system can immediately detect when someone invades the home or community,and the drone can automatically track the intruder to achieve that the two significant shortcomings of the traditional monitor will be improved.Experimental results show that the intelligent monitoring and tracking drone sys-tem has an excellent performance,which not only dramatically reduces the num-ber of monitors and the required equipment but also achieves perfect monitoring and tracking.

Platform Strategies of the Chinese Commercial Drone Manufacturer:A Theoretical and Empirical Study of Ecosystem Development

From the perspective of the business ecosystem,this paper analyzes the competitive advantage and platform strategy of Da-Jiang Innovations Science and Technology Co.,Ltd.(DJI),a Chinese commercial drone manufacturer that is currently leading the global commercial drone industry.DJI was established in 2006 and developed the industry’s first core components such as drone control system.DJI released its“Phantom”in the United States in 2013 and occupied the global commercial drone market accounting for 70%in a short period of time.Its market share has maintained its superiority till present.During the inflection transition from the formation of a new ecosystem to expansion,DJI has defended and strengthened its core technology through a strong containment strategic action of competing with GoPro;therefore,DJI has obtained its hub position of multiple markets with bargaining power.In addition,DJI has entered the surrounding markets of corporate market from the general consumer market,and instilled its own product standards&design standards(reference design).Furthermore,it has stimulated and revitalized coexisting companies,individual&corporate customers for expanding the ecosystem of drone industry.

Geographic Drone-based Route Optimization Approach for Emergency Area Ad-Hoc Network

Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.

农村电商物流下无人机与车辆协同配送路径优化研究

无人机配送正在成为解决物流末端配送难题的重要手段。无人机与车辆协同配送模式克服了无人机配送能力不足、安全性不高的弊端,是无人机参与配送的重要途径之一。针对农村电商物流“最后一公里”配送难、配送贵问题,考虑无人机与车辆协同方式、多无人机多包裹配送等约束,以配送成本最小化为目标构建混合整数规划模型并提出一种两阶段算法对无人机与车辆协同配送路径优化问题进行求解。第一阶段通过带约束的自适应K-means算法确定车辆停靠点范围,第二阶段设计爬山算子与分裂算子改进遗传算法,求得无人机与车辆配送路径。最后,通过算例实验验证了模型和算法的可行性与有效性。研究成果有望为农村电商物流末端配送降本增效提供新思路和参考价值。

基于无人机技术的露天矿山越界开采监测与评估方法

高效快速评估露天矿山越界开采对开展矿产卫片执法检查和推进严格保护矿产资源策略、推动区域高质量发展具有重要意义。基于无人机飞行技术、传感器技术、通信技术和影像处理技术,结合卫星遥感、地理信息系统应用,采集无人机航测数据与飞控系统数据,快速获取了疑似违法矿山二维场景图像(SIM),动态飞行展示矿区全貌和精细地物;进一步建立了矿山三维数字高程模型(DEM),定性研判违法类型与性质;针对需求建立了矿山的三维数字表面模型(DSM),定量估算违法采矿资源量;利用四维数字表面模型(DSM),查明矿山历史时期的违法过程,测算违法开采的资源量。无人机航测技术实现区域矿产资源开发秩序的快速监测和精细评估,为矿产资源的卫片执法检查工作提供理论支撑和决策依据。在江南某地进行试验的实例表明,基于无人机技术的露天矿山越界开采监测与评估方法技术具有时效性强、操作便捷、精细高效等优势,在支撑服务自然资源领域的卫片执法检查工作方面可以更加广泛地应用。

考虑动态能耗的无人机采收菠萝田间收集点优化配置研究

为了深化无人机在田间菠萝采收和运输过程中的应用,探索无人机悬停放置菠萝到收集箱中的技术方案。无人机的续航能力限制菠萝采收工作的完成,而对续航能力影响最大的因素是无人机的载重,提出在田间设立菠萝收集点,并确定收集点的最优设立数量及位置,以确保菠萝采收工作的有效完成。在研究方法上,基于K-means算法寻找最佳收集点位置及分组方式,建立无人机采收菠萝的能耗及成本模型,通过编写程序对模型进行求解,并以无人机续航能力为约束条件,确定了无人机采收菠萝的最大覆盖范围和菠萝采摘数量。通过比较不同设立数量和位置的收集点方案,得出综合最优的结果。在不更换无人机电池的情况下,1台农业无人机采收菠萝约480个,采收面积约44m^(2)。随着收集点设立数量增加,无人机总路程和总能耗呈稳步下降状态,总成本呈稳步上升状态,分析发现667 m^(2)菠萝地设立15个固定位置的收集点时总能耗、总路程和总成本达到均衡最优。无人机采收菠萝的收集点配置优化方案,可为应用无人机进行田间作物采收及运输时收集点的数量和选址提供建设性意见。

基于雷达自动目标识别技术的反无人机雷达

面对“低慢小”(Low Slow Small, LSS)无人机威胁挑战,反无人机雷达的关键作用日益凸显。在反无人机系统(Countering-Unmanned Aerial Systems, C-UAS)技术中,反无人机雷达作为C-UAS的核心传感器,承担着关键任务,包括探测和导引等。尽管其重要性显而易见,反无人机雷达设计仍然存在概念上的不清晰,例如如何定义LSS无人机目标,以及为何众多反无人机雷达难以探测无人机等问题。通过探讨雷达自动目标识别(Automatic Target Recognition, ATR)技术在反无人机雷达中的应用,从目标特性和探测技术2个方面明确了关键问题。强调了雷达探测的独立过程,将其分为“信号检测”和“目标识别”,并指出反无人机雷达的主要探测对象是LSS无人机目标。ATR性能等级被明确定义,涵盖了“Detection探测”“Classification分类”“Identification识别”“Description描述”4个等级。整合ATR功能显著提升了无人机的探测距离和识别能力,推动了反无人机雷达系统的性能升级。

基于模型微调的空中无人机小样本目标识别方法

空中无人机目标识别是现代军事、航空领域的迫切需求,由于目前无人机的功能和种类繁多,对于新机型很难采集大量的无人机样本用于训练目标识别模型;针对该问题,提出了一种基于模型微调的空中无人机小样本目标识别方法;方法以Faster R-CNN为基础架构,首先采用具有大量标记样本的常见机型数据预训练Faster R-CNN模型;然后将基础架构最后的分类层替换为余弦度量,构建联合新机型与常见机型的小样本平衡数据集以较小的学习率微调分类层;实验结果表明,在标记样本数量为5、10和50的情况下,基于模型微调的小样本目标识别模型的mAP分别为88.6%,89.2%和90.8%,能够满足空中无人机小样本目标识别任务需求,且优于其它小样本目标识别方法。

车辆与无人机协同配送路径规划问题研究进展

随着民用无人机技术的不断发展,无人机速度快、通行能力强等优势更加凸显。且无人机已经被一些企业应用到了物流配送当中。最近,一种车辆与无人机协同进行配送的模式在物流运输领域得到了广泛关注,国内外的学者针对车辆与无人机协同配送路径规划问题进行了大量的研究。首先,本文从优化目标以及约束两个方面对车辆与无人机协同配送路径规划模型进行了梳理。然后,对现有的车辆与无人机协同配送路径规划算法进行了分类总结。最后,探讨了车辆与无人机协同配送路径规划问题相关的研究热点与方向。