Unmanned aerial vehicle swarm mission reliability modeling and evaluation method oriented to systematic and networked mission

With the development of Unmanned Aerial Vehicle (UAV) system autonomy, network communication technology and group intelligence theory, mission execution in the form of a UAV swarm will be an important realization of future applications. Traditional single-UAV mission reliability modeling methods have been unable to meet the requirements of UAV swarm mission reliability modeling. Therefore, the UAV swarm mission reliability modeling and evaluation method is proposed. First, aimed at the interdependence among the multiple layers, a multi-layer network model of a UAV swarm is established. At the same time, based on the system having the following characteristics—using a mission chain to complete the mission and applying the connectivity of the mission network—the mission network model of a UAV swarm is established. Second, vulnerability and connectivity are selected as two indicators to reflect the reliability of the mission, and aimed at random attack and deliberate attack, vulnerability and connectivity evaluation methods are proposed. Finally, the validity and accuracy of the constructed model are verified through simulations, and the model and selected indicators can meet the reliability requirements of the UAV swarm mission. In this way, this study provides quantitative reference for UAV-swarm-related decision-making work and supports the development of UAV-swarm-related work.     

毫米波波束编码技术在无人机智能集群中的应用

毫米波波束编码技术由于其速率高、抗干扰能力强的优点被认为是无人机智能集群通信网络的重要解决方案。但在无人机智能集群的通信场景中,多种原因造成的机体不稳定抖动会使通信波束产生小角度偏转,引起通信质量的下降,从而影响无人机集群的控制与决策。针对这一问题,提出一种面向无人机集群通信的自适应波束设计方法。首先,根据传感器回传的机体波束抖动情况建立等效信道模型,随后利用量化的信道模型参数建立目标函数并获得理想的波束编码向量,在此基础上利用几何贪婪算法对其进行分解。仿真结果表明,提出的集群波束编码方法能够有效提高均值通信速率,同时相较于其他的系数分解算法,有效降低了计算复杂度。     

面向无人机蜂群的航电云多层任务调度模型

在航空作战体系中，基于航电云的无人机（UAV）蜂群作战是提高未来无人机综合作战能力的一种新模式。针对无人机蜂群作战的航电云架构，如何将云端作战任务派发到无人机且保证作战任务完成时间是其中关键。在无人机蜂群分层分簇网络结构和模块级资源虚拟化的基础上，对传统单层平台级任务调度模型进行改进，提出了一种细化到模块级的多层任务调度模型，将作战任务从云端逐层调度到无人机功能模块上执行。利用OMNeT++对无人机蜂群多层任务调度模型以及传统的单层任务调度模型分别进行仿真，云端以攻击使命组为例构建使命组集进行分配，并对任务吞吐量、消息平均端到端延时和任务完成时间进行性能对比。仿真结果表明：与平台级单层任务调度相比，在执行任务方面，模块级多层任务调度模型将单个任务平均完成时间降低了46.2%，将使命组完成时间降低了52.1%，在保证任务吞吐量的基础上具有对复杂任务更稳定的调度能力；在网络性能方面，模块级多层任务调度模型消息端到端延时更低，延时分布更集中，提高了网络消息传输的实时性。     

基于DDPG算法的无人机集群追击任务

无人机的集群化应用技术是近年来的研究热点，随着无人机自主智能的不断提高，无人机集群技术必将成为未来无人机发展的主要趋势之一。针对无人机集群协同执行对敌方来袭目标的追击任务，构建了典型的任务场景，基于深度确定性策略梯度网络（DDPG）算法，设计了一种引导型回报函数有效解决了深度强化学习在长周期任务下的稀疏回报问题，通过引入基于滑动平均值的软更新策略减少了DDPG算法中Eval网络和Target网络在训练过程中的参数震荡，提高了算法的训练效率。仿真结果表明，训练完成后的无人机集群能够较好地执行对敌方来袭目标的追击任务，任务成功率达到95%。可以说无人机集群技术作为一种全新概念的作战模式在军事领域具有潜在的应用价值，人工智能算法在无人机集群的自主决策智能化发展方向上具有一定的应用前景。     

Battery package design optimization for small electric aircraft

The increasing gross weight of electric Unmanned Aerial Vehicle (UAV) poses a challenge in practical applications. The range and endurance of the electric UAV are limited by the fixed mass of the battery package. In this work, a design optimization method for the battery package topology of small electric UAV is proposed to enhance the performance. To improve the accuracy of the method, the dynamic battery model and simplified electric component models are presented. These models are utilized by the trajectory optimization method, which takes the dynamic characteristic into consideration to calculate the aircraft performance. The direct optimal control method is used for solving the trajectory optimization problem, and this method is tested on a small blended-wing-body electric aircraft. The test result shows that the range and energy-consumption are mainly influenced by the parallel topology of the battery package, while the flight time in climb phase is more sensitive to the series topology. It is deduced that the range- and energy-optimal design points can be considered concurrently in design optimization. The work proves the feasibility of integrating the trajectory optimization and battery package design.     

Optimal video communication strategy for intelligent video analysis in unmanned aerial vehicle applications

For Unmanned Aerial Vehicles (UAV), the intelligent video analysis is a key technology in intelligent autonomous control, real-time navigation and surveillance. However, poor UAV wireless links would degrade the quality of video communication, leading to difficulties in video analysis. To meet the challenges of packet-loss and limited bandwidth in adverse UAV channel environments, this paper proposes a parameter optimization mechanism for UAV intelligent video analysis. In the proposed method, an Optimal Strategy Library (OSL) is designed to optimize the parameters for video encoding and forward error correction. Adapted to the packet-loss rate and bandwidth in practical UAV wireless network, the proposed OSL can facilitate the encoding of video sequences and the recovery of degraded videos with optimal performance. Experimental results demonstrate that the proposed solution can keep intelligent video analysis working efficiently with adverse UAV wireless links, and is capable of maximizing the inference accuracy of Multi-Object Tracking (MOT) algorithms in various scenarios.     

基于群智机理的集群防碰撞控制

在执行作战任务以及民用中,无人机集群由于具备高可靠性和高效费比的特点而受到广泛关注;随着城市低空空域的开放,作战环境的动态复杂性剧增,对无人机集群防碰撞提出了严峻的挑战。针对无人机集群执行任务时的编队个体之间防碰撞以及遭遇突发障碍时的集群防碰撞问题,提出了基于群智机理的集群防碰撞控制方法。通过引入力学概念,设计了集群内部吸引力、排斥力以及编队构型力的模型;通过集群个体间的信息共享以及类脑知识发育,构建了基于群信息共享和类脑反射的无人机防碰撞模型。仿真结果表明：所提集群控制方法提高了集群的集结效率,而且在遭遇突发障碍时能够规避动态障碍,再次完成编队重构;群智机理的引入缩短了无人机集群面临突发障碍时的反应时间。     

通信和测量受限条件下异构多UAV分布式协同目标跟踪方法

研究了通信和测量受限的异构多无人机(UAV)网络化分布式协同目标观测与跟踪问题.该分布式UAV系统采用长机一僚机异构型网络结构,以实现在电子静默和战术隐身条件下扩大探测和打击纵深.提出改进的一致性信息滤波(ICF)算法,实现通信和测量范围内各UAV节点的分布式信息融合.由于一致性算法的收敛性与网络拓扑结构的连通性密切相...     

大规模UAV编队信息交互拓扑的分级分布式生成

UAV编队信息交互拓扑的优化设计是保证UAV编队安全性和任务执行高效性的重要基础。目前队形保持下UAV编队信息交互拓扑生成算法局限于小规模编队,且优化目标单一。针对这一问题,采用了分级分簇结构扩展信息交互拓扑层级,以满足大规模场景,同时以提高编队续航能力和减少编队总通信代价为组合优化目标,提出了基于最小树形图的分级分布式领航-跟随者编队信息交互拓扑生成算法,并通过OMNeT++进行了仿真验证。实验结果表明在考虑位置误差传递迭代时,分级分布式领航-跟随者编队的总通信代价明显低于传统领航-跟随者编队,且通过周期性更新簇首,网络能耗更加均衡,提高了编队续航能力;在80架UAV编队规模下,分级分布式领航-跟随者编队生成算法能够在0.3 s内求解完成,相比于传统领航-跟随者编队算法,求解效率提高了约2.5倍;在100架UAV编队规模下,分级分布式领航-跟随者编队生成算法能够在0.4 s内求解完成,而传统领航-跟随者编队由于位置误差的传递迭代已不能保持原有队形。     

无人机参数优化自抗扰编队保持控制器设计

针对无人机编队飞行中的队形保持问题,采用结合粒子群算法的自抗扰控制技术对控制器进行设计,使响应过程具有良好的动态品质和较小的稳态误差。建立三维空间内基于相对误差的无人机编队飞行模型,并在此基础上设计了跟踪微分器、扩展状态观测器和非线性状态反馈自抗扰控制器,采用粒子群算法对控制器中部分参数进行优化整定。仿真结果表明,设计的结合粒子群算法的改进自抗扰控制器具有良好的控制性能,能以较高的精度实现无人机编队保持任务。     

Distributed bearing-based formation control of unmanned aerial vehicle swarm via global orientation estimation

Most existing formation control approaches for Unmanned Aerial Vehicle(UAV)swarm assume that global position and global coordinate frame are directly available for each agent. To extend the application domain, this paper proposes a distributed bearing-based formation control scheme, without any reliance on global position or global coordinate frame. The interactions among UAVs are described by a directed topology with two-leader structure. To address the issue of unavailable global coordinate fr...     

Robust leaderless time-varying formation control for unmanned aerial vehicle swarm system with Lipschitz nonlinear dynamics and directed switching topologies

This paper tackles the robust leaderless Time-Varying Formation(TVF) control problem for the Unmanned Aerial Vehicle(UAV) swarm system with Lipschitz nonlinear dynamics, external disturbances and directed switching topologies. In comparison with the previous achievements on formation control problems, the UAV swarm system with Lipschitz nonlinear dynamics can accomplish the pre-designed TVF while tracking a pre-given trajectory which is produced by a virtual leader UAV in the presence of externa...     

基于神经网络补偿动态逆的飞翼布局无人机姿态控制方法

将动态逆控制技术应用于飞翼式布局无人机的姿态控制回路,以适应飞翼布局无人机控制系统要求。介绍了动态逆控制器解耦控制原理,以及神经网络补偿结构的作用和设计方法,并基于无人机非线性姿态运动学和动力学模型设计了基于神经网络补偿的动态逆控制器。在强耦合、强非线性的飞翼布局无人机模型上,通过数学仿真验证了系统具有良好的动态性能和稳态特性,控制器具有很强的鲁棒性。     

一种面向航空集群的集中控制式网络部署方法

软件定义网络（SDN）的发展为机载网络构建提供了全新的设计思想。针对航空集群环境下控制平面的可扩展性问题,研究了混合层次式架构下的多控制器部署问题。首先,为实现对底层传输节点的弹性管控,通过扩展控制层级定义了本地控制器资源池。然后,为减少时间复杂度,实现本地控制器的快速部署,将传统的多控制器直接部署转化为子域划分和域内部署两个步骤,提出了基于节点密度排序的子域划分算法和基于改进多目标模拟退火的域内控制器部署算法。实验结果表明：与已有算法相比,所提算法在保证控制器的负载均衡基础上,可以有效减少网络控制链路的平均时延和平均失连率,同时时间复杂度更低,能够适用于大规模及动态网络环境下的控制器部署场景。     

无人机对雷达组网航迹欺骗综述

未来战争对体系作战的需求不仅仅是靠规模"以量取胜",更要有战术上的智谋"以活胜僵"。通过干扰敌方对我方的意图推断,甚至设计出让敌方相信的假意图,能够在双方的博弈中塑造态势,取得先手,从顶层设计和战术策略上掌握主动权。运用无人机对敌方组网雷达进行航迹欺骗就是一种有效的对抗手段,能够在实现对敌探测的同时,消耗敌方雷达组网的计算资源,干扰敌方雷达及幕后指挥员对我方作战意图的推断,同时提高己方态势感知能力和生存力。本文首先介绍了航迹欺骗干扰的概念,并以时间为序,分类梳理了国内外各研究团队在航迹欺骗领域取得的技术成果;然后针对航迹欺骗干扰的特点,详细分析了虚假航迹设计和生成、由不确定性误差导致的估计问题、延时转发策略和同源检验准则、以及动力学约束问题等4个方面的关键问题和技术难点;最后展望了航迹欺骗的应用前景和下一步研究方向。     

基于多机协作的认知无人机网络能效联合优化

针对无人机（UAV）通信网络中频谱资源紧缺的问题,构建基于认知无线电的多无人机通信网络,通过多机协作频谱感知有效探索授权频谱。提出一种基于Bisection算法的迭代算法,通过联合优化感知时间和判决门限对构建的复杂非凸问题求解,显著提高了无人机次级认知网络的能量效率（EE）。分析了无人机飞行过程中能效的变化情况,仿真结果表明,存在最优感知时间使能效获得最大值,且判决门限的选择会影响该能效最优值;提出的高能效迭代算法具有较好收敛性,有效提高了认知无人机网络的能量利用率。     

小型固定翼无人机集群综述和未来发展综述

围绕小型固定翼无人机集群这一难度高、发展快、应用前景广阔、多学科交叉的新方向,从集群系统内涵、现有典型项目、关键技术3个角度综述了国内外小型固定翼无人机集群的研究现状。在系统梳理集群系统内涵和应用优势的基础上,从集群协同模式探索、分布指挥体系构建、核心关键技术突破和集群验证等4个视角总结现有典型项目,从体系架构、通信组网、决策与规划、飞机平台、集群飞行、集群安全与集群指控等7个核心点综述了技术研究现状。最后,综合小型固定翼无人机集群中亟需解决的关键技术,展望了这一领域未来的发展趋势。     

Robust trajectory planning for UAV communication systems in the presence of jammers

Unmanned Aerial Vehicle(UAV) has emerged as a promising novel application for the Sixth-Generation(6G) wireless communication by leveraging more favorable Line-of-Sight(Lo S)propagation. However, the jamming resistance by exploiting UAV’s mobility is a new challenge in the UAV-ground communication. This paper investigates the trajectory planning problem in an UAV communication system, where the UAV is operated by a Ground Control Unit(GCU)to perform certain tasks in the presence of multiple jamm...     

Cooperative task assignment of multi-UAV system

With the rapid development of Unmanned Aerial Vehicle (UAV) technology, one of the emerging fields is to utilize multi-UAV as a team under autonomous control in a complex environment. Among the challenges in fully achieving autonomous control, Cooperative task assignment stands out as the key function. In this paper, we analyze the importance and difficulties of multi-UAV cooperative task assignment in characterizing scenarios and obtaining high-quality solutions. Furthermore, we present three promising directions for future research: Cooperative task assignment in a dynamic complex environment, in an unmanned-manned aircraft system and in a UAV swarm. Our goal is to provide a brief review of multi-UAV cooperative task assignment for readers to further explore.     

Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory

Unmanned Aerial Vehicle (UAV) swarms have been foreseen to play an important role in military applications in the future, wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults. Therefore, a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms. However, their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system. In this paper, a complex network theory-based robustness evaluation method for a UAV swarming system is proposed. This method takes into account the dynamic evolution of UAV swarms, including dynamic reconfiguration and information correlation. The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies. The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed. Finally, the validity of the proposed method is verified by conducting a case study analysis. The results can further provide some guidance and reference for the robust design, mission planning and decision-making of UAV swarms.