基于多智能体的无人作战平台多机协同对抗多目标任务决策方法

建立了基于多智能体的多机协同作战任务决策方法结构模型,提出了基于神经网络与证据理论的敌我双方对抗态势分析方法和基于完全信息静态博弈模型的多机协同对抗多目标任务决策方法,并进行了基于典型作战想定的多机协同对抗多目标任务决策方法仿真研究.     

多机型协同空战多目标分配战术决策仿真算法

针对多个机型能够同时使用而且空战性能差别很大的特点,研究运用多机协同作战的理论来探讨适合多机型协同空战的目标分配战术决策方法。提出仿真算法并给出不同机型搭配的目标分配结果。计算结果表明,文中介绍的方法可以满足我军高低机型搭配作战的要求,并能更有针对性地完成战术决策任务。     

基于动态非零和博弈的无人机集群协同对抗方法研究

无人机集群协同对抗是当今国内外学术研究的热点问题,动态机动决策是无人机对抗最重要的研究领域之一.本文提出了一种基于动态非零和博弈的无人机集群协同对抗决策算法.首先,确定对抗双方的决策集,并通过角度、速度和距离等机动因素构造对抗双方的态势优势.其次,对无人机集群机动可选方案进行多属性评价,进一步计算双方的动态收益矩阵,建...     

基于MILP的多无人机对敌防空火力压制

建立了基于混合整数线性规划（Mixed Integer LinearProgram,MILP）的多无人机编队对敌防空火力压制协同任务分配模型,以0-1决策变量表征无人机一任务指派关系,引入连续时间决策变量来表示任务的执行时间,并通过对决策变量之间的线性等式和不等式的数学描述,建立无人机之间和无人机执行任务之间合理的协同约束关系。采用商用软件CPLEX对模型求解,仿真验证了模型的合理性。     

现代空战机动决策研究

将影响图和对策论引入到现代空战机动决策中,提出了考虑交战双方对抗的n步连续机动决策的多级影响网对策决策模型。首先,确定现代空战飞机相关数学和运动模型;其次,将大规模群集空战转化为多个小集团作战;在此基础上,依据协同的原则,又将小集团作战转化为多个一对一空战,然后运用多级影响图对策理论解决一对一空战。空战仿真结果表明该模型的有效性。     

基于矩阵博弈的两机攻防对抗空战仿真

矩阵博弈法是空战决策的主要方法之一。考虑了7种基本机动动作,根据攻击和逃避两大基本原则,分别计算了空战双方的支付函数,建立了各自的支付矩阵,采用矩阵博弈方法对红蓝双方的空战进行了攻防对抗仿真。最后以两架F-16战斗机的空战为例进行了数值仿真验证。     

基于DE-MADDPG的多无人机协同追捕策略

针对多无人机协同对抗快速目标的追逃博弈问题,研究了多无人机的协同追捕策略。基于解耦多智能体深度确定性策略梯度算法DE-MADDPG研究了多无人机协同对抗快速目标的追捕策略,设计了多无人机协同追捕的全局奖励和局部奖励两种奖励函数,训练后的多无人机能够有效地执行协同追捕任务。通过设置快速目标的多种逃逸控制策略,仿真验证了所设计的方法能够利用追捕无人机的数量优势,通过协作完成对快速目标的协同围捕,并且通过比较,验证本文所提出的算法相比MADDPG算法更快地取得了收敛效果。     

如何实现多机协同多目标攻击的作战效能

实现多机协同多目标攻击作战效能的根本途径关键技术的应用,为此本文基所涉及的主要关键技术,数据融合,信息处理,火控决策,飞行管理,人机接口等,进行了分析和探讨。     

非对称机动能力多无人机智能协同攻防对抗

协同攻防对抗是未来军用无人机的重要作战场景。针对不同机动能力无人机群体间的攻防对抗问题，建立了多无人机协同攻防演化模型，基于多智能体强化学习理论，研究了多无人机协同攻防的自主决策方法，提出了基于执行-评判（Actor-Critic）算法的集中式评判和分布式执行的算法结构，保证算法稳定收敛的同时，提升执行效率。无人机的评判模块使用全局信息评价决策优劣引导策略学习，而执行时只需要依赖局部感知信息进行自主决策，提高了多机攻防对抗的效能。仿真结果表明，所提的多无人机强化学习方法具备较强的自进化属性，赋予了无人机一定智能，即稳定的自主学习能力，通过不断演化，能自主学习提升协同对抗的决策效能。     

基于智能微分对策的自主机动决策方法研究

提出了一种基于智能微分对策的自主机动决策系统.该系统通过预测对抗双方的未来运动状态,将双边极值问题转化为单边极值问题,以降低计算复杂程度,满足机动决策的实时性要求.为体现对抗特性,采用战术过程评价获得最优机动,战术过程评价采用逐级淘汰方法.仿真结果表明,对抗双方未来运动状态预测误差能满足机动决策的需求,机动决策结果能有效提高作战性能.     

Mission decision-making method of multi-aircraft cooperatively attacking multi-target based on game theoretic framework

Coordinated mission decision-making is one of the core steps to effectively exploit the capabilities of cooperative attack of multiple aircrafts. However, the situational assessment is an essential base to realize the mission decision-making. Therefore, in this paper, we develop a mission decision-making method of multi-aircraft cooperatively attacking multi-target based on situational assessment. We have studied the situational assessment mathematical model based on the Dempster-Shafer (D-S) evidence theory and the mission decision-making mathematical model based on the game theory. The proposed mission decision-making method of antagonized airfight is validated by some simulation examples of a swarm of unmanned combat aerial vehicles (UCAVs) that carry out the mission of the suppressing of enemy air defenses (SEAD).     

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.     

Optimal planning for a multi-debris active removal mission with a partial debris capture strategy

In this paper, a new mission model, called a multi-debris active removal mission with partial debris capture strategy, is proposed. The model assumes that a platform only captures part of the scheduled debris at a time and then releases these debris pieces to a disposal orbit. This process is then repeated until all of the scheduled debris is removed. A genetic algorithm with a multiparameter concatenated coding method is designed to optimize the plan of a multi-debris active removal mission wit...     

Decision-making of Aircraft Optimum Configuration Utilizing Multidimensional Game Theory

As multi-discipline coupling and components interference often affect the aircraft configuration decision-making and analysis during conceptual design process, this article presents an approach of multidimensional game theory based on aircraft components to deal with this problem. The idea is that the configuration decision-making process is regarded as the game for different disciplines and technologies, and the aircraft components are players. The payoff function with highest total gain means that according to the game protocols and multidimensional theory, the optimal aircraft configuration within the strategy set will be chosen. The decision-making model is applied to conceptual design process of the high altitude long endurance (HALE) unmanned aerial vehicle (UAV) based on the assessment of technological risk. The obtained optimum configuration is quite consistent with the current HALE UAV development trends. Thus, taking into account the coupling and interference factors, the multidimensional gaming model based on aircraft components will be an effective analysis method in the decision-making process of aircraft optimum configuration.     

基于深度强化学习的多无人机协同进攻作战智能规划

无人机依靠作战效费比高、灵活自主等优势逐步替代了有生力量作战,多无人机协同作战任务规划成为热点研究问题。针对传统任务规划采用的智能优化算法存在的依赖静态、低维的简单场景、机上计算较慢等不足,提出一种基于深度强化学习（DRL）的端到端的多无人机协同进攻智能规划方法。将压制敌防空作战（SEAD）任务规划过程建模为马尔科夫决策过程,建立基于近端策略优化（PPO）算法的SEAD 智能规划模型,通过两组实验验证智能规划模型的有效性和鲁棒性。结果表明：基于DRL 的智能规划方法可以实现快速、精细规划,适应未知、连续高维的环境态势,智能规划模型具有战术协同规划能力。     

自主攻击型无人机的导引技术研究

针对无人机执行攻击任务前的攻击导引问题,从导引信息的获取、导引控制策略和导引控制律三个方面进行分析。选取光电平台为侦察设备,并具体分析在此侦察设备下导引信息的获取途径。在无人机控制过程中分析比较了两种导引控制策略,指出适合自主控制的导引控制策略并加以改进。在导引控制律方面,对目前广泛使用的比例导引律进行研究,且根据导引律的指令需求在无人机模型上实现了过载控制。     

多传感器协同探测证据理论分类融合方法

随着智能化、网络化集群作战等理念和技术的兴起，精确制导武器越来越向智能化、协同化方向发展。多传感器协同探测能够针对不同的探测任务背景和作战需求，提升目标探测性能，还可以跨域整合多种探测平台。但是由于信息的不确定性等特点，使得多传感器数据直接融合可能造成决策困难。因此，在证据理论体系下对信息融合的有效性进行合理分析与度量是很有必要的。提出了一种基于Deng熵的证据理论分类融合算法，以熵减为主要思想，将证据进行分类融合。在决策过程中，将含有证据数最多的类别融合结果作为总体融合结果，避免高冲突证据的影响，提升融合结果的信息有效性。采用算例说明了所提方法不仅能够得到合理正确的结果，并且融合可靠性较高，便于决策与后续的信息处理。     

任务规划系统机动模型与航线设计

航空兵任务规划系统中飞机空对地攻击航线解算的准确性、航线生成质量和航线解算速度是衡量任务规划系统能力的重要指标。飞机空对地攻击航线设计是飞机航线规划中典型的多约束复杂机动,其在设计与使用过程中受到来自时间、空间及飞机本体的多维约束,对飞行动力学建模与航迹优化提出了较高的要求。本文利用面向对象的思想对飞机无侧滑动力学模型...     

蜂群对抗决策故障下的容错博弈控制

针对大规模分簇蜂群无人机的任务分配问题,考虑对抗场景下某些无人机由于受到敌方攻击或操控,决策规则遭到篡改,进而导致群决策行为与期望的均衡点产生偏差的决策故障。在演化矩阵博弈的框架下,运用多群体复制子动态方程对蜂群无人机和故障建模,基于李雅普诺夫函数对故障发生前后均衡点的局部渐近稳定性及其吸引域进行了分析,从而建立了自容错条件,并设计了基于激励的簇间协同容错博弈控制方法,对群决策行为偏差进行补偿,使得蜂群任务分配状态在故障下仍能达到期望的均衡点,获得理想的分工收益。