HEURISTIC PARTICLE SWARM OPTIMIZATION ALGORITHM FOR AIR COMBAT DECISION-MAKING ON CMTA

Combining the heuristic algorithm （HA） developed based on the specific knowledge of the cooperative multiple target attack （CMTA） tactics and the particle swarm optimization （PSO）, a heuristic particle swarm optimization （HPSO） algorithm is proposed to solve the decision-making （DM） problem. HA facilitates to search the local optimum in the neighborhood of a solution, while the PSO algorithm tends to explore the search space for possible solutions. Combining the advantages of HA and PSO, HPSO algorithms can find out the global optimum quickly and efficiently. It obtains the DM solution by seeking for the optimal assignment of missiles of friendly fighter aircrafts （FAs） to hostile FAs. Simulation results show that the proposed algorithm is superior to the general PSO algorithm and two GA based algorithms in searching for the best solution to the DM problem.

Air combat decision-making of multiple UCAVs based on constraint strategy games

Game theory can be applied to the air combat decision-making problem of multiple unmanned combat air vehicles(UCAVs).However,it is difficult to have satisfactory decision-making results completely relying on air combat situation information,because there is a lot of time-sensitive information in a complex air combat environment.In this paper,a constraint strategy game approach is developed to generate intelligent decision-making for multiple UCAVs in complex air combat environment with air combat situation information and time-sensitive information.Initially,a constraint strategy game is employed to model attack-defense decision-making problem in complex air combat environment.Then,an algorithm is proposed for solving the constraint strategy game based on linear programming and linear inequality(CSG-LL).Finally,an example is given to illustrate the effectiveness of the proposed approach.

Multi-target Collaborative Combat Decision-Making by Improved Particle Swarm Optimizer

A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.

Cooperative decision-making algorithm with efficient convergence for UCAV formation in beyond-visual-range air combat based on multi-agent reinforcement learning

Highly intelligent Unmanned Combat Aerial Vehicle(UCAV)formation is expected to bring out strengths in Beyond-Visual-Range(BVR)air combat.Although Multi-Agent Reinforcement Learning(MARL)shows outstanding performance in cooperative decision-making,it is challenging for existing MARL algorithms to quickly converge to an optimal strategy for UCAV formation in BVR air combat where confrontation is complicated and reward is extremely sparse and delayed.Aiming to solve this problem,this paper proposes an Advantage Highlight Multi-Agent Proximal Policy Optimization(AHMAPPO)algorithm.First,at every step,the AHMAPPO records the degree to which the best formation exceeds the average of formations in parallel environments and carries out additional advantage sampling according to it.Then,the sampling result is introduced into the updating process of the actor network to improve its optimization efficiency.Finally,the simulation results reveal that compared with some state-of-the-art MARL algorithms,the AHMAPPO can obtain a more excellent strategy utilizing fewer sample episodes in the UCAV formation BVR air combat simulation environment built in this paper,which can reflect the critical features of BVR air combat.The AHMAPPO can significantly increase the convergence efficiency of the strategy for UCAV formation in BVR air combat,with a maximum increase of 81.5%relative to other algorithms.

AIRCRAFT CONCEPT EVALUATION AND EFFECTIVENESS-BASED DECISION-MAKING

Based on effectiveness analysis , a novel method is presented for combat aircraft top-hierarchy concept evaluation and decision-making. Applying multi-criterion decision-making ( MCDM ) and analytic hierarchy process , the new method can help to overcome the limitations of existing evaluation systems and decision-make methods.The proposed method includes the following process :( 1 ) Establish a multi-criterion and multi-hierarchy evaluation attribute system by introducing combat effectiveness ;( 2 ) Assign weight to the attributes and normalize them ;( 3 ) Evaluate and decision-make top-hierarchy aircraft concept based on effectiveness to reach a satisfactory design by comprehensively applying four multi-criterion decision-making methodologies , i.e.grey correlation projection method , weighted summation method , weighted quadrature method and ideal solution decision-making method , while considering the attribute hierarchy system and the logical relations among the attributes.Finally , an example is given to indicate the validity and feasibility of the proposed method.

UAV Maneuvering Decision-Making Algorithm Based on Twin Delayed Deep Deterministic Policy Gradient Algorithm

Aiming at intelligent decision-making of unmanned aerial vehicle(UAV)based on situation information in air combat,a novelmaneuvering decision method based on deep reinforcement learning is proposed in this paper.The autonomous maneuvering model ofUAV is established byMarkovDecision Process.The Twin DelayedDeep Deterministic Policy Gradient(TD3)algorithm and the Deep Deterministic Policy Gradient(DDPG)algorithm in deep reinforcement learning are used to train the model,and the experimental results of the two algorithms are analyzed and compared.The simulation experiment results show that compared with the DDPG algorithm,the TD3 algorithm has stronger decision-making performance and faster convergence speed and is more suitable for solving combat problems.The algorithm proposed in this paper enables UAVs to autonomously make maneuvering decisions based on situation information such as position,speed,and relative azimuth,adjust their actions to approach,and successfully strike the enemy,providing a new method for UAVs to make intelligent maneuvering decisions during air combat.

无人空战指控建模仿真方法研究

针对当前无人空战仿真中,缺乏支持全流程作战指挥控制建模与仿真这一问题,结合当前无人空战仿真系统相关研究,系统梳理和研究指控模型构建方法,通过探索不同仿真层级不同类型的无人机指挥控制建模原理和方法,设计并且构建了一套适合指挥信息系统装备特点的指控模型仿真系统。通过进行符合预设约束的想定设定,对目标作战任务进行了效能评估和对比,可为无人空战相关指挥信息系统装备的论证、评估方式提供理论和现实指导。

C2SIM互操作标准发展关键问题研究

针对联合作战信息交换需求和多军兵种作战数据互联互通问题,采用指挥控制系统与仿真系统之间交互性标准(command and control systems-simulation systems interoperation,C2SIM)标准指导实现指挥控制系统、仿真系统和无人系统之间的高效互操作。概述C2SIM的地位、作用和开发概况;介绍C2SIM逻辑数据模型开发方法;从C2SIM核心本体、领域本体扩展、本体到XML Schema的转换和参考实现等方面,分析C2SIM的关键技术问题;探讨C2SIM标准在北约近年中实兵演习中的应用情况;总结C2SIM的发展和影响,为我军标准化互操作技术的构建提供方法和技术上的启发和借鉴。结果表明,该研究可有效提高军事信息系统的开发能力和质量。

指挥信息系统韧性度量评估研究

指挥信息系统是现代战争体系的中枢,度量指挥信息系统的韧性,可以评估其在面对不确定性攻击扰动下的系统性能及作战能力,更好地保障作战任务的完成。分析了指挥信息系统韧性度量指标体系。针对指挥信息系统的网络化、面临威胁种类的多样化和作战任务的动态化等特征,从网络拓扑结构性质、威胁场景试验驱动以及作战任务完成能力等不同视角,总结归纳了当前指挥信息系统韧性度量评估方法,在此基础上进行深入分析和评述。考虑指挥信息系统的特点和发展趋势,指出未来指挥信息系统韧性度量评估值得关注的研究方向。

基于作战行动特征的作战指挥行为评估方法

为对兵棋推演过程中联合作战指挥员的作战指挥行为进行客观定量评估,提出了一种基于作战行动特征的作战指挥行为评估方法。根据联合作战指挥员指挥作战形成的作战事件序列构建作战事件知识图谱,采用双层图注意力网络和ConvKB模型抽取作战行动特征,基于CatBoost算法对联合作战指挥员的作战指挥行为进行评估。实验结果表明,该方法能够对联合作战指挥员的作战指挥行为进行客观有效评估。

基于隐蔽手势交互技术的弹群指挥控制方法

针对城市作战场景下,班排作战小组协同无人装备遂行作战任务时信息传递的隐蔽式需求,提出了一种基于表面肌电信号的隐蔽手势交互方法,并通过弹群指挥控制仿真应用实验进行了验证。开发了表面肌电信号离线和在线采集系统,提出了一种基于卷积神经网络的端到端表面肌电信号分类算法,进而形成了隐蔽手势交互范式。在此基础上,依托仿真环境构建了巡飞弹指挥控制系统,并招募了5名被试开展实验,实验结果表明,基于肌电信号的隐蔽手势交互技术在提高信息传递隐蔽性的同时,也保证了信息交互的准确性,对于提升城市作战场景下班排小组协同无人装备作战的能力具有积极意义。

未来智能空战指挥控制关键技术发展思考

聚焦未来大国博弈环境下的空战对抗的新样式、新形态和新能力,分析了空战场指挥控制面临的挑战,思考了人工智能技术对于空战智能指挥控制变革的影响。首先,总结了以战斗机平台、机载电子设备和武器装备的能力演进为主的空战发展历程,给出了空战制胜机理的演变过程;然后,分析了新质作战概念和智能化技术对于未来智能空战的影响,阐述了未来智能空战的新形态;最后,总结了对手在智能空战项目布局和能力形成上的相关工作,提出了智能空战关键技术面临的问题及其发展思路。

面向作战指挥的武器装备知识图谱本体构建

当前,在武器装备知识图谱本体构建的研究上,多是面向武器装备维修管理、武器装备体系建设进行本体构建,面向作战指挥的武器装备本体构建研究相对缺乏。鉴于此,面向作战指挥需求,提出一种基于七步法和DODAF相结合的本体构建流程框架,基于该流程框架进行了面向作战指挥的武器装备本体设计,并最终利用protégé工具进行了本体构建及展示,为后续面向作战指挥人员的知识库构建及知识应用打下基础。

危险化学品灾害事故处置思考

分析危险化学品灾害事故的特点,结合作者多年实战指挥经验,从初战、相持、决战3个阶段分析实战处置要点,从组织指挥、把握重点2个方面进行探讨。通过分析大量实战案例,对灭火剂计算估算量进行了说明,以全新的实战视野探讨危险化学品灾害事故处理方法。

面向体系仿真的蓝军舰艇编队防空作战指控建模方法

舰艇编队防空作战指挥控制是蓝军实施海上对空作战任务的关键环节,舰艇编队防空作战指控建模是蓝军海上作战体系仿真建模的重要组成部分。基于体系对抗的仿真应用背景,开展蓝军舰艇编队防空作战指控建模总体设计,采用模块化建模的方法设计资产综合管理与筹划、统一态势生成、作战指挥决策等功能模块,并参照蓝军作战指挥业务逻辑对各模块进行功能集成,构建蓝军舰艇编队防空作战指控系统模型,实验表明:所构建的蓝军舰艇编队防空作战指控模型能够实现编队防空作战指挥控制功能的模拟,并可模拟蓝军舰艇编队防空作战指挥控制流程,有效支撑体系对抗仿真需求。

一种基于OODA循环的作战能力评估模型

以作战能力为对象,OODA循环为参考、马尔科夫过程为工具,提出了一种基于OODA循环的作战能力评估模型。在模型准备上,借鉴OODA循环思想,从一般意义将作战任务的执行过程界定为顺序相连的循环程序。在模型建立上,视每运行一次OODA循环即在理论上执行了一次作战任务,多次循环后将形成关于作战任务完成程度的样本空间,从随机事件角度建立了马尔科夫过程模型。在模型解算上,给出了侦察感知、作战决策、指挥控制、火力打击能力和目标出现概率的解析解,依据作战能力的时变图和对比图,提出了相关评估建议。

面向边缘作战辅助决策的战例事件图谱构建研究

构建战例事件图谱是辅助提高边缘作战指挥决策能力的关键举措。在深入研究边缘作战、辅助决策和知识图谱等理论基础上,提出了战例事件图谱。首先描述了战例事件图谱逻辑结构,对战例事件组成要素、关联关系进行了梳理,构建了战例事件图谱知识表示模型;然后通过战例事件数据源选取、事件抽取、事件关系识别,进行了战例事件图谱构建实践,验证了模型的可行性;在此基础上,探究了战例事件图谱在情报分析、态势感知、方案拟制等阶段的应用模式和方法。

基于“情指行”一体化机制的警务指挥能力生成探析

在“专业+机制+大数据”新型警务模式和“情指行”一体化机制双重视阈下,警务指挥呈现出指挥主体多元化、指挥信息网络化、辅助决策智能化、指挥手段现代化等新特点。但是当前“情指行”一体化机制运行中“指挥”环节还存在不少问题,需要从培养新型警务指挥人才、重构警务指挥机构、变革警务指挥组训模式、完善警务指挥保障体系等方面寻求突破,深入挖掘警务指挥能力生长点,为加快形成和提升新质公安战斗力提供保障。

未来智能杀伤网构想

大国战争的战场形态朝着高动态、高烈度的方向发展,单一武器系统或其形成的杀伤链难以适应未来的战争形态。杀伤网作为一种高动态、高弹性的新型作战概念,具有极高的自适应性、自组织性和韧性,可以通过极快的反应速度完成作战编组构建和指挥控制,大幅提升决策效率。本文首先针对杀伤网这一作战概念展开研究,然后从杀伤链的产生背景、“链”到“网”的过渡、基本概念、典型案例、关键技术等方面展开分析论述,最后结合智能化技术总结了对未来杀伤网智能化的构想。

UAV cooperative air combat maneuver decision based on multi-agent reinforcement learning

In order to improve the autonomous ability of unmanned aerial vehicles(UAV)to implement air combat mission,many artificial intelligence-based autonomous air combat maneuver decision-making studies have been carried out,but these studies are often aimed at individual decision-making in 1 v1 scenarios which rarely happen in actual air combat.Based on the research of the 1 v1 autonomous air combat maneuver decision,this paper builds a multi-UAV cooperative air combat maneuver decision model based on multi-agent reinforcement learning.Firstly,a bidirectional recurrent neural network(BRNN)is used to achieve communication between UAV individuals,and the multi-UAV cooperative air combat maneuver decision model under the actor-critic architecture is established.Secondly,through combining with target allocation and air combat situation assessment,the tactical goal of the formation is merged with the reinforcement learning goal of every UAV,and a cooperative tactical maneuver policy is generated.The simulation results prove that the multi-UAV cooperative air combat maneuver decision model established in this paper can obtain the cooperative maneuver policy through reinforcement learning,the cooperative maneuver policy can guide UAVs to obtain the overall situational advantage and defeat the opponents under tactical cooperation.