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.

Air-combat behavior data mining based on truncation method

This paper considers the problem of applying data mining techniques to aeronautical field.The truncation method,which is one of the techniques in the aeronautical data mining,can be used to efficiently handle the air-combat behavior data.The technique of air-combat behavior data mining based on the truncation method is proposed to discover the air-combat rules or patterns.The simulation platform of the air-combat behavior data mining that supports two fighters is implemented.The simulation experimental results show that the proposed air-combat behavior data mining technique based on the truncation method is feasible whether in efficiency or in effectiveness.

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.

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.

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.

Cooperative Decision-Making for Multiple UAVs Autonomous Confrontation

This paper presents a rule-based framework for addressing decision-making problems within the context of the "UI-STRIVE"Competition.First,two distinct autonomous confrontation scenarios are described:autonomous air combat and cooperative interception.Second,a State-Event-Condition-Action(SECA)decision-making framework is developed,which integrates thefinite state machine and event-condition-action frameworks.This framework provides three products to describe rules,i.e.the SECA model,the SECA state chart,and the SECA rule description.Third,the situation assessment and target assignment during autonomous air combat are investigated,and the mathematical models are established.Finally,the decisionmaking model's rationality and feasibility are verified through data simulation and analysis.

基于模糊综合评判法的空战多目标威胁评估

在综合分析目标空战能力和战场态势的基础上,提出一种多目标威胁评估方法。首先考虑了空战客观态势信息和决策者主观偏好信息,然后采用模糊综合评判法,建立目标与准则的模糊关系,并将其函数量化为评判矩阵。基于该模糊集合最大隶属度、最小隶属度和加权隶属度,选用3种评判函数进行二级综合威胁评估。实例计算表明,该方法可以有效地解决目标威胁评估和排序问题,而且可得出更加可靠的评估结果。

空战格斗决策研究

基于敌对的两机空战背景,利用不断更新的威胁态势作为飞行员的决策依据,采用影响图分析法描述空战飞行员的决策过程,建立了空战飞行员的决策模型.其特点是空战威胁态势的评估及决策方式易于根据飞行员的经验和偏好进行描述与调整.仿真结果表明,利用所建立的空战决策模型可以有效地导引战机格斗,决策过程符合空战格斗理念.

基于区间数理论的空战目标威胁评估

针对空战目标威胁评估问题,提出了一种新的基于区间数理论的评估方法。在分析了现有的目标威胁评估方法中距离威胁模型存在不足的基础上,提出了改进的距离威胁模型。给出了区间数特征向量法求解威胁指标权重的计算步骤,建立了基于区间数理论的空战目标威胁评估的数学模型。最后给出了仿真算例,仿真结果表明该方法是合理和有效的。

协同空战的最小共享信息集

首先提出了最小共享信息集的概念。然后 ,提出了作战飞机的智能系统模型。从它入手 ,分析了协同空战的信息 ,提出了确定协同空战的最小共享信息集的事件法 ,并应用信息论证明了事件法的确能够减少通信的信息量。在上述基础上 ,确定了超视距空对空协同作战的最小共享信息集 ;

基于混合型多属性决策方法的目标威胁评估

针对目标属性信息存在不确定性的特点,通过对目标属性选取及其权重确定方法的全面分析,采用混合精确实数型指标和模糊区间型指标的决策模型,评价目标多属性并对目标威胁进行评估。最后给出仿真实例,仿真结果表明该方法简单、可靠,为目标威胁评估提供了又一有效途径。

基于排列法的目标威胁评估模型

目标威胁评估是一个推理、决策的行为,结合现代防空作战的特点和指挥自动化系统的工作过程,分析了影响目标威胁评估的几个重要因素,应用多属性决策中的一种改进排列法建立了数学模型,验证了其可行性。

网络化防空导弹体系生存能力的评估与权衡

针对现代防空导弹网络化作战特点,基于网络化防空导弹体系的概念与内涵,研究并分析了其生存能力的影响因素及相关计算公式,提出了一种评估网络化防空导弹体系生存能力的综合方法。考虑到网络化防空导弹体系生存能力的提高必须以费用为代价,提出了一种网络化防空导弹体系生存能力/作战费用综合权衡方法。通过仿真计算,分析了影响生存能力的主要因素,验证了所提方法的正确性。研究成果对网络化防空导弹体系生存能力的设计与提高具有积极的意义。

基于滚动时域控制的战斗机空战机动决策

采用滚动时域控制(Receding Horizon Control,RHC)方法对空战机动决策进行建模研究。首先,基于空战态势和战斗机作战能力给出探测优势和攻击优势的集合描述,并建立定量评估函数,得到机动决策的目标集;其次,综合考虑战斗机的战术优势、机动时间和控制损耗构建了决策指标函数;然后,基于直接多重打靶法给出其数值求解方法。仿真结果表明:基于RHC的机动决策方法能够在规避目标威胁的情况下,有效地达成攻击条件。

作战效能分析的近似计算法在方案论证中的应用

介绍了作战效能分析近似计算法中的一种一对数法，并通过对数法在方案论证中应用的一个具体算例详细介绍了该方法的计算过程。

多机协同多目标分配战术决策研究

神经网络在协同空战决策领域有着非常好地运用的前景,本文引入威胁指数法来计算协同优先权,并将其作为神经网络的教师值.最后给出了仿真算例,计算结果表明,通过文中介绍的方法改进的协同优先权算法可以更有针对性地完成战术决策任务.

基于改进的排列法的目标威胁评估与排序模型

结合现代防空作战的特点和指挥自动化系统的工作过程,分析了影响目标威胁评估的几个重要因素,应用多属性决策中的一种改进排列法建立了数学模型,验证了其可行性。该方法不必将各属性值精确量化,只需知道各属性间的大小关系及权重就可以进行威胁评估与排序。

基于匈牙利融合遗传算法的多无人机不平衡目标分配

基于无人机和目标之间的态势,研究不平衡目标任务分配建模和求解问题。首先,分析空战战场下无人机和目标之间的态势,提出一种某时刻的态势评估方法,建立多无人机不平衡目标分配模型;然后,用匈牙利遗传算法的目标分配算法求解该模型;最后,构建仿真算例进行仿真实验。仿真结果验证了所提算法的有效性,同时也表明了匈牙利遗传算法能够提升模型的求解效率。

蒙特卡洛法的截击空战仿真

针对具体空战环节,应用蒙特卡洛方法仿真了截击空战的全过程,提出了截击空战仿真的程序逻辑方案;并结合具体实例,得出仿真数据,定量分析了空战毁伤概率,进而分析了截击空战的作战效能。仿真结果比较符合实际空战情况,具有一定的创新性和实际应用价值,其分析问题解决问题的方法和过程对解决类似的问题有一定的借鉴意义。