Cooperative maneuver decision making for multi-UAV air combat based on incomplete information dynamic game

Cooperative autonomous air combat of multiple unmanned aerial vehicles(UAVs)is one of the main combat modes in future air warfare,which becomes even more complicated with highly changeable situation and uncertain information of the opponents.As such,this paper presents a cooperative decision-making method based on incomplete information dynamic game to generate maneuver strategies for multiple UAVs in air combat.Firstly,a cooperative situation assessment model is presented to measure the overall combat situation.Secondly,an incomplete information dynamic game model is proposed to model the dynamic process of air combat,and a dynamic Bayesian network is designed to infer the tactical intention of the opponent.Then a reinforcement learning framework based on multiagent deep deterministic policy gradient is established to obtain the perfect Bayes-Nash equilibrium solution of the air combat game model.Finally,a series of simulations are conducted to verify the effectiveness of the proposed method,and the simulation results show effective synergies and cooperative tactics.

Extracting Decision Rules for Cooperative Team Air Combat Based on Rough Set Theory

In order to reduce redundant features in air combat information and to meet the requirements of real-time decision in combat, rough set theory is introduced to the tactical decision analysis in cooperative team air combat. An algorithm of attribute reduction for extracting key combat information and generating tactical rules from given air combat databases is presented. Then, considering the practical requirements of team combat, a method for reduction of attribute-values under single decision attribute is extended to the reduction under multi-decision attributes. Finally, the algorithm is verified with an example for tactical choices in team air combat. The results show that, the redundant attributes in air combat information can be reduced, and that the main combat attributes, i.e., the information about radar command and medium-range guided missile, can be obtained with the algorithm mentioned above, moreover, the minimal reduced strategy for tactical decision can be generated without losing the result of key information classification. The decision rules extracted agree with the real situation of team air combat.

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.

Target distribution in cooperative combat based on Bayesian optimization algorithm

Target distribution in cooperative combat is a difficult and emphases. We build up the optimization model according to the rule of fire distribution. We have researched on the optimization model with BOA. The BOA can estimate the joint probability distribution of the variables with Bayesian network, and the new candidate solutions also can be generated by the joint distribution. The simulation example verified that the method could be used to solve the complex question, the operation was quickly and the solution was best.

Formation and adjustment of manned/unmanned combat aerial vehicle cooperative engagement system

Manned combat aerial vehicles （MCAVs）, and un-manned combat aerial vehicles （UCAVs） together form a cooper-ative engagement system to carry out operational mission, whichwill be a new air engagement style in the near future. On the basisof analyzing the structure of the MCAV/UCAV cooperative engage-ment system, this paper divides the unique system into three hi-erarchical levels, respectively, i.e., mission level, task-cluster leveland task level. To solve the formation and adjustment problem ofthe latter two levels, three corresponding mathematical modelsare established. To solve these models, three algorithms calledquantum artificial bee colony （QABC） algorithm, greedy strategy（GS） and two-stage greedy strategy （TSGS） are proposed. Finally,a series of simulation experiments are designed to verify the effec-tiveness and superiority of the proposed algorithms.

A Multi-UCAV cooperative occupation method based on weapon engagement zones for beyond-visual-range air combat

Recent advances in on-board radar and missile capabilities,combined with individual payload limitations,have led to increased interest in the use of unmanned combat aerial vehicles(UCAVs)for cooperative occupation during beyond-visual-range(BVR)air combat.However,prior research on occupational decision-making in BVR air combat has mostly been limited to one-on-one scenarios.As such,this study presents a practical cooperative occupation decision-making methodology for use with multiple UCAVs.The weapon engagement zone(WEZ)and combat geometry were first used to develop an advantage function for situational assessment of one-on-one engagement.An encircling advantage function was then designed to represent the cooperation of UCAVs,thereby establishing a cooperative occupation model.The corresponding objective function was derived from the one-on-one engagement advantage function and the encircling advantage function.The resulting model exhibited similarities to a mixed-integer nonlinear programming(MINLP)problem.As such,an improved discrete particle swarm optimization(DPSO)algorithm was used to identify a solution.The occupation process was then converted into a formation switching task as part of the cooperative occupation model.A series of simulations were conducted to verify occupational solutions in varying situations,including two-on-two engagement.Simulated results showed these solutions varied with initial conditions and weighting coefficients.This occupation process,based on formation switching,effectively demonstrates the viability of the proposed technique.These cooperative occupation results could provide a theoretical framework for subsequent research in cooperative BVR air combat.

Task assignment under constraint of timing sequential for cooperative air combat

According to the previous achievement, the task assignment under the constraint of timing continuity for a cooperative air combat is studied. An extensive task assignment scenario with the background of the cooperative air combat is proposed. The utility and time of executing a task as well as the continuous combat ability are defined. The concept of the matching method of weapon and target is modified based on the analysis of the air combat scenario. The constraint framework is also redefined according to a new objective function. The constraints of timing and continuity are formulated with a new method, at the same time, the task assignment and integer programming models of the cooperative combat are established. Finally, the assignment problem is solved using the integrated linear programming software and the simulation shows that it is feasible to apply this modified model in the cooperative air combat for tasks cooperation and it is also efficient to optimize the resource assignment.

Cooperative Path Dynamic Planning Model of UCAV Team Based on Global Optimization Method

Cooperative path dynamic planning of a UCAV （unmanned combat air vehicle） team not only considers the capability of task requirement of single UCAV, but also considers the cooperative dynamic connection among members of the UCAV team. A cooperative path dynamic planning model of the UCAV team by applying a global optimization method is discussed in this paper and the corresponding model is built and analyzed. By the example simulation, the reasonable result acquired indicates that the model could meet dynamic planning demand under the circumstance of membership functions. The model is easy to be realized and has good practicability.

有人/无人系统协同作战任务规划方法:综述与展望

综述了有人/无人系统协同作战任务规划方法。首先介绍了研究背景,从有人/无人系统的人机关系和任务规划流程2个方面对问题进行了分析,进而引出任务理解、任务分解、任务分配、行动序列生成和方案评估等5个方面的关键技术,并分别对相关研究进行全面综述。最后综合对有人/无人系统协同作战任务规划的研究现状进行了总结和分析,并对该领域的未来提出展望。

俄罗斯预警机装备发展过程研究及展望

俄罗斯预警机继承于苏联,受技术水平、政治和经济等多重因素影响,其现役装备能力已落后于世界先进水平.首先通过对苏联/俄罗斯研发的主要预警机装备情况进行梳理;然后回顾了俄罗斯预警机装备发展历程,对其装备能力现状、技术特点进行了总结;最后结合俄乌冲突中两架A-50预警机被击落事件,对俄罗斯预警机装备未来发展趋势进行展望.

基于Multi-Agent的无人机集群体系自主作战系统设计

针对无人集群自主作战体系设计中的关键问题,提出基于Multi-Agent的无人集群自主作战系统设计方法。建立无人集群各节点的Agent模型及其推演规则;对于仿真系统模块化和通用化的需求,设计系统互操作式接口和无人集群自主作战的交互关系;开展无人集群系统仿真推演验证。仿真结果表明,所提设计方案不仅能够有效开展并完成自主作战网络生成-集群演化-效能评估的全过程动态演示验证,而且能够通过重复随机试验进一步评估无人集群的协同作战效能,最后总结了集群协同作战的策略和经验。

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.

有人/无人机协同空战研究进展综述

有人/无人机协同空战是未来空战的重要发展方向,有人机和无人机协同运用、优势互补,形成灵活高效的作战体系,将对未来空战效能的提升发挥巨大作用。首先,阐述了近年来美军作战概念的发展演进,分析了美军有人/无人机协同作战的相关项目发展,提炼总结了有人/无人机协同空战的特点;其次,以穿透性制空作战为场景,分析了有人/无人机协同空战的具体运用和作战流程;最后,从航迹规划、空战决策、编队控制3个方面对有人/无人机协同空战的关键技术和研究现状进行梳理和分析,并对未来有人/无人机协同空战的研究发展方向进行了总结和展望。

舰载电子战飞机与无人机协同作战视图研究

为更加高效帮助航母编队指挥所和指挥员精准判断战场态势,进而作出最佳决策,从战术运用角度分析未来舰载电子战飞机与无人机协同实施电子进攻行动的优势,通过借鉴美军DoDAF(department of defense architecture framework)体系结构框架设计思路,启发构建“五视图体系”,用以清晰呈现各参战平台在重要时间节点的具体行动及内在联系。该研究对于未来进一步优化我航母编队遂行作战任务流程具有较强的现实意义和理论价值。

有人/无人机协同空战任务规划技术

随着“分布式作战”“马赛克战”等新型作战概念的提出,无人机作为新型空战力量,依靠其灵活性、机动性、互操作性和低廉的成本,有效地弥补了有人机在空战中的作战“盲区”,将其纳入现代空战的任务规划体系,与有人机协同配合执行空战任务,会对空战结果产生巨大的影响。研究了基于数据的空战离线任务规划技术和基于任务的空战在线任务规划技术,并基于OODA环对有人/无人机协同空战任务规划技术进行了综述,结合该技术在测试进程和实际作战应用中存在的不足,展望了该技术未来的发展趋势,对下一步开展有人/无人机协同空战任务规划研究提供借鉴参考。

基于多智能体的指挥控制系统协同作战模型

针对传统线性机械的各种科学理论方法所开发的指挥控制系统作战模型对复杂战场环境的分析、处理和计算与实际情况存在很大误差的问题,提出一种基于多智能体(agent)的指挥控制系统协同作战模型。构建了基于多agent的作战建模仿真方法和多agent的作战模型建模流程,制定了作战想定;针对现阶段的多agent作战仿真模型太过简单、仿真模型内涉及的智能体种类太少的问题,扩充了作战仿真模型中agent的种类,强调了各个智能体之间的相互关系;基于Anylogic开发平台,创建一种新的复杂多智能体协同作战系统模型,模拟真实环境进行多智能体协同作战。实验结果表明,新构建的指挥控制系统多智能体作战模型中智能体种类更加全面,互相协同作战能力显著提高,更加真实地反映出现代化战争中复杂的作战环境。

有人机/无人机协同作战效能的物元可拓评价

为提高有人机/无人机(MAV/UAV)的协同作战能力,提出一种基于物元可拓理论的MAV/UAV协同作战效能评价方法。该方法通过文献研究和作战体系分析,利用灰色关联与秩相关筛选过程,构建MAV/UAV协同作战效能评价指标体系;在博弈论基础上,平衡G1法权重与熵权法权重的主客观成分,完成评价指标的综合赋权;最后,结合非对称贴近度准则,建立MAV/UAV协同作战效能的物元可拓评价模型。结合作战案例进行评估与比对,得出敏感性影响因素,验证了方法的有效性和科学性,从而为MAV/UAV协同作战效能的正确评价提供了一种解决途径。

OBE理念指导下BOPPPS模式在战伤救治教学中的应用

现代战争形式的改变使伤情呈现出多样化、复杂化和严重化。战伤救治能力直接影响战场致死率、致残率以及伤员预后,因此提高我军军医的战伤救治能力尤为关键。但战伤救治不同于平时急救,除考虑伤情本身外,还需考虑战术、战争环境和救治资源等,这决定了战伤救治课程与常规医学课程本质上的不同。文章在深入分析成果导向教育(outcome based education,OBE)理念和BOPPPS[导入(bridge-in)、学习目标(objective)、预评估(pre-assessment)、参与式学习(participatory learning)、后评估(post-assessment)、总结(summary)]教学模式基础上,结合战(现)场特殊环境,以战伤救治课程教学设计和课堂实践为例,探讨课堂导入、学习目标、课堂前测、参与式学习、课堂后测和课堂总结等环节的应用,以期提高学生学习的主动性、积极性和团队协作能力,最终达到提高战伤救治水平,保障部队战斗力的目的。

基于人机混合智能的协同作战研究

体系作战是未来海上战争的主要模式,兵力协同是面向体系作战的具体手段;人机混合智能技术将人类智能和机器智能有机地结合起来,充分发挥人类和机器各自的智能优势,以提高系统整体的性能和效率,完成对海上协同作战任务的混合决策与作战;文章对现有人机混合智能决策作战的研究方法进行了论述和对比分析,并针对海战场协同作战决策系统,分析了不同模式的人机混合智能技术在海上协同作战方面的应用;在此基础上,提出了一种系统的人机混合智能协同作战方法,能够提高决策效率、增强态势感知能力和优化资源分配,为后续深入研究和完善海上智能协同作战提供了理论基础。