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 info...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.展开更多
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 co...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.展开更多
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 ou...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.展开更多
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 ...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.展开更多
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 n...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.展开更多
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 cooperativ...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.展开更多
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 dur...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.展开更多
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 member...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.展开更多
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 opt...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.展开更多
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...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。展开更多
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...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。展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61933010 and 61903301)Shaanxi Aerospace Flight Vehicle Design Key Laboratory。
文摘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.
基金Preliminary research foundation of national defense
文摘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.
基金supported by the Aeronautical Science Foundation of China(2017ZC53033)the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University(CX2020156)。
文摘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.
基金This project was supported by the Fund of College Doctor Degree (20020699009)
文摘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.
基金supported by the National Natural Science Foundation of China(61573017)the Doctoral Innovation Found of Air Force Engineering University(KGD08101604)
文摘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.
基金supported by the National Natural Science Foundation of China(61472441)
文摘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.
基金supported by the National Natural Science Foundation of China(No.61573286)the Aeronautical Science Foundation of China(No.20180753006)+2 种基金the Fundamental Research Funds for the Central Universities(3102019ZDHKY07)the Natural Science Foundation of Shaanxi Province(2020JQ-218)the Shaanxi Province Key Laboratory of Flight Control and Simulation Technology。
文摘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.
基金supported by the National Social Science Foundation of China in 2012 under Grant No. 11GJ003-074the Science Foundation of Aeronautics of China under Grant No. 20085584010
文摘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.
文摘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.
文摘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。
文摘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。