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
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
Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and h...Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems.展开更多
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(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.
基金supported by the National Natural Science Foundation of China(Grant Nos.60975072,60604009)the Aeronautical Science Foundation of China(Grant No.2008ZC01006)+2 种基金Beijing NOVA Program Foundation(Grant No.2007A017)the Fundamental Research Funds for the Central Universities(Grant No.YWF-10-01-A18)the Program for New Century Excellent Talents in University of China(Grant No.NCET-10-0021)
文摘Bio-inspired intelligence is in the spotlight in the field of international artificial intelligence,and unmanned combat aerial vehicle(UCAV),owing to its potential to perform dangerous,repetitive tasks in remote and hazardous,is very promising for the technological leadership of the nation and essential for improving the security of society.On the basis of introduction of bioinspired intelligence and UCAV,a series of new development thoughts on UCAV control are proposed,including artificial brain based high-level autonomous control for UCAV,swarm intelligence based cooperative control for multiple UCAVs,hy-brid swarm intelligence and Bayesian network based situation assessment under complicated combating environments, bio-inspired hardware based high-level autonomous control for UCAV,and meta-heuristic intelligence based heterogeneous cooperative control for multiple UCAVs and unmanned combat ground vehicles(UCGVs).The exact realization of the proposed new development thoughts can enhance the effectiveness of combat,while provide a series of novel breakthroughs for the intelligence,integration and advancement of future UCAV systems.
