Joint Task Management of Sensor and Weapon Based on Distributed Management System
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- 英文论文题名：Joint Task Management of Sensor and Weapon Based on Distributed Management System
- 论文作者：ZHANG Mingyang ; CHEN Chen
- 英文论文作者：ZHANG Mingyang ; CHEN Chen ; School of Automation ; Beijing Institute of Technology ; Key Laboratory of Intelligent Control and Decision of Complex Systems
- 年：2017
- 作者机构：School of Automation,Beijing Institute of Technology;Key Laboratory of Intelligent Control and Decision of Complex Systems;
- 英文论文关键词：distributed management system ; join task management ; time constraints ; resource constraints ; CDPSO
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：E9
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：545k
- 原文格式：D
- 会议级别：全国
摘要
In the modern war, many combat units combat collectively has become the main form. In the task management of the combat units, the calculation is large and the real-time requirement is high. To solve the problem better, a joint task management method of sensor and weapon based on distributed management system is proposed. Firstly, the distributed management system is constructed according to the target trajectory and the distribution of combat units. In the system, all the combat units are divided into several combinations, which only consist of a few combat units. As a result, the problem scale is reduced, and the real-time requirement of battlefield is met. Secondly, joint task management model of sensor and weapon is established. We consider time constraints and resource constraints to ensure the feasibility of the attack scheme, and consider the impact between sensor and weapon to achieve better combat effect. A discrete particle swarm optimization algorithm based on crossover strategy(CDPSO) is proposed to solve the model. The results show that the performance of the proposed algorithm is satisfactory in joint task management of sensor and weapon.
In the modern war, many combat units combat collectively has become the main form. In the task management of the combat units, the calculation is large and the real-time requirement is high. To solve the problem better, a joint task management method of sensor and weapon based on distributed management system is proposed. Firstly, the distributed management system is constructed according to the target trajectory and the distribution of combat units. In the system, all the combat units are divided into several combinations, which only consist of a few combat units. As a result, the problem scale is reduced, and the real-time requirement of battlefield is met. Secondly, joint task management model of sensor and weapon is established. We consider time constraints and resource constraints to ensure the feasibility of the attack scheme, and consider the impact between sensor and weapon to achieve better combat effect. A discrete particle swarm optimization algorithm based on crossover strategy(CDPSO) is proposed to solve the model. The results show that the performance of the proposed algorithm is satisfactory in joint task management of sensor and weapon.
In the modern war, many combat units combat collectively has become the main form. In the task management of the combat units, the calculation is large and the real-time requirement is high. To solve the problem better, a joint task management method of sensor and weapon based on distributed management system is proposed. Firstly, the distributed management system is constructed according to the target trajectory and the distribution of combat units. In the system, all the combat units are divided into several combinations, which only consist of a few combat units. As a result, the problem scale is reduced, and the real-time requirement of battlefield is met. Secondly, joint task management model of sensor and weapon is established. We consider time constraints and resource constraints to ensure the feasibility of the attack scheme, and consider the impact between sensor and weapon to achieve better combat effect. A discrete particle swarm optimization algorithm based on crossover strategy(CDPSO) is proposed to solve the model. The results show that the performance of the proposed algorithm is satisfactory in joint task management of sensor and weapon.
引文
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[13]D.Li,JH.Wang,QZ.Fu,ZL.Chen,The estimation of ammo wastage about 35mm twin anti-aircraft artillery fire unit based on Monte Carlo,in Journal of the Academy of Equipment Command&Technology,16(5):44-47,2005.
[14]BB.Li,GJ.He,ML.Zhang,XL.You,DW.Tian,Weapon target assignment method based on modified gravitation search algorithm,in Journal of Detection&Control,38(3):61-65,2016.
[2]XL.Yang,DS.Qiu,W.Huang and L.Peng,Two planning models and solving method of sensor/weapon-target assignment problem,in Fire Control and Command Control,37(9):18-22,26,2012.
[3]ZF.Li,XM.Li,JJ.Dai,JZ.Chen,FX.Zhang,Sensor-weapon-target assignment based on improved SWT-opt algorithm,in IEEE 2nd International Conference on Computing,Control and Industrial Engineering,2011:25-28.
[4]L.Li,H.Feng,Method of precision distribution over weapon channel in combat systems,in Chinese Research of Ship,2(4):37-40,2007.
[5]Z.Chen,Y.Zhang,TK.Li,Research on sensor and weapon coordinated control decision-making model of beyond-visual-range coordinated air combat,in Fire Control&Command Control,39(1):90-94,102,2014.
[6]ZF.Li,XM.Li,JZ.Chen,JJ.Dai,FE.Kong,Dynamic joint fire distribution method based on decentralized cooperative auction algorithm,in Fire Control&Command Control,37(11):49-52,2012.
[7]HD.Chen,HY.Wang,SZ.Wang,L.Wu,Distributed target assignment based on contract mechanism in corporative engagement,in Journal of System Simulation,21(16):5116-5119,2009.
[8]LH.Wu,The research and applications of differential evolution algorithm,Hunan University,2007.
[9]SL.Wang,WY.Chen,XF.Gu,Solving weapon-target assignment problems based on self-adaptive differential evolution algorithm,in Systems Engineering and Electronics,35(10):2115-2120,2013.
[10]RCP.Silva,RA.Lopes,ARR.Freitas,A study on self-configuration in the differential evolution algorithm,in IEEE Symposium on Differential Evolution,2014.
[11]D.Khosla,T.Nichols,Hybrid evolutionary algorithms for network-centric command and control,in Defense Transformation and Network-Centric Systems6249(1),2006.
[12]A.Shahzad,R.Ur-Rehman,An artificial intelligence based novel approach for real-time allocation of armament to hostile targets,in Proceedings of 2013 10th International Bhurban Conference on Applied Sciences and Technology,141-146,2013.
[13]D.Li,JH.Wang,QZ.Fu,ZL.Chen,The estimation of ammo wastage about 35mm twin anti-aircraft artillery fire unit based on Monte Carlo,in Journal of the Academy of Equipment Command&Technology,16(5):44-47,2005.
[14]BB.Li,GJ.He,ML.Zhang,XL.You,DW.Tian,Weapon target assignment method based on modified gravitation search algorithm,in Journal of Detection&Control,38(3):61-65,2016.