网络雷达对抗系统反隐身优化部署技术
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摘要
针对传统雷达反隐身检测存在的角度信息丢失、装备资源利用率不高等问题,提出了一种基于改进粒子群算法的网络雷达对抗系统反隐身优化部署技术。该技术建立了网络雷达对抗系统反隐身检测模型,在此模型基础上将任务区域进行划分和覆盖率加权,得到粒子群算法的目标函数并对收发站粒子种群进行重新部署,通过多次仿真实验得到了最优化布站数量。仿真结果表明,与标准粒子群算法相比,所提出的研究算法收敛更快,得到的加权覆盖率更高,可满足反隐身优化部署效费比需求。
Aiming at the loss of angle information and low utilization ratio of equipment resources in traditional radar anti-stealth detection,an improved Particle Swarm Optimization(PSO) algorithm for anti-stealth optimization of Network Radar Countermeasures System is proposed in this paper.Firstly,the anti-stealth detection model of the Network Radar Countermeasure Systems is established.Next,the task area is divided and coverage weighted on the basis of this model. And then,the objective function of the particle swarm algorithm is obtained and the particle swarm of the transceiver station is redeployed. Through several simulation experiments, the optimal number of stations is obtained under the background of the paper. The simulation results show that,when compared with the standard particle swarm optimization algorithm, the algorithm proposed in this paper has faster convergence and higher weighted coverage,which can meet the demand of anti-stealth optimization.
Aiming at the loss of angle information and low utilization ratio of equipment resources in traditional radar anti-stealth detection,an improved Particle Swarm Optimization(PSO) algorithm for anti-stealth optimization of Network Radar Countermeasures System is proposed in this paper.Firstly,the anti-stealth detection model of the Network Radar Countermeasure Systems is established.Next,the task area is divided and coverage weighted on the basis of this model. And then,the objective function of the particle swarm algorithm is obtained and the particle swarm of the transceiver station is redeployed. Through several simulation experiments, the optimal number of stations is obtained under the background of the paper. The simulation results show that,when compared with the standard particle swarm optimization algorithm, the algorithm proposed in this paper has faster convergence and higher weighted coverage,which can meet the demand of anti-stealth optimization.
引文
[1]桑建华.飞行器隐身技术[M].北京:航空工业出版社,2013.
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[5]崔玉娟,察豪,田斌.改进的混合蛙跳算法在雷达网部署中的应用[J].海军工程大学学报,2015,27(1):108-112.
[6]王春阳,宫健,冯坤菊.基于粒子群算法的多基地雷达优化布站方法[J].现代防御技术,2010,38(2):104-107.
[7]JIAN L,PETER S. MIMO雷达信号处理[M].北京:国防工业出版社,2013.
[8]FISHLER E. Spatial diversity in radars models and detection performance[J]. IEEE Transactions on Signal Processing,2006,54(3):823-837.
[9]周万幸,吴鸣亚,胡明春.双(多)基地雷达系统[M].北京:电子工业出版社,2011.
[10]陈世春,黄沛霖,姬金祖.从探测概率的角度评价飞机的隐身性能[J].航空学报,2015,36(4):1150-1161.
[11]韩英臣,张永顺,刘芸江.雷达组网反隐身技术探讨[J].电子对抗技术,2003,18(2):9-12.
[12]宋佳庆,张峰,关永胜.基于最优作战效能的雷达部署优化问题研究[J].中国电子科学研究院学报,2015,10(4):373-378.
[13]郑贵文,蒋奎.基于粒子群算法的雷达网优化部署方法研究[J].无线电工程,2016,46(1):42-45.
[14]袁俊超,张小宽,林存坤,等.制导雷达组网反隐身探测效能评估[J].火力与指挥控制,2017,42(8):64-67.
[15]周宇,王红军,林绪森.分布式无线感知网络节点部署算法研究[J].信号处理,2017,33(3):225-232.
[2]姜秋喜.网络雷达对抗系统导论[M].北京:国防工业出版社,2010.
[3]曹丽梅,王瑛.雷达隐身与反隐身技术发展综述[J].现代导航,2012,6(3):215-218.
[4]李修和,陈永光,沈阳.电子战环境下双基地雷达对隐身目标的跟踪技术研究[J].电子学报,2004,32(6):918-923.
[5]崔玉娟,察豪,田斌.改进的混合蛙跳算法在雷达网部署中的应用[J].海军工程大学学报,2015,27(1):108-112.
[6]王春阳,宫健,冯坤菊.基于粒子群算法的多基地雷达优化布站方法[J].现代防御技术,2010,38(2):104-107.
[7]JIAN L,PETER S. MIMO雷达信号处理[M].北京:国防工业出版社,2013.
[8]FISHLER E. Spatial diversity in radars models and detection performance[J]. IEEE Transactions on Signal Processing,2006,54(3):823-837.
[9]周万幸,吴鸣亚,胡明春.双(多)基地雷达系统[M].北京:电子工业出版社,2011.
[10]陈世春,黄沛霖,姬金祖.从探测概率的角度评价飞机的隐身性能[J].航空学报,2015,36(4):1150-1161.
[11]韩英臣,张永顺,刘芸江.雷达组网反隐身技术探讨[J].电子对抗技术,2003,18(2):9-12.
[12]宋佳庆,张峰,关永胜.基于最优作战效能的雷达部署优化问题研究[J].中国电子科学研究院学报,2015,10(4):373-378.
[13]郑贵文,蒋奎.基于粒子群算法的雷达网优化部署方法研究[J].无线电工程,2016,46(1):42-45.
[14]袁俊超,张小宽,林存坤,等.制导雷达组网反隐身探测效能评估[J].火力与指挥控制,2017,42(8):64-67.
[15]周宇,王红军,林绪森.分布式无线感知网络节点部署算法研究[J].信号处理,2017,33(3):225-232.