现代防御技术 ›› 2020, Vol. 48 ›› Issue (3): 104-112.doi: 10.3969/j.issn.1009-086x.2020.03.017

• 仿真技术 • 上一篇    

改进粒子群算法在雷达组网优化布站中的应用

马艳艳, 金宏斌, 李浩, 张辉   

  1. 空军预警学院,湖北 武汉 430019
  • 收稿日期:2019-05-21 修回日期:2019-11-20 出版日期:2020-06-20 发布日期:2021-01-20
  • 作者简介:马艳艳(1991-),女,甘肃天水人。硕士生,主要研究方向为通信与信息系统。通信地址:430019 湖北省武汉市江岸区黄浦大街288号 E-mail:ma163email@163.com

Application of Improved PSO Algorithm in Radar-Net Deployment

MA Yan-yan, JIN Hong-bin, LI Hao, ZHANG Hui   

  1. Air Force Early Warning Academy,Hubei Wuhan 430019,China
  • Received:2019-05-21 Revised:2019-11-20 Online:2020-06-20 Published:2021-01-20

摘要: 针对雷达组网优化布站问题,通过分析雷达组网探测性能指标,建立了优化布站数学模型,提出了一种融入自适应反向学习机制的改进粒子群算法并应用于雷达组网布站优化。在此基础上,通过调整加权值分析了不同情形下各性能指标对雷达网探测能力的影响,得出了各情形下的最优布站方案。仿真结果表明,采用改进算法得到的布站方案相对标准粒子群算法要更优,从而验证了布站模型的可行性和改进算法的有效性。

关键词: 雷达组网, 优化布站, 数学模型, 粒子群算法, 反向学习, 仿真

Abstract: A mathematical model of radar net deployment is established by analyzing the detection performance indicators of radar network.At the same time,an optimal deployment scheme of radar networking based on adaptive opposition-based particle swarm optimization (AOPSO) algorithm is proposed.On this basis,the influence of each performance index on the detection ability in different cases is analyzed by adjusting the weighted value,and the optimal deployment in each case is obtained.The simulation results show that the results obtained by the improved algorithm are better than those obtained by standard particle swarm optimization algorithm,which verifies the feasibility of the mathematical model and the effectiveness of the improved algorithm.

Key words: radar network, optimized deployment, mathematical model, particle swarm optimization(PSO), opposition-based learning, simulation

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