改进遗传算法在协同干扰资源分配中的应用
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摘要
针对敌方雷达网的多干扰机协同干扰资源分配问题,提出了一种基于二维整数编码的改进遗传算法。首先,根据不同干扰样式在不同恒虚警检测器中的干扰效果差异,从压制概率公式出发,构建目标函数,然后根据分配原则建立干扰资源分配模型,最后利用改进遗传算法对模型寻优求解,并给出具体求解步骤。仿真结果表明,改进遗传算法收敛稳定性好,全局寻优能力强,能很好解决雷达干扰资源协同优化分配问题。
In order to solve the problem of optimization assignment for cooperative jamming resources against radar network,an improved genetic algorithm was proposed in this paper.Firstly,To get the best jamming effect by using different types of jamming in different constant false alarm detectors,an objective function for allocating radar jamming resources was built,which was based on the calculation of the suppression probability formula.Then,The optimal distribution model of radar jamming resource was built according to the principle of distribution.Finally,the improved genetic algorithm was applied to solve the model and the calculation steps were presented in detail.Simulation results showed that the improved genetic algorithm had satisfying global optimization performance and the convergence quality.The genetic algorithm was applicable for the optimal distribution of radar jamming resource.
In order to solve the problem of optimization assignment for cooperative jamming resources against radar network,an improved genetic algorithm was proposed in this paper.Firstly,To get the best jamming effect by using different types of jamming in different constant false alarm detectors,an objective function for allocating radar jamming resources was built,which was based on the calculation of the suppression probability formula.Then,The optimal distribution model of radar jamming resource was built according to the principle of distribution.Finally,the improved genetic algorithm was applied to solve the model and the calculation steps were presented in detail.Simulation results showed that the improved genetic algorithm had satisfying global optimization performance and the convergence quality.The genetic algorithm was applicable for the optimal distribution of radar jamming resource.
引文
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[2]Zhao S S,Zhang L R,Zhou Y,et al.Discrimination of active false targets in multistatic radar using spatial scattering properties[J].LET Radar,Sonar&Navigation,2016,10(5):816-826.
[3]Rao B,Xiao S P,Wang X,et al.Maximum Likelihood approach to the estimation and discrimination of exoatmospheric active phantom tracks using motion features[J].IEEE Transactions on Aerospace&Electronic Systems,2012,48(1):794-819.
[4]刘以安,倪天权,张秀辉,等.模拟退火算法在雷达干扰资源优化分配中的应用[J].系统工程与电子技术,2009,31(8):1914-1300.
[5]李东生,高杨,雍爱霞.基于改进离散布谷鸟算法的干扰资源分配研究[J].电子与信息学报,2016,38(4):899-905.
[6]沈阳,陈永光,李修和.基于0-1规划的雷达干扰资源优化分配研究[J].兵工学报,2007,28(5):528-532.
[7]吕永胜,王树宗,王向伟,等.基于贴近度的雷达干扰资源分配策略研究[J].系统工程与电子技术,2005,27(11):1893-1894.
[8]任松,司长哲.雷达干扰机分配的模糊多属性动态规划模型[J].系统工程与电子技术,2008,30(10):1909-1913.
[9]高彬,吕善伟.遗传算法在电子战干扰规划中的应用[J].北京航空航天大学学报,2006,32(8):339-936.
[10]张养瑞,李云杰,高梅国.协同干扰资源优化分配模型及算法[J].系统工程与电子技术,2014,36(11):1744-1749.
[11]雷磊,周青松,张剑云,等.基于小生境遗传算法的干扰资源调度研究[J].现代防御技术,2014,42(1):94-100.
[12]何友,关键,孟祥伟,等.雷达目标检测与恒虚警处理[M].北京:清华大学出版社,2011.
[13]柳向,李东生,刘庆林.基于OS-CFAR的LFM脉压雷达多假目标干扰分析[J].系统工程与电子技术,2017,39(7):1486-1492.
[14]张锡祥,肖开奇,顾杰.新体制雷达对抗导论[M].北京:北京理工大学出版社,2011.
[15]刘忠.基于DRFM的线性调频脉冲压缩雷达干扰新技术[D].长沙:国防科技大学,2006:39-46.
[16]张养瑞,高梅国.基于检测概率的雷达网协同干扰效果评估方法[J].系统工程与电子技术,2015,37(8):1778-1786.