基于阵列Taylor权的最优粒子初值估计法  

Optimum particle’s initial values’estimation by array’s Taylor weights

作　　者：侴永斌 张树春 李德鑫 史毅夫 姜巍 Chou Yongbin;Zhang Shuchun;Li Dexin;Shi Yifu;Jiang Wei(China AVIC International Simulation Technology and Service Co.,Ltd.,Shanghai 201600,China;Department of Bomber and Transport Pilot Conversion,Air Force Harbin Flight Academy,Harbin 150088,China)

机构地区：[1]中航国际仿真科技服务有限公司,上海201600 [2]空军哈尔滨飞行学院改装系,哈尔滨150088

出　　处：《电子测量技术》2021年第6期70-75,共6页Electronic Measurement Technology

基　　金：国家自然科学基金(61273095)项目资助。

摘　　要：优化阵列天线方向图时,最优粒子初值的有效估计能极大改善粒子群优化算法(PSO)的收敛性,得到更好的全局最优解。提出了基于Taylor权的最优粒子初值估计法,即利用阵列Taylor权作为最优粒子初值的有效估计量来改善PSO算法的收敛特性。仿真实验评估随机权、Taylor权和解析权3种最优粒子初值估计法对PSO算法收敛特性的影响,结果表明,相对于随机权,Taylor权和解析权都能够有效地改善PSO算法收敛特性,且两种方法的性能比较接近。由于解析权必须对大型矩阵求广义逆,这种运算不但非常消耗计算资源,且对超大型矩阵甚至无法实现,而Taylor权初值估计法相对简单高效,因而在阵列天线方向图非线性优化时,Taylor权最优粒子初值估计法具有更好的算法实用性。When particle swarm optimization(PSO)is adopted to optimize antenna array pattern,the optimizer’s convergence can be greatly improved by the efficient estimations of the optimum particle’s initial values,and some global optimal solutions better than usual can be obtained.By using the array’s Taylor weighting to estimate the optimum particle’s initial values,a new method is presented to improve PSO optimizer’s convergence.Comparisons are made between three methods of estimations,which are random weights,Taylor weights and analytic weights respectively,it can be said that Taylor weights and analytic weights are both effective to improve the PSO optimizer convergence relative to the random weights,and they both have similar performance.As the analytic weights only can be solved by performing large-scale matrix’s generalized inversion which is usually difficult and hardly afforded by common PC,while Taylor weights are relatively simple and easy to be calculated,the practicability of the latter is far better than the former.

关 键 词：非线性优化算法 粒子群优化算法(PSO) 阵列天线方向图 方向图优化 

分 类 号：TN95[电子电信—信号与信息处理] TN82[电子电信—信息与通信工程]