遗传算法在相控阵子阵量化随机馈相中的应用  

The Application of Genetic Algorithm in Randomizing Phase Feeding of Phased Arrays Sub-array Quantization

作　　者：刘昊 樊德森[2] 

机构地区：[1]中国航天科技集团704所,北京100076 [2]中国科学技术大学电子工程与信息科学系,安徽合肥230026

出　　处：《应用科学学报》2004年第3期314-317,共4页Journal of Applied Sciences

摘　　要：在大型相控阵中,为了减少昂贵的数字移相器,一般采用子阵技术.但这又带来新的问题,子阵相位量化时,到底是采用前一个单元的相位还是采用后一个单元的相位,量化时是舍去还是进入,这些都给子阵相位量化的分析增加了复杂性.在子阵量化随机馈相的基础上,采用遗传算法对子阵量化中各种随机馈相的方案进行优化,并给出应用实例,取得了令人满意的优化结果.In Iarge scale phased arrays, we apply the sub-array technique to reduce the number of expensive digital shifters. But new problems will arise when it comes to sub-array phase quantization——which phase should be chosen? The former or the later element? All this makes the analysis of sub-array phase quantization more complicated. On the basis of sub-array quantization randomizing phase feeding, we use genetic algorithm to optimize the randomizing phase feeding and give an optimization example, in which a good result is obtained.

关 键 词：遗传算法 子阵量化 随机馈相 移相器 相控阵雷达 

分 类 号：TN958.92[电子电信—信号与信息处理]