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

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.