遗传算法综合阵列的幅度和相位方向图

Synthesis of the array’s amplitude and phase pattern using genetic algorithm

分析了阵列方向图分别为对称、实的或实的且对称时所对应的单元激励关系,简要介绍了遗传算法,并将其用于阵列方向图综合.遗传算法是一种自适应全局优化概率搜索算法,它直接以目标函数作为搜索信息.通过合理地设计目标函数,遗传算法不仅可以综合幅度方向图,还可以综合相位,适合于需要进行幅度和相位补偿的情况,以及多目标的情况.最后,结合平顶波束方向图与抛物面天线初级馈源的正割平方方向图进行综合,证明了遗传算法的有效性和灵活性,说明使用遗传算法时尽量利用方向图的实的或对称特性,可以减少待优化变量的数目,加快收敛.并提出将传统遗传算法与传统方向图综合方法相结合,可以进一步减少得到最优解所需要的进化代数.

The element excitation relationships were analyzed respectively when the array patterns are symmetric, real or symmetric and real, Then genetic algorithm（GA） was introduced, and it was applied to synthesize array patterns. GA is an adaptive and global optimizing probability search method. It directly uses the objective function as search message. So GA can synthesize not only amplitude but also phase by designing the objective function correctly. It is fit for the conditions that need to compensate amplitude and phase and optimize multi-objective. At last, GA was proved to be valid and flexible through synthesizing flatbeam power pattern and the square of secant pattern which is used for the parabolic antenna＇s initial feed. These two simulation examples imply that making best use of patterns＇ real or symmetric characters in GA can reduce optimizing parameters and accelerate convergence speed. It also suggests that combining GA with traditional pattern synthesis method will farther reduce the evolution generations, which are needed to get the best solution.