利用GA实现非对称稀疏线阵旁瓣电平的优化

该文讨论运用遗传算法综合非对称的稀疏直线阵列(阵元从规则栅格中稀疏).阵元在中心两侧非对称分布的布阵方式提供了可利用的优化自由度,将更有利于提高稀疏阵列的性能.构造了阵元关于阵中心非对称时优化旁瓣电平的适应度函数,仿真结果表明,无对称约束的阵元排列,不仅可以进一步抑制稀疏线阵的相对旁瓣电平(RSLL),而且当阵列由有向阵元组成时,有益于改善阵列波束扫描过程中RSLL的恶化.

Sidelobe Reduction of Asymmetric Linear Thinned Arrays Using Genetic Algorithm

This paper presents how to synthesize asymmetric linear arrays using genetic algorithms. The asymmetric configuration is an available degree of freedom to control the characters of thinned arrays. In this paper, the Genetic Algorithms (GA) determines which elements are turned off in a periodic array to yield the lowest maximum Relative SideLobe Level (RSLL). Fitness function of GA for asymmetric linear arrays synthesis is designed. Simulation results show that the optimized asymmetric linear arrays which lack the design constraint of bilateral symmetry can not only obtain lower sidelobes, but also weaken the deterioration of scanning beam if the thinned arrays are made up of the directional elements.