一种低副瓣稀布阵列天线的方向图综合算法

提出了一种对含有较多单元的稀布直线阵列，以及稀布平面阵列天线进行低副瓣综合的二阶算法。采用迭代傅里叶算法获得一个具有较低副瓣，栅格间距为半波长的稀疏直线平面阵列。针对所得到的稀疏阵列，选择相邻间距大于半波长的单元作为被优化对象，进一步采用差分进化算法，在满足单元间距不小于半波长的约束条件下，对被选中单元的位置和激励相位进行优化来获取具有更低副瓣的稀布阵列天线。根据上述约束条件，在执行完算法的第一步后，阵列中大部分单元的位置已经固定下来，因此，只有少量单元进入下一步的优化进程，从而有效缩减了差分进化算法的寻优空间，加速算法的收敛。基于不同直线阵列和矩形平面阵列的方向图综合结果表明，采用本算法得到的稀布阵列天线，其旁瓣电平值相比文献中已有的结果均表现出不同程度的下降。

An approach for pattern synthesis of low sidelobe sparse antenna arrays

A two step method is proposed to address the low sidelobe pattern synthesis of sparse linear arrays including many elements, as well as the planar sparse arrays. In this method, the Iterative Fourier Transform (IFT) is used firstly to get a low sidelobe thinned array with the adjacent grids spaced at half wavelength. Then, for the same thinned array, the elements spaced greater than half wavelength are selected as the candidates needing for optimizing. Going further, in order to get a sparse array with reduced sidelobe level, the locations and phases of these selected elements are optimized by the algorithm of differential evolution (DE) as long as the inter-element spacing is not less than half wavelength. According to the constraint rule, most element locations are fixed after executing the first step of the algorithm. Therefore only a small number of elements entered the next optimization process. For this reason, the solution space of DE is considerably reduced, and thereby the convergence speed of the algorithm is accelerated. Numerical results based on different linear arrays and some rectangular planar arrays show impressive sidelobe suppression by adopting the proposed method, as are compared to the published reports.