Sparse linear arrays based on a novel subarrayed scheme are proposed and synthesized in this letter. The array with a fixed aperture size is partitioned into several uniformly spaced subarrays while number, spacing, and excitation in each subarray are optimized with multiple constraints. Compared with conventional sparse linear array with all the elements excited independently, the sparse linear array with the novel subarrayed scheme provides excitations at the subarray port and reduces the excitation control numbers remarkably. By integrating the cuckoo search (CS) algorithm with convex programming (CP), a hybrid CS–CP method is proposed and applied to the synthesis problem while the constraints are satisfied during the optimization process. Three examples with series of cases are presented, and the obtained results are compared to those presented in some state-of-the-art references. The optimized array achieves an improved peak sidelobe level and reduced excitation control number.

中文翻译：

---

使用带有凸规划的混合布谷鸟搜索算法合成具有减少的激励控制数的稀疏线性阵列

在这封信中提出并综合了基于新子阵列方案的稀疏线性阵列。具有固定孔径大小的阵列被划分为多个均匀间隔的子阵列，而每个子阵列中的数量、间距和激励在多个约束条件下进行优化。与传统的所有单元独立激励的稀疏线阵相比，采用新型子阵方案的稀疏线阵在子阵端口提供激励，显着减少了激励控制次数。通过将布谷鸟搜索 (CS) 算法与凸规划 (CP) 相结合，提出了一种混合 CS-CP 方法，并将其应用于优化过程中满足约束条件的综合问题。给出了一系列案例的三个例子，并将获得的结果与一些最先进的参考文献中的结果进行比较。优化的阵列实现了改进的峰值旁瓣电平和减少的激励控制数量。