Pattern Synthesis of Subarrayed Large Linear and Planar Arrays Using $K$-Means Solution
Quanhu Shi, Zhi Zheng, Yan Sun
Abstract
In this letter, a new algorithm is presented for synthesizing large linear and planar arrays. To reduce the system cost and implementation complexity, the large array is partitioned into multiple disjoint subarrays and is only weighted at subarray level. By the approximation of the optimization objective, the pattern synthesis problem is first formulated as a weight matching problem. Subsequently, a clustering methodology named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means approach is adopted to solve this problem. The proposed algorithm can produce a good-shaped pattern, which has lower peak sidelobe level than that synthesized by the state-of-the-art method. Numerical results demonstrate the effectiveness of the proposed algorithm.