A Hybrid Approach for the Synthesis of Nonuniformly-Spaced Linear Subarrays

By combining convex programming (CP) and particle swarm optimization (PSO), a hybrid method for the synthesis of excitations and locations of nonuniformly spaced linear subarrays to minimize the subarray number is proposed and discussed in this article. The synthesis problem herein is formulated as a CP problem by minimizing the l(1)-norm with respect to the excitation variables, and the PSO procedure is carried out as far as location variables are concerned. Through collaborations like this, we can finally get the optimal solution in a global sense. A set of representative numerical experiments shows the effectiveness of this method with quite a number of subarrays saved when compared with the uniformly spaced layouts.

Automated summary

By combining convex programming and particle swarm optimization, a hybrid method is proposed for synthesizing excitations and locations of nonuniformly spaced linear subarrays to minimize the subarray number. Numerical experiments show the effectiveness of this method in saving a considerable number of subarrays compared to uniformly spaced layouts.