Synthesis of Clustered Concentric Ring Arrays Through Joint Optimization of Multi-Parameters

Abstract

This article proposes a novel hybrid methodology that addresses the clustering of concentric ring arrays (CRAs) through joint optimization of ring radii, clusters partitions, and clusters excitations. It takes advantage of convex programming (CP) with respect to the excitation variables due to their convex nature. For the optimization of rings radii and clusters partitions, a subproblem is formulated by mix-integer programming with linear constraints, which is effectively resolved using the developed technique of mixed-integer linear-constraint genetic algorithm (MILCGA). A set of representative numerical experiments show the effectiveness of the proposed method and illustrate its potentialities, representing a promising solution for CRAs to obtain a suitable tradeoff between costs, robustness, flexibility, and beam control performances.