A Hybrid Approach for the Synthesis of Nonuniformly Spaced and Excited Linear Arrays With Strict Element Spacing Constraints

A hybrid methodology for the synthesis of nonuniformly spaced and excited linear arrays is proposed. At first, a modified genetic algorithm (GA) named linear-constraint genetic algorithm (LCGA) is discussed in this article to adapt to generalized linear-constraint optimization problems. Then, by nesting the technique of convex programming (CP) into LCGA, a hybrid optimization implementation is developed to jointly optimize the position and excitation variables. Due to the linear-constraint character of LCGA, array layouts can be exactly constrained with element spacings and the aperture size. A set of representative numerical experiments shows the effectiveness of this method to obtain very high-quality solutions.

Automated summary

A hybrid methodology for synthesizing nonuniformly spaced and excited linear arrays is proposed. The method combines a modified genetic algorithm (LCGA) with convex programming (CP) to jointly optimize the position and excitation variables of the array, resulting in high-quality solutions.