Hybrid synthesis approach for enhanced sidelobe suppression in linear and planar antenna arrays

Abstract

Side Lobe Level (SLL) suppression is a fundamental challenge in antenna array design, as high sidelobes degrade radiation efficiency and lead to increased interference. While several approaches including Method of Moments (MoM), Genetic Algorithms (GA), and convolution-based synthesis have been explored for SLL reduction, each suffers from specific limitations such as computational intensity, convergence instability, or structural constraints. This paper proposes a novel hybrid synthesis framework termed 1DC/MoM/GA, which integrates the analytical precision of MoM, the global search capability of GA, and the spatial expansion power of one-dimensional convolution (1DC). For planar arrays, a virtual antenna array (VAA) model is employed, and the final excitation matrix is constructed using the Kronecker product of the optimized linear arrays. The approach strategically decomposes the planar arrays into vertical and horizontal linear sub-arrays to reduce the optimization burden, followed by convolution-based enhancement to achieve deep SLL suppression. The proposed method achieves up to a sixfold reduction in SLL while maintaining computational efficiency. Validation is carried out through MATLAB simulations and CST full-wave modeling, with results demonstrating superior performance compared to state-of-the-art techniques.