Peak Sidelobe Level Reduction With a Hybrid Approach Based on GAs and Difference Sets

Abstract

--This paper presents an approach for the optimization of the beam pattern produced by massively thinned arrays. The method, which combines the most attractive features of a genetic algorithm and those of a combinatorial technique (namely, the Difference Sets Method), is aimed at synthesizing massively thinned antenna arrays in order to suitably reduce the peak side-lobe level. Selected numerical results are presented in order to assess the effectiveness and reliability of the proposed approach.

ARRAY ANTENNAS OF SIZE 8x8 BASED ON HADAMARD DIFFERENCE SETS

“…Recently, the so called genetic algorithms were applied to the synthesis of linear AAs in a number of papers, including those where cyclic DSs as initial ones were taken [16]. In some cases, one can pass from a linear grid to a rectangular one having coprime sidelengths, thus obtaining a planar AA.…”

Design of a metamaterial-based linear insulated antenna using a genetic algorithm

“…They have been shown to be useful for optimizing sidelobe levels in phased arrays [16,17] and for optimizing element spacings in Yagi-Uda antennas [18], among other applications [19]. There are certainly other optimization methods that have been recently discussed in the literature that could be employed for this problem, such as multiobjective optimization [20,21] and particle swarm optimization [22], but our goal here is not so much to highlight a method of optimization as it is to highlight the resulting design of the antenna.…”

On the radiation properties of ADS-thinned dipole arrays

“…The periodic nature of the arising layouts also enables an easier construction compared to other nonregular arrangements such as aperiodic structures [1]. Also for these reasons, array thinning has been deeply investigated and several methodologies have been proposed ranging from dynamic programming [3], random approaches [4,5], as well as stochastic tools based on genetic algorithms [1,6,7,8,9,10,11,12,13], simulated annealing [14], particle swarm optimizations [15,16,17], and pattern search [18]. Although very effective, these methodologies exhibit some drawbacks either in terms of PSL control (specifically, random layouts) or computational costs (namely, stochastic optimization-based techniques) especially when large layouts are at hand.…”