A Technique to Optimize Nonuniformly Spaced Arrays with Low Sidelobe Level by Using a Genetic Algorithm

Abstract

A Genetic Algorithm procedure for synthesizing the radiation pattern of nonunifornly spaced linear arrays with low side lobe level is presented. Some selected preliminary results are shown to validate the effectiveness and the reliability ofthe proposed approach.

Design of Linear and Planar Arrays With Low Sidelobe Levels and High Directivity Using Two-Way Array Factor

“…The product of the array patterns along the x-and y-directions determine the array factor of the 2D rectangular array in the x-y plane [4]. In our case, the total number of transmit elements are N Tx ×N Ty , and the corresponding array factor (AF T ) is given as: where, AF Tx and AF Ty are the transmit array pattern of the 1D array with two-weight amplitude distribution along the xand y-directions, and are given in (7) and (8), respectively.…”

“…O NE of the major problems of radar-based antenna arrays is the presence of unwanted sidelobes (SLs) [1], [2], which reduces its overall radiation efficiency. Many techniques have evolved to suppress SLs, which include the usage of characteristic amplitude distribution about the radiation aperture [3]- [6], Genetic Algorithm [7], and randomly overlapped subarrays [8]. However, an amplitude distribution reduces array directivity and taper efficiency and requires a complex feed network, increasing the cost and footprint of the system.…”

Novel Method for Optimal Synthesis of 5G Millimeter Wave Linear Antenna Array

“…The biggest advantages of using stochastic methods are their ability in dealing with large number of optimization parameters and avoiding getting stuck in local minima. Some of the methods used for the antenna array synthesis are Evolutionary Algorithms (EA) [10], Genetic Algorithms (GA) [11][12][13], Tabu Search (TS) [11,14], Particle Swarm Optimization (PSO) [15][16][17], Ant Colony Optimization (ACO) [18], Nature-Inspired Cuckoo Search (CS) [19,20], Combination of Global and Local Search [21], and Cat Swarm Optimization [22]. All the above-mentioned methods have shown the capability of searching for global solution in electromagnetic optimization problems.…”