Shaped-beam pattern synthesis of equally and unequally spaced linear antenna arrays using a modified tabu search algorithm

2002 ◽  
Vol 36 (1) ◽  
pp. 16-20 ◽  
Author(s):  
Ali Akdagli ◽  
Kerim Guney
Keyword(s):  
Tabu Search ◽  
Antenna Arrays ◽  
Search Algorithm ◽  
Beam Pattern ◽  
Tabu Search Algorithm ◽  
Linear Antenna ◽  
Pattern Synthesis ◽  
Shaped Beam ◽  
Unequally Spaced ◽  
Linear Antenna Arrays

Phase-Only Pattern Synthesis for Linear Antenna Arrays

2017 ◽  
Vol 16 ◽  
pp. 3232-3235 ◽  
Author(s):  
Junli Liang ◽  
Xuhui Fan ◽  
Wen Fan ◽  
Deyun Zhou ◽  
Jian Li
Keyword(s):  
Antenna Arrays ◽  
Linear Antenna ◽  
Pattern Synthesis ◽  
Linear Antenna Arrays

scholarly journals Optimal Pattern Synthesis of Linear Antenna Array Using Grey Wolf Optimization Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Prerna Saxena ◽  
Ashwin Kothari
Keyword(s):  
Antenna Arrays ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Main Beam ◽  
Linear Antenna ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Pattern Synthesis ◽  
Uniform Array ◽  
Linear Antenna Arrays

The aim of this paper is to introduce the grey wolf optimization (GWO) algorithm to the electromagnetics and antenna community. GWO is a new nature-inspired metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It has potential to exhibit high performance in solving not only unconstrained but also constrained optimization problems. In this work, GWO has been applied to linear antenna arrays for optimal pattern synthesis in the following ways: by optimizing the antenna positions while assuming uniform excitation and by optimizing the antenna current amplitudes while assuming spacing and phase as that of uniform array. GWO is used to achieve an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. GWO is also applied for the minimization of the first side lobe nearest to the main beam (near side lobe). Various examples are presented that illustrate the application of GWO for linear array optimization and, subsequently, the results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired evolutionary algorithms. The results suggest that optimization of linear antenna arrays using GWO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques.

scholarly journals Pattern Synthesis of Linear Antenna Array Using Improved Differential Evolution Algorithm with SPS Framework

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5158
Author(s):  
Ruimeng Zhang ◽  
Yan Zhang ◽  
Jinping Sun ◽  
Qing Li
Keyword(s):  
Differential Evolution ◽  
Antenna Array ◽  
Antenna Arrays ◽  
Optimization Problem ◽  
Differential Evolution Algorithm ◽  
Linear Antenna ◽  
Pattern Synthesis ◽  
De Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Linear Antenna Arrays

In this paper, an improved differential evolution (DE) algorithm with the successful-parent-selecting (SPS) framework, named SPS-JADE, is applied to the pattern synthesis of linear antenna arrays. Here, the pattern synthesis of the linear antenna arrays is viewed as an optimization problem with excitation amplitudes being the optimization variables and attaining sidelobe suppression and null depth being the optimization objectives. For this optimization problem, an improved DE algorithm named JADE is introduced, and the SPS framework is used to solve the stagnation problem of the DE algorithm, which further improves the DE algorithm’s performance. Finally, the combined SPS-JADE algorithm is verified in simulation experiments of the pattern synthesis of an antenna array, and the results are compared with those obtained by other state-of-the-art random optimization algorithms. The results demonstrate that the proposed SPS-JADE algorithm is superior to other algorithms in the pattern synthesis performance with a lower sidelobe level and a more satisfactory null depth under the constraint of beamwidth requirement.

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