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.

A Crossover Improved Genetic Algorithm and Its Application in Non-Uniform Linear Antenna Arrays

2017 ◽  
Vol 16 (04) ◽  
pp. 1750027 ◽  
Author(s):  
Bhargav Appasani ◽  
Rahul Pelluri ◽  
Vijay Kumar Verma ◽  
Nisha Gupta
Keyword(s):  
Genetic Algorithm ◽  
Antenna Arrays ◽  
Optimization Technique ◽  
Beam Width ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Linear Antenna ◽  
Improved Genetic Algorithm ◽  
Element Array ◽  
Linear Antenna Arrays

Genetic Algorithm (GA) is a widely used optimization technique with multitudinous applications. Improving the performance of the GA would further augment its functionality. This paper presents a Crossover Improved GA (CIGA) that emulates the motion of fireflies employed in the Firefly Algorithm (FA). By employing this mimicked crossover operation, the overall performance of the GA is greatly enhanced. The CIGA is tested on 14 benchmark functions conjointly with the other existing optimization techniques to establish its superiority. Finally, the CIGA is applied to the practical optimization problem of synthesizing non-uniform linear antenna arrays with low side lobe levels (SLL) and low beam width, both requirements being incompatible. However, the proposed CIGA applied for the synthesis of a 12 element array yields an SLL of [Formula: see text]29.2[Formula: see text]dB and a reduced beam width of 19.1[Formula: see text].

scholarly journals A Hybrid GA/GE Beamforming Technique for Side Lobe Cancellation of Linear Antenna Arrays

2021 ◽  
Vol 10 (2) ◽  
pp. 67-77
Author(s):  
S. I. Abdelrahman ◽  
A. H. Hussein ◽  
A. E. A. Shaalan
Keyword(s):  
Antenna Arrays ◽  
Optimization Technique ◽  
Side Lobe ◽  
Main Beam ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Gauss Elimination ◽  
Half Power ◽  
Beamforming Technique ◽  
Linear Antenna Arrays

Side lobe level reduction is one of the most critical research topics in antenna arrays beamforming as it mitigates the interfering and jamming signals. In this paper, a hybrid combination between the Genetic algorithm (GA) optimization technique and the gauss elimination (GE) equation solving technique is utilized for the introduction of the proposed GA/GE beamforming technique for linear antenna arrays. The proposed technique estimates the optimum excitation coefficients and the non-uniform inter-elements spacing for a specific side lobe (SL) cancellation without disturbing the half power beamwidth (HPBW) of the main beam. Different size Chebychev linear antenna arrays are taken as simulation targets. The simulation results revealed the effectiveness of the proposed technique

scholarly journals Antenna Array Weight Synthesis for Low Side Lobe Levels using Window Functions

2019 ◽  
Vol 9 (1) ◽  
pp. 3074-3081
Keyword(s):  
Antenna Array ◽  
Radiation Pattern ◽  
Antenna Arrays ◽  
Side Lobe ◽  
Optimization Techniques ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Window Functions ◽  
Linear Antenna Arrays ◽  
Array Weight

A lot of research is being carried out to reduce side lobe levels (SSLs) in the radiation pattern of antenna arrays. A number of novel optimization techniques have been developed over the years and adapted for this purpose. In this paper, a number of window functions are applied to suppress the maximum side lobe level (MSLL) in linear antenna arrays. The window functions Bartlett, Taylor, Hanning, Barthann, Hamming, Gaussian, Blackman, Chebyshev, Blackman-Harris and Kaiser are considered in the simulation. The optimized pattern for a 10 element linear antenna array and corresponding normalized window tappers for every window are presented. Finally the efficiency of all windows is compared in terms of their computed parameters.

scholarly journals The optimal synthesis of scanned linear antenna arrays

2020 ◽  
Vol 10 (2) ◽  
pp. 1477
Author(s):  
Anas A. Amaireh ◽  
Asem S. Al-Zoubi ◽  
Nihad I. Dib
Keyword(s):  
Antenna Arrays ◽  
Firefly Algorithm ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Symbiotic Organisms Search ◽  
Optimization Parameters ◽  
Antlion Optimization ◽  
Symbiotic Organisms ◽  
Linear Antenna Arrays

In this paper, symmetric scanned linear antenna arrays are synthesized, in order to minimize the side lobe level of the radiation pattern. The feeding current amplitudes are considered as the optimization parameters. Newly proposed optimization algorithms are presented to achieve our target; Antlion Optimization (ALO) and a new hybrid algorithm. Three different examples are illustrated in this paper; 20, 26 and 30 elements scanned linear antenna array. The obtained results prove the effectiveness and the ability of the proposed algorithms to outperform and compete other algorithms like Symbiotic Organisms Search (SOS) and Firefly Algorithm (FA).

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 Synthesis and Failure Correction of Flattop and Cosecant Squared Beam Patterns in Linear Antenna Arrays

2017 ◽  
Vol 4 ◽  
pp. 25-30 ◽  
Author(s):  
Hemant Patidar ◽  
Gautam Kumar Mahanti ◽  
Ramalingam Muralidharan
Keyword(s):  
Antenna Arrays ◽  
Firefly Algorithm ◽  
Recovery Process ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Linear Antenna ◽  
Successful Employment ◽  
The Individual ◽  
Beam Patterns ◽  
Linear Antenna Arrays

This paper deals with the synthesis of flattop and cosecant squared beam patterns using the firefly algorithm which is based on metaheuristics. This synthesis is followed by the correction of the radiation patterns when unfortunate malfunctioning of the individual elements in the array occurs. The necessary attention is given to the recovery process, with due emphasis on reduction of side lobe level, ripple and the reflection coefficient. Simulation in Matlab shows a successful employment of the firefly algorithm in producing voltage excitations of the good elements necessary for the recovered patterns. The performance of the firefly algorithm in failure correction is validated by duly comparing it with a standard benchmark.

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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