scholarly journals Controlling Asymmetric Equal Side Lobe Level in Radiation Patterns of Array Antenna using Genetic Algorithm

2010 ◽  
Vol 22 (1) ◽  
pp. 135-141
Author(s):  
Shigeru OKUBO ◽  
Fumikazu MINAMIYAMA
Keyword(s):  
Genetic Algorithm ◽  
Side Lobe ◽  
Array Antenna ◽  
Side Lobe Level ◽  
Radiation Patterns ◽  
Equal Side

scholarly journals Iterative GA Optimization Scheme for Synthesis of Radiation Pattern of Linear Array Antenna

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Sarayoot Todnatee ◽  
Chuwong Phongcharoenpanich
Keyword(s):  
Genetic Algorithm ◽  
Radiation Pattern ◽  
Linear Array ◽  
Weight Coefficient ◽  
Side Lobe ◽  
Array Element ◽  
Array Antenna ◽  
Side Lobe Level ◽  
Optimization Scheme ◽  
Iterative Optimization

This research has proposed the iterative genetic algorithm (GA) optimization scheme to synthesize the radiation pattern of an aperiodic (nonuniform) linear array antenna. The aim of the iterative optimization is to achieve a radiation pattern with a side lobe level (SLL) of ≤−20 dB. In the optimization, the proposed scheme iteratively optimizes the array range (spacing) and the number of array elements, whereby the array element with the lowest absolute complex weight coefficient is first removed and then the second lowest and so on. The removal (the element reduction) is terminated once the SLL is greater than −20 dB (>−20 dB) and the elemental increment mechanism is triggered. The results indicate that the proposed iterative GA optimization scheme is applicable to the nonuniform linear array antenna and also is capable of synthesizing the radiation pattern with SLL ≤ −20 dB.

Pattern Synthesis of Circular Array Antenna by Improved Genetic Algorithm

2012 ◽  
Vol 236-237 ◽  
pp. 896-900
Author(s):  
Gong Wei Ren ◽  
Cheng Hua Zhou
Keyword(s):  
Genetic Algorithm ◽  
Side Lobe ◽  
Array Antenna ◽  
Main Beam ◽  
Side Lobe Level ◽  
Improved Genetic Algorithm ◽  
Pattern Synthesis ◽  
Multi Objective Genetic Algorithm ◽  
Real Coded Genetic Algorithm ◽  
Crossover And Mutation

Propose an application of multi-objective genetic algorithm (GA) for pattern synthesis of circular arrays with equally spaced isotropic elements. Aim at reduce the side-lobe level and deep the null of array antenna patterns, adopt improved real-coded genetic algorithm, which modify the crossover operator and mutation operator compare with standard GA, both the crossover and mutation probability adjustable in accordance with the generation numbers, in the final, the approach for antenna pattern synthesis can suppress interferences by deep the nulls at directions of interfering sources and placing main beam in direction of desired signal by controlling amplitude weights. Numerical results show effectiveness of the procedure proposed.

scholarly journals Sidelobe Level Optimization of Rectangular Microstrip Patch Antenna Array using Binary Coded Genetic Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 2053-2056
Keyword(s):  
Genetic Algorithm ◽  
Patch Antenna ◽  
Communication Systems ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Patch Antennas ◽  
Radiation Patterns ◽  
Maximum Reduction ◽  
Microstrip Patch ◽  
Rectangular Patch

In modern world, communication systems requires development of low cost, minimal weight, and low profile antennas which are capable of maintaining high performance over wide range of frequencies. Patch antenna is one such antenna which fulfills the demands of current communication systems. The widely used microstrip patch antennas are rectangular patch antennas. This paper presenting the application of binary coded Genetic Algorithm (BGA) which is applied to the rectangular patch microstrip antenna with uniform linear arrays. The fitness function of GA is maximum reduction in peak side lobe level of the radiation pattern of the antenna with maximum reduction in the side lobe level and also achieved the minimum possible null to null beam width, the resultant radiation patterns for both before GA and after GA of microstrip array are compared. The radiation patterns are presented for 20,50,100 number of elements. All the simulated results are obtained by using MATLAB software.

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