Application of the Global Optimization Approaches To Planar Near-Field Antenna Phaseless Measurements

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F09%3APU80389" target="_blank" >RIV/00216305:26220/09:PU80389 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Application of the Global Optimization Approaches To Planar Near-Field Antenna Phaseless Measurements

Original language description

This paper deals with a method of the radiation pattern determination of the directional antennas. The method combining both the functional minimization method and the Fourier iterative algorithm is based on the phaseless near-field measurement on two plane surfaces. The method is used for a reconstruction of the phase distribution on the aperture of the measured antenna, and for the determination of the antenna radiation pattern, consequently. The binary genetic algorithm (BGA), the real-valued geneticalgorithm (RVGA), the particle swarm optimization (PSO) and differential evolutionary algorithm (DEA) were chosen for the global functional minimization. The paper is aimed to analyze the performance of the global optimizations (GOs) when solving the described problem, and to compare the GOs. GOs were exemined through datas achieved by measurement of the horn atenna and the parabola.

Czech name

Application of the Global Optimization Approaches To Planar Near-Field Antenna Phaseless Measurements

Czech description

This paper deals with a method of the radiation pattern determination of the directional antennas. The method combining both the functional minimization method and the Fourier iterative algorithm is based on the phaseless near-field measurement on two plane surfaces. The method is used for a reconstruction of the phase distribution on the aperture of the measured antenna, and for the determination of the antenna radiation pattern, consequently. The binary genetic algorithm (BGA), the real-valued geneticalgorithm (RVGA), the particle swarm optimization (PSO) and differential evolutionary algorithm (DEA) were chosen for the global functional minimization. The paper is aimed to analyze the performance of the global optimizations (GOs) when solving the described problem, and to compare the GOs. GOs were exemined through datas achieved by measurement of the horn atenna and the parabola.

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JA - Electronics and optoelectronics

OECD FORD branch

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Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2009

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Radioengineering

ISSN

1210-2512

e-ISSN

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Volume of the periodical

18

Issue of the periodical within the volume

1

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

9

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

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