z-logo
open-access-imgOpen Access
DESIGN AND OPTIMIZATION OF LOW RCS PATCH ANTENNAS BASED ON A GENETIC ALGORITHM
Author(s) -
Xinyue Zhu,
Wei Shao,
JiaLin Li,
Yuliang Dong
Publication year - 2011
Publication title -
electromagnetic waves
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 89
eISSN - 1559-8985
pISSN - 1070-4698
DOI - 10.2528/pier11100703
Subject(s) - hfss , radar cross section , crossover , genetic algorithm , computer science , antenna (radio) , selection (genetic algorithm) , convergence (economics) , radar , algorithm , mathematical optimization , electronic engineering , microstrip antenna , mathematics , telecommunications , engineering , artificial intelligence , machine learning , economics , economic growth
In this article, a genetic algorithm (GA) is employed to the design of low radar cross section (RCS) patch antennas. Combined with the high frequency simulation software (HFSS) for antenna simulations, the GA performs the optimization of geometric parameters. In order to reduce the RCS while holding the satisfying radiation performance of antennas, the radiation model and scattering model are respectively calculated. The combination of proportionate selection and elitist model for the selection strategy is used to speed up the convergence of the GA. Two-point crossover is adopted to accelerate the converging speed and results in more flt individuals. Moreover, the whole design procedure is auto-controlled by programming the VBScript in the HFSS. Two examples of low RCS slot antennas are provided to verify the accuracy and e-ciency of the proposed method.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom