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Fast optimization of sparse antenna array using numerical Green's function and genetic algorithm
Author(s) -
Raji Mordecai F.,
Zhao Huapeng,
Monday Happy N.
Publication year - 2019
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2544
Subject(s) - antenna array , side lobe , antenna (radio) , computer science , genetic algorithm , algorithm , binary number , coding (social sciences) , constraint (computer aided design) , mathematical optimization , electronic engineering , mathematics , telecommunications , engineering , statistics , arithmetic , geometry
A single‐element antenna is unfit for application in most wireless systems and an alternative is an array of antenna. The desire to reduce weight and cost of antenna arrays gave rise to sparse arrays. The design of a sparse antenna array requires an optimization process, which is time‐consuming for large arrays. In order to accelerate the optimization process, a method combining the numerical Green's function (NGF) and genetic algorithm (GA) is presented in this paper. In the proposed method, binary coding is applied to describe the status of antenna elements, and GA optimization is performed to sparsify the array subject to constraint on the peak side lobe level (PSLL). The PSLL is calculated efficiently by the NGF. Simulation results are presented to illustrate the advantage of the proposed method. It is shown that the proposed method significantly reduces the optimization time.