Fast optimization of sparse antenna array using numerical Green's function and genetic algorithm

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...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:International journal of numerical modelling Ročník 33; číslo 4
Hlavní autori: Raji, Mordecai F., Zhao, Huapeng, Monday, Happy N.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 01.07.2020
Predmet:
Antenna arrays
Antennas
Arrays
Binary codes
Computer simulation
genetic algorithm
Genetic algorithms
Green's function
Green's functions
Optimization
peak side lobe level
Sidelobes
sparse array
Weight reduction
ISSN:0894-3370, 1099-1204
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0894-3370
1099-1204
DOI:10.1002/jnm.2544