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...

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Vydáno v:International journal of numerical modelling Ročník 33; číslo 4
Hlavní autoři: Raji, Mordecai F., Zhao, Huapeng, Monday, Happy N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bognor Regis Wiley Subscription Services, Inc 01.07.2020
Témata:
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
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Abstract 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.
AbstractList 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.
Author Zhao, Huapeng
Monday, Happy N.
Raji, Mordecai F.
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  surname: Monday
  fullname: Monday, Happy N.
  organization: University of Electronic Science and Technology of China
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CitedBy_id crossref_primary_10_1016_j_dt_2021_03_001
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crossref_primary_10_3390_mi16091038
crossref_primary_10_1155_2022_1169648
crossref_primary_10_3390_electronics12194087
crossref_primary_10_3390_photonics10121300
Cites_doi 10.1109/ICEAA.2011.6046328
10.1109/WIECON-ECE.2015.7443956
10.1109/TAP.2013.2292529
10.1109/TEMC.2016.2515846
10.1109/TAP.2005.872568
10.1088/2040-8986/aae0d6
10.1109/MAP.2018.2859163
10.1109/TAP.2002.807947
10.1109/MAP.2015.2437275
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SubjectTerms 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
Title Fast optimization of sparse antenna array using numerical Green's function and genetic algorithm
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