Synthesis of Sparse Planar Antenna Arrays Using a Matrix Constraints Method

Sparse arrays have gained increasing research attention due to their potential to reduce system cost and weight. However, current studies on sparse array synthesis often overlook the physical dimensions of the antennas and only consider the distance constraints of antenna centers. In this communicat...

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Vydané v:IEEE transactions on antennas and propagation Ročník 72; číslo 5; s. 4618 - 4623
Hlavní autori: Miao, Ke, Zhang, Yi, Wang, Shuoguang, Yao, Chen, Zhao, Guoqiang, Sun, Houjun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Antenna arrays
Antennas
Array synthesis
compressed sensing (CS)
convex optimization
Convexity
genetic algorithm
Genetic algorithms
Lattices
Layout
Mixed integer
Optimization
Sidelobe reduction
Sidelobes
sparse array
Sparse matrices
Synthesis
Transmission line matrix methods
Vectors
ISSN:0018-926X, 1558-2221
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Shrnutí:Sparse arrays have gained increasing research attention due to their potential to reduce system cost and weight. However, current studies on sparse array synthesis often overlook the physical dimensions of the antennas and only consider the distance constraints of antenna centers. In this communication, we propose a novel matrix constraints (MCs) method for the sparse planar array synthesis, taking into account the actual area occupied by each antenna unit. The proposed method introduces a matrix that relates the aperture lattices to each candidate antenna; this matrix is then used as a spatial constraint for the antennas. The synthesis problem, aimed at achieving a low sidelobe level, is formulated as a mixed-integer optimization problem under this MC. To obtain the array layouts efficiently, the synthesis problem is relaxed to a compressed sensing (CS) problem and subsequently solved using a combination of reweighted <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> minimization convex optimization and the integer genetic algorithm (IGA). Numerical experiments and full-wave simulations were conducted, verifying the effectiveness of the proposed method.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2024.3371642