Synthesis of planar sparse arrays by perturbed compressive sampling framework

Recently, compressive sensing (CS) theory has been applied for synthesising maximally sparse arrays, in which the best subset of sampling element locations is chosen to compose a sparse array for matching a desired radiation pattern. However, their performances are strongly depended on the proper se...

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Bibliographic Details
Published in:IET microwaves, antennas & propagation Vol. 10; no. 11; pp. 1146 - 1153
Main Authors: Yan, Fei, Yang, Peng, Yang, Feng, Dong, Tao
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 20.08.2016
Subjects:
antenna radiation patterns
aperture antennas
array signal processing
Arrays
compressed sensing
computational cost reduction
continuous array aperture gridding
continuous element placement
CS theory
extended PCS model
FOCal under‐determined system solver
joint sparse recovery approach
Mathematical models
Microwave antennas
modelling error reduction
pattern matching
Perturbation methods
perturbed compressive sampling framework
planar antenna arrays
planar sparse array synthesis
radiation pattern matching
Recovering
Sampling
secondary grid strategy
signal synthesis
Solvers
Synthesis
signal synthesis
planar sparse array synthesis
pattern matching
compressed sensing
aperture antennas
modelling error reduction
joint sparse recovery approach
perturbed compressive sampling framework
continuous array aperture gridding
FOCal under-determined system solver
planar antenna arrays
extended PCS model
radiation pattern matching
continuous element placement
antenna radiation patterns
CS theory
computational cost reduction
array signal processing
secondary grid strategy
ISSN:1751-8725, 1751-8733
Online Access:Get full text
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Summary:Recently, compressive sensing (CS) theory has been applied for synthesising maximally sparse arrays, in which the best subset of sampling element locations is chosen to compose a sparse array for matching a desired radiation pattern. However, their performances are strongly depended on the proper setting of the initial sampling locations, which are typically obtained by gridding the continuous array aperture. Such a setting is usually hard to handle for large planar array synthesis. To address this problem, a precision and effective method based on the perturbed compressive sampling (PCS) is proposed. Position perturbation variables are augmented to the traditional CS-based model, which allow continuous element placement. Then, a joint sparse recovery approach is used to optimise the excitations and position perturbations of the elements simultaneously. Moreover, the authors implement an extended PCS model with a secondary grid strategy to reduce the modelling error and the computational cost. The proposed design problem is solved with a general sparse recovery solver, named FOCal under-determined system solver. Numerical results show that the method yields a higher array sparsity, a faster computational speed and a better pattern matching accuracy than the existing CS-based methods.
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ISSN:1751-8725
1751-8733
DOI:10.1049/iet-map.2015.0775