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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Veröffentlicht in:	IET microwaves, antennas & propagation Jg. 10; H. 11; S. 1146 - 1153
Hauptverfasser:	Yan, Fei, Yang, Peng, Yang, Feng, Dong, Tao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 20.08.2016
Schlagworte:	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
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1751-8725 1751-8733
DOI:	10.1049/iet-map.2015.0775