Sparsening Conformal Arrays Through a Versatile BCS-Based Method

Sparsening conformal arrangements is carried out through a versatile Multi-Task Bayesian Compressive Sensing (MT-BCS) strategy. The problem, formulated in a probabilistic fashion as a pattern-matching synthesis, is that of determining the sparsest excitation set (locations and weights) fitting a ref...

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Veröffentlicht in:IEEE transactions on antennas and propagation Jg. 62; H. 4; S. 1681 - 1689
Hauptverfasser: Oliveri, Giacomo, Bekele, Ephrem T., Robol, Fabrizio, Massa, Andrea
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.04.2014
Institute of Electrical and Electronics Engineers
Schlagworte:
Antennas
Apertures
Arrays
Bayes methods
Bayesian analysis
Bayesian compressive sampling
Computational efficiency
conformal arrays
constrained array synthesis
Electromagnetism
Engineering Sciences
Fittings
Indexes
Layout
Merging
Pattern matching
Probability theory
sparse arrays
Strategy
Synthesis
Vectors
Bayesian compressive sampling
sparse arrays
constrained array s ynthesis
conformal arrays
ISSN:0018-926X, 1558-2221
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Zusammenfassung:Sparsening conformal arrangements is carried out through a versatile Multi-Task Bayesian Compressive Sensing (MT-BCS) strategy. The problem, formulated in a probabilistic fashion as a pattern-matching synthesis, is that of determining the sparsest excitation set (locations and weights) fitting a reference pattern subject to user-defined geometrical constraints. Results from a set of representative numerical experiments are presented to illustrate the key-features of the proposed approach as well as to assess, also through comparisons, its potentials in terms of matching accuracy, element saving, and computational costs.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2013.2287894