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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Bibliographic Details
Published inIEEE transactions on antennas and propagation Vol. 62; no. 4; pp. 1681 - 1689
Main Authors Oliveri, Giacomo, Bekele, Ephrem T., Robol, Fabrizio, Massa, Andrea
Format Journal Article
LanguageEnglish
Published IEEE 01.04.2014
Institute of Electrical and Electronics Engineers
Subjects
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
Online AccessGet full text
ISSN0018-926X
1558-2221
DOI10.1109/TAP.2013.2287894

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Summary: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.
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2013.2287894