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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Vydáno v:IEEE transactions on antennas and propagation Ročník 62; číslo 4; s. 1681 - 1689
Hlavní autoři: Oliveri, Giacomo, Bekele, Ephrem T., Robol, Fabrizio, Massa, Andrea
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.04.2014
Institute of Electrical and Electronics Engineers
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ISSN:0018-926X, 1558-2221
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Abstract 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.
AbstractList Sparsening conformal arrangements is carried out through a versatile Multi-Task Bayesian Compressive Sensing openbracket MT - BCS [ closebracket ] 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.
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.
Author Oliveri, Giacomo
Robol, Fabrizio
Massa, Andrea
Bekele, Ephrem T.
Author_xml – sequence: 1
  givenname: Giacomo
  surname: Oliveri
  fullname: Oliveri, Giacomo
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  organization: DISI, Univ. of Trento, Trento, Italy
– sequence: 2
  givenname: Ephrem T.
  surname: Bekele
  fullname: Bekele, Ephrem T.
  email: bekele@disi.unitn.it
  organization: DISI, Univ. of Trento, Trento, Italy
– sequence: 3
  givenname: Fabrizio
  surname: Robol
  fullname: Robol, Fabrizio
  email: fabrizio.robol@disi.unitn.it
  organization: DISI, Univ. of Trento, Trento, Italy
– sequence: 4
  givenname: Andrea
  surname: Massa
  fullname: Massa, Andrea
  email: andrea.massa@unitn.it
  organization: DISI, Univ. of Trento, Trento, Italy
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conformal arrays
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Snippet Sparsening conformal arrangements is carried out through a versatile Multi-Task Bayesian Compressive Sensing (MT-BCS) strategy. The problem, formulated in a...
Sparsening conformal arrangements is carried out through a versatile Multi-Task Bayesian Compressive Sensing openbracket MT - BCS [ closebracket ] strategy....
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SubjectTerms 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
Title Sparsening Conformal Arrays Through a Versatile BCS-Based Method
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