Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling
An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task ( MT ) Bayesian compressive sensing ( BCS ) theory to enable the design of complex...
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| Published in | IEEE transactions on antennas and propagation Vol. 60; no. 5; pp. 2309 - 2326 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York, NY
IEEE
01.05.2012
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-926X 1558-2221 |
| DOI | 10.1109/TAP.2012.2189742 |
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| Abstract | An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task ( MT ) Bayesian compressive sensing ( BCS ) theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine ( RVM ) technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of and it gives some indications about the reliability, flexibility, and numerical efficiency of the MT - BCS approach also in comparison with state-of-the-art sparse-arrays synthesis methods. |
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| AbstractList | An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task ( MT ) Bayesian compressive sensing ( BCS ) theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine ( RVM ) technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of and it gives some indications about the reliability, flexibility, and numerical efficiency of the MT - BCS approach also in comparison with state-of-the-art sparse-arrays synthesis methods. An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task ( MT ) Bayesian compressive sensing ( BCS ) theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine ( RVM ) technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of [Ref 18] and it gives some indications about the reliability, flexibility, and numerical efficiency of the MT - BCS approach also in comparison with state-of-the-art sparse-arrays synthesis methods. An innovative method for the synthesis of maximally sparse linear arrays matching arbitrary reference patterns is proposed. In the framework of sparseness constrained optimization, the approach exploits the multi-task [Formula Omitted] Bayesian compressive sensing [Formula Omitted] theory to enable the design of complex non-Hermitian layouts with arbitrary radiation and geometrical constraints. By casting the pattern matching problem into a probabilistic formulation, a Relevance-Vector-Machine [Formula Omitted] technique is used as solution tool. The numerical assessment points out the advances of the proposed implementation over the extension to complex patterns of and it gives some indications about the reliability, flexibility, and numerical efficiency of the [Formula Omitted] approach also in comparison with state-of-the-art sparse-arrays synthesis methods. |
| Author | Oliveri, G. Massa, A. Carlin, M. |
| Author_xml | – sequence: 1 givenname: G. surname: Oliveri fullname: Oliveri, G. email: giacomo.oliveri@ing.unitn.it organization: ELEDIA Res. Center DISI, Univ. of Trento, Trento, Italy – sequence: 2 givenname: M. surname: Carlin fullname: Carlin, M. email: matteo.carlin@disi.unitn.it organization: ELEDIA Res. Center DISI, Univ. of Trento, Trento, Italy – sequence: 3 givenname: A. surname: Massa fullname: Massa, A. email: andrea.massa@ing.unitn.it organization: ELEDIA Res. Center DISI, Univ. of Trento, Trento, Italy |
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| Keywords | Performance evaluation Sparse array State of the art Probabilistic approach complex-weight pattern Hermite interpolation Bayesian compressive sampling linear arrays Pattern recognition Vector method Flexibility Implementation Constrained optimization Array synthesis Multiple task Relevance criterion shaped-beam pattern, sparse arrays Linear antenna arrays Reliability Pattern matching Beam shaping Compressed sensing Antenna synthesis |
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| SubjectTerms | Antennas Applied sciences Array synthesis Arrays Assessments Bayesian analysis Bayesian compressive sampling Bayesian methods complex-weight pattern Electromagnetism Engineering Sciences Exact sciences and technology Layout Linear arrays Matching Mathematical models Pattern matching Probabilistic logic Radiocommunications Sampling shaped-beam pattern Signal and Image processing sparse arrays Sparse matrices Synthesis Telecommunications Telecommunications and information theory Vectors |
| Title | Complex-Weight Sparse Linear Array Synthesis by Bayesian Compressive Sampling |
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