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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Veröffentlicht in:	IEEE transactions on antennas and propagation Jg. 60; H. 5; S. 2309 - 2326
Hauptverfasser:	Oliveri, G., Carlin, M., Massa, A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.05.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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 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
ISSN:	0018-926X, 1558-2221
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Beschreibung
Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Article-2 ObjectType-Feature-1 content type line 23
ISSN:	0018-926X 1558-2221
DOI:	10.1109/TAP.2012.2189742