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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Bibliographic Details
Published in:	IEEE transactions on antennas and propagation Vol. 60; no. 5; pp. 2309 - 2326
Main Authors:	Oliveri, G., Carlin, M., Massa, A.
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:	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
Online Access:	Get full text
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