Pattern synthesis of sparse linear array by off-grid Bayesian compressive sampling

An off-grid (OG) pattern synthesis algorithm for sparse non-uniform linear arrays is presented. It is based on Bayesian compressive sampling (BCS), and the design of maximally sparse linear arrays for the given reference patterns can be obtained. The proposed algorithm novelly introduces the OG mode...

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Veröffentlicht in:	Electronics letters Jg. 51; H. 25; S. 2141 - 2143
Hauptverfasser:	Lin, Jincheng, Ma, Xiaochuan, Yan, Shefeng, Jiang, Li
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 10.12.2015
Schlagworte:	Algorithms array signal processing Bayes methods Bayesian analysis BCS‐based algorithm compressed sensing Computational efficiency learning (artificial intelligence) Linear arrays Mathematical models mean square error methods Mean square values normalised mean square error off‐grid Bayesian compressive sampling OG pattern synthesis algorithm Radar, sonar and navigation relevance vector machine Sampling sparse linear array pattern synthesis Synthesis relevance vector machine normalised mean square error compressed sensing sparse linear array pattern synthesis BCS-based algorithm computational efficiency Bayes methods learning (artificial intelligence) OG pattern synthesis algorithm mean square error methods off-grid Bayesian compressive sampling array signal processing
ISSN:	0013-5194, 1350-911X, 1350-911X
Online-Zugang:	Volltext
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