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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Vydáno v:	Electronics letters Ročník 51; číslo 25; s. 2141 - 2143
Hlavní autoři:	Lin, Jincheng, Ma, Xiaochuan, Yan, Shefeng, Jiang, Li
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 10.12.2015
Témata:	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
On-line přístup:	Získat plný text
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