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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Bibliographic Details
Published in:Electronics letters Vol. 51; no. 25; pp. 2141 - 2143
Main Authors: Lin, Jincheng, Ma, Xiaochuan, Yan, Shefeng, Jiang, Li
Format: Journal Article
Language:English
Published: The Institution of Engineering and Technology 10.12.2015
Subjects:
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 Access:Get full text
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Summary: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 model into the pattern synthesis problem, and it makes the synthesis more accurate than the conventional BCS algorithm. Moreover, the proposed algorithm has the advantage of high computational efficiency, since the BCS-based algorithms can be realised by the fast relevance vector machine. Numerical experiments show that the proposed algorithm has improved accuracy in terms of normalised mean square error.
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ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2015.2455