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...

Celý popis

Uloženo v:
Podrobná bibliografie
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
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0013-5194
1350-911X
1350-911X
DOI:10.1049/el.2015.2455