Open AccessPattern synthesis of sparse linear array by off‐grid Bayesian compressive sampling
Author(s) -
Lin Jincheng,
Ma Xiaochuan,
Yan Shefeng,
Jiang Li
Publication year - 2015
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2015.2455
Subject(s) - compressed sensing , algorithm , sampling (signal processing) , grid , computer science , bayesian probability , sparse grid , mathematics , artificial intelligence , geometry , filter (signal processing) , computer vision
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.