Open Access2‐D DOA Estimation Using Off‐Grid Sparse Learning via Iterative Minimization with L‐Parallel Coprime Array
Author(s) -
Feng Mingyue,
He Minghao,
Han Jun and CHEN Changxiao,
Chen Changxiao
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.11.002
Subject(s) - coprime integers , underdetermined system , direction of arrival , computer science , algorithm , minification , grid , sparse array , mathematics , telecommunications , antenna (radio) , geometry , programming language
An L‐parallel coprime array is designed and an Off‐grid sparse learning via iterative minimization (OGSLIM) algorithm is proposed in order to improve the performance of Two‐dimensional direction‐of‐arrival (2‐D DOA) estimation. The L‐parallel coprime array consists of two parts, one is a parallel coprime array and the other one is a linear coprime array perpendicular to the parallel coprime array. The OGSLIM algorithm is based on sparse Bayesian framework and can learn the offi‐grid parameter. Theory analysis and simulation results demonstrate that 2‐D DOA estimation using OGSLIM algorithm with L‐parallel coprime array can lead to higher estimation accuracy and resolution, it also fits to the underdetermined signals and correlated signals.