2‐D DOA Estimation Using Off‐Grid Sparse Learning via Iterative Minimization with L‐Parallel Coprime Array. Issue 6 (1st November 2018)

• Record Type: Journal Article
• Authors: Feng, Mingyue; He, Minghao; Han, Jun and CHEN Changxiao; Chen, Changxiao
• Is Part Of: Chinese journal of electronics. Volume 27:Issue 6(2018)
• Page Start: 1322
• Page End: 1328
• Publication Date: 2018-11-01
• Subjects: Two‐dimensional direction‐of‐arrival (2D DOA) estimation -- Off‐grid problem -- L‐parallel coprime array -- Off‐grid sparse learning via iterative minimization (OGSLIM); Bayes methods -- direction‐of‐arrival estimation -- iterative methods -- learning (artificial intelligence) -- minimisation; OGSLIM algorithm -- L‐parallel coprime array -- 2‐D DOA estimation -- iterative minimization algorithm -- linear coprime array -- off‐grid sparse learning -- two‐dimensional direction‐of‐arrival estimation -- off‐grid parameter -- underdetermined signals -- correlated signals; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2017.11.002
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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.