Sparse Bayesian learning for off-grid DOA estimation with nested arrays

被引：53
|
作者：
Chen, Fangfang [1 ]
Dai, Jisheng [1 ]
Hu, Nan [2 ]
Ye, Zhongfu [3 ]
机构：
[1] Jiangsu Univ, Dept Elect Engn, Zhenjiang 212013, Peoples R China
[2] Soochow Univ, Sch Elect & Informat Engn, Suzhou 215006, Peoples R China
[3] Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei 230027, Anhui, Peoples R China
来源：
DIGITAL SIGNAL PROCESSING | 2018年 / 82卷
基金：
中国国家自然科学基金;
关键词：
Sparse Bayesian learning (SBL); DOA estimation; Nested array; Off-grid; ARRIVAL ESTIMATION; SOURCE LOCALIZATION; PERSPECTIVE;
D O I：
10.1016/j.dsp.2018.08.004
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The existing off-grid sparse Bayesian learning (SBL) DOA estimation method for nested arrays suffers from two major drawbacks: reduced array aperture and high modeling error. To solve these issues, a new data model formulation is first presented in this paper, in which we take the noise variance as a part of the unknown signal of interest, so as to learn its value by the Bayesian inference inherently. Then, we provide a novel grid refining procedure to eliminate the modeling error caused by off-grid gap, where we consider the locations of grid points as adjustable parameters and proceed to refine the grid point iteratively. Simulation results demonstrate that our method significantly improves the DOA estimation performance especially using a coarse grid. (C) 2018 Elsevier Inc. All rights reserved.
引用
收藏
页码：187 / 193
页数：7
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