Root Sparse Bayesian Learning for Off-Grid DOA Estimation

被引：203

作者：

Dai, Jisheng ^{[1
,2
]}

Bao, Xu ^{[3
]}

Xu, Weichao ^{[4
]}

Chang, Chunqi ^{[5
,6
]}

机构：

[1] Jiangsu Univ, Dept Elect Engn, Zhenjiang 212013, Peoples R China

[2] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Jiangsu, Peoples R China

[3] Jiangsu Univ, Dept Telecommun Engn, Zhenjiang 212013, Peoples R China

[4] Guangdong Univ Technol, Dept Automat Control, Guangzhou 510006, Guangdong, Peoples R China

[5] Shenzhen Univ, Sch Biomed Engn, Shenzhen 518060, Peoples R China

[6] Guangdong Key Lab Biomed Measurements & Ultrasoun, Shenzhen 518060, Peoples R China

来源：

IEEE SIGNAL PROCESSING LETTERS | 2017年 / 24卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Direction-of-arrival (DOA); polynomial root; sparse Bayesian learning (SBL); sparse representation; ARRIVAL ESTIMATION; ARRAYS;

D O I：

10.1109/LSP.2016.2636319

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 performance of the existing sparse Bayesian learning (SBL) methods for off-grid direction-of-arrival (DOA) estimation is dependent on the tradeoff between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, which adopts a coarse grid and considers the sampled locations in the coarse grid as the adjustable parameters. We utilize an expectation-maximization algorithm to iteratively refine this coarse grid and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced, and the modeling error can be almost eliminated. © 1994-2012 IEEE.

引用

页码：46 / 50

页数：5

共 12 条

[1] Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies [J].

Carlin, Matteo ;

Rocca, Paolo ;

Oliveri, Giacomo ;

Viani, Federico ;

Massa, Andrea .

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION, 2013, 61 (07) :3828-3838

[2] Direction-of-Arrival Estimation Via Real-Valued Sparse Representation [J].

Dai, Jisheng ;

Xu, Xin ;

Zhao, Dean .

IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS, 2013, 12 :376-379

[3] Real-valued DOA estimation for spherical arrays using sparse Bayesian learning [J].

Huang, Qinghua ;

Zhang, Guangfei ;

Fang, Yong .

SIGNAL PROCESSING, 2016, 125 :79-86

[4] Bayesian compressive sensing [J].

Ji, Shihao ;

Xue, Ya ;

Carin, Lawrence .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2008, 56 (06) :2346-2356

[5] Two decades of array signal processing research - The parametric approach [J].

Krim, H ;

Viberg, M .

IEEE SIGNAL PROCESSING MAGAZINE, 1996, 13 (04) :67-94

[6] A sparse signal reconstruction perspective for source localization with sensor arrays [J].

Malioutov, D ;

Çetin, M ;

Willsky, AS .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2005, 53 (08) :3010-3022

[7] Sparse Bayesian learning and the relevance vector machine [J].

Tipping, ME .

JOURNAL OF MACHINE LEARNING RESEARCH, 2001, 1 (03) :211-244

[8] Sparse Bayesian learning for basis selection [J].

Wipf, DP ;

Rao, BD .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2004, 52 (08) :2153-2164

[9] Off-Grid Direction of Arrival Estimation Using Sparse Bayesian Inference [J].

Yang, Zai ;

Xie, Lihua ;

Zhang, Cishen .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2013, 61 (01) :38-43

[10] Off-grid DOA estimation using array covariance matrix and block-sparse Bayesian learning [J].

Zhang, Yi ;

Ye, Zhongfu ;

Xu, Xu ;

Hu, Nan .

SIGNAL PROCESSING, 2014, 98 :197-201

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