Estimating the direction of arrival of spatially spread sources using block-sparse Bayesian learning with an extended dictionary

被引：1

作者：

Zhao, Anbang ^{[1
,2
,3
]}

Wang, Keren ^{[1
]}

Hui, Juan ^{[1
,2
,3
]}

Song, Pengfei ^{[1
]}

Guo, Jiabin ^{[1
]}

机构：

[1] Harbin Engn Univ, Coll Underwater Acoust Engn, Harbin 150001, Peoples R China

[2] Harbin Engn Univ, Acoust Sci & Technol Lab, Harbin 150001, Peoples R China

[3] Harbin Engn Univ, Key Lab Marine Informat Acquisit & Secur, Minist Ind & Informat Technol, Harbin 150001, Peoples R China

来源：

JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA | 2024年 / 155卷 / 03期

关键词：

LOCALIZATION; ALGORITHM;

D O I：

10.1121/10.0025287

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Estimating the direction of arrival (DOA) of spatially spread sources is a significant challenge in array signal processing. This work introduces an effective method within the sparse Bayesian framework to tackle this issue. A spatially spread source is modeled using a multi-dimensional Slepian signal subspace that expands the dictionary and results in a block-sparse structured solution. By taking advantage of block-sparse Bayesian learning, parameter estimation becomes feasible. A complex Gaussian posterior is derived under a multi-snapshot block-sparse framework with a complex Gaussian prior and varying noise conditions. The hyperparameters are estimated using the expectation-maximization algorithm. Through numerical tests and sea test data evaluations, the proposed method shows superior energy focusing for spatially spread signals. Under limited snapshots and challenging signal-to-noise ratios, the current method can still offer precise DOA determination for spatially spread sources. (c) 2024 Acoustical Society of America.

引用

页码：2000 / 2013

页数：14

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