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

Anbang Zhao; Keren Wang; Juan Hui; Pengfei Song; Jiabin Guo

doi:10.1121/10.0025287

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

J Acoust Soc Am. 2024 Mar 1;155(3):2000-2013. doi: 10.1121/10.0025287.

Authors

Anbang Zhao^{1

2

3}, Keren Wang¹, Juan Hui^{1

2

3}, Pengfei Song¹, Jiabin Guo¹

Affiliations

¹ College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China.
² Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China.
³ Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University, Ministry of Industry and Information Technology; Harbin 150001, China.

PMID: 38470187
DOI: 10.1121/10.0025287

Abstract

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.