Grid-less wideband direction of arrival estimation based on variational Bayesian inference

Rui Dou; Feilong Ding; Xi Chen; Jian Wang; Deyong Yu; Yuangui Tang

doi:10.1121/10.0025284

Grid-less wideband direction of arrival estimation based on variational Bayesian inference

J Acoust Soc Am. 2024 Mar 1;155(3):2087-2098. doi: 10.1121/10.0025284.

Authors

Rui Dou^{1

2

3}, Feilong Ding⁴, Xi Chen^{1

2

3}, Jian Wang^{1

2

3}, Deyong Yu^{1

2

3}, Yuangui Tang^{1

2

3}

Affiliations

¹ State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
² Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.
³ Key Laboratory of Marine Robotics, Liaoning Province, Shenyang 110169, China.
⁴ The Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China.

PMID: 38483207
DOI: 10.1121/10.0025284

Abstract

Many recent works have addressed the problem of wideband direction of arrival (DOA) estimation using grid-less sparse techniques, and these methods have been shown to outperform the traditional wideband DOA estimation methods. However, these methods often suffer from the problem of requiring manual parameter tuning or high computational complexity, which reduces their practicality. To alleviate this problem, a grid-less wideband DOA estimation method based on variational Bayesian inference is proposed in this paper. The method approximates the posterior probability density function of DOA with the help of variational Bayesian inference, which does not require manual adjustment of parameters and can obtain accurate DOA estimation results with low computational complexity. Numerical simulations and real measurement data processing show that the proposed method has a higher DOA estimation accuracy than other grid-less wideband methods while providing higher computational speed.