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Open AccessArticle

Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays

by 1,2,3,4, 1,2,3,4, 1,2,3,4,*, 1,2,3,4 and 1,2,3,4
1
Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
2
Key Laboratory of Marine Information Acquisition and Security, Harbin Engineering University, Ministry of Industry and Information Technology, Harbin 150001, China
3
College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
4
Qingdao Haina Underwater Information Technology Co., Ltd., Qingdao 266500, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2021, 9(2), 127; https://doi.org/10.3390/jmse9020127
Received: 24 December 2020 / Revised: 24 January 2021 / Accepted: 25 January 2021 / Published: 27 January 2021
(This article belongs to the Section Physical Oceanography)
Direction-of-arrival (DOA) estimation in a spatially isotropic white noise background has been widely researched for decades. However, in practice, such as underwater acoustic ambient noise in shallow water, the ambient noise can be spatially colored, which may severely degrade the performance of DOA estimation. To solve this problem, this paper proposes a DOA estimation method based on sparse Bayesian learning with the modified noise model using acoustic vector hydrophone arrays. Firstly, an applicable linear noise model is established by using the prolate spheroidal wave functions (PSWFs) to characterize spatially colored noise and exploiting the excellent performance of the PSWFs in extrapolating band-limited signals to the space domain. Then, using the proposed noise model, an iterative method for sparse spectrum reconstruction is developed under a sparse Bayesian learning (SBL) framework to fit the actual noise field received by the acoustic vector hydrophone array. Finally, a DOA estimation algorithm under the modified noise model is also presented, which has a superior performance under spatially colored noise. Numerical results validate the effectiveness of the proposed method. View Full-Text
Keywords: DOA estimation; spatially colored noise; SBL; PSWFs DOA estimation; spatially colored noise; SBL; PSWFs
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MDPI and ACS Style

Liang, G.; Shi, Z.; Qiu, L.; Sun, S.; Lan, T. Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays. J. Mar. Sci. Eng. 2021, 9, 127. https://doi.org/10.3390/jmse9020127

AMA Style

Liang G, Shi Z, Qiu L, Sun S, Lan T. Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays. Journal of Marine Science and Engineering. 2021; 9(2):127. https://doi.org/10.3390/jmse9020127

Chicago/Turabian Style

Liang, Guolong; Shi, Zhibo; Qiu, Longhao; Sun, Sibo; Lan, Tian. 2021. "Sparse Bayesian Learning Based Direction-of-Arrival Estimation under Spatially Colored Noise Using Acoustic Hydrophone Arrays" J. Mar. Sci. Eng. 9, no. 2: 127. https://doi.org/10.3390/jmse9020127

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