Variational Bayesian Inference Based Direction Of Arrival Estimation in Presence of Shallow Water Non-Gaussian Noise

FENG Xiao; ZHOU Mingzhang; ZHANG Xuebo; YE Kun; WANG Junfeng; SUN Haixin

doi:10.11999/JEIT211284

Volume 44 Issue 6

Jun. 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(6): 1887-1896

FENG Xiao, ZHOU Mingzhang, ZHANG Xuebo, YE Kun, WANG Junfeng, SUN Haixin. Variational Bayesian Inference Based Direction Of Arrival Estimation in Presence of Shallow Water Non-Gaussian Noise[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1887-1896. doi: 10.11999/JEIT211284

Citation:

FENG Xiao, ZHOU Mingzhang, ZHANG Xuebo, YE Kun, WANG Junfeng, SUN Haixin. Variational Bayesian Inference Based Direction Of Arrival Estimation in Presence of Shallow Water Non-Gaussian Noise[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1887-1896. doi: 10.11999/JEIT211284

Citation:

FENG Xiao, ZHOU Mingzhang, ZHANG Xuebo, YE Kun, WANG Junfeng, SUN Haixin. Variational Bayesian Inference Based Direction Of Arrival Estimation in Presence of Shallow Water Non-Gaussian Noise[J]. Journal of Electronics & Information Technology, 2022, 44(6): 1887-1896. doi: 10.11999/JEIT211284

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Variational Bayesian Inference Based Direction Of Arrival Estimation in Presence of Shallow Water Non-Gaussian Noise

doi: 10.11999/JEIT211284

1.
College of Information, Xiamen University, Xiamen 361005, China
2.
Northwest Normal University, Lanzhou 730070, China
3.
College of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin 300384, China

Funds: The National Natural Science Foundation of China (61971362)

Received Date: 2021-11-17
Accepted Date: 2022-03-03
Rev Recd Date: 2022-02-28

Available Online: 2022-03-07

Publish Date: 2022-06-21

Abstract

Abstract

Conventional Direction Of Arrival (DOA) estimators achieve satisfactory performance with the common assumptions of Gaussian noise. However, the impulsive noise exists in the shallow water extensively and does not follow the Gaussian distribution, which induce undesirable biases and degrade the performance of the conventional estimators. In the paper, a new DOA estimation method based on variational Bayesian inference in presence of shallow water non-Gaussian noise is proposed to improve the DOA estimation performance. Firstly, the multiple measurement vectors Sparse Signal Representation (SSR) model is formulated utilizing the sparsity of signal and impulsive noise. After that, the hierarchical Bayesian estimation framework is formulated which considers the common sparsity of signal and the independent sparsity of impulsive noise. Subsequently, the variational Bayesian inference is utilized to achieve the posterior estimations for the signal and impulsive noise. The SSR model incorporates the off-grid bias, and the root sparse Bayesian learning realizes to refine the bias and mitigate the basis mismatches. At last, the accurate DOA estimation is achieved through iterative updates and the effects of impulsive noise are mitigated. Simulations are used to verify that the proposed estimator achieves superior performance compared with state-of-art benchmarks.
- Direction Of Arrival (DOA) estimation,
- Sparse Bayesian learning,
- Variational Bayesian inference,
- Impulsive noise

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References(31)

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