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Citation:
　Wentao Tong,Wei Ge,Yizhen Jia,et al.Gibbs Sampling-based Sparse Estimation Method over Underwater Acoustic Channels[J].Journal of Marine Science and Application,2024,(2):434-442.[doi:10.1007/s11804-024-00415-4]
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Gibbs Sampling-based Sparse Estimation Method over Underwater Acoustic Channels

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Title:
Gibbs Sampling-based Sparse Estimation Method over Underwater Acoustic Channels
Author(s):
Wentao Tong1; 2; 3;  Wei Ge4; 5;  Yizhen Jia1; 2; 3;  Jiaheng Zhang1; 2; 3
Affilations:
Author(s):
Wentao Tong1; 2; 3;  Wei Ge4; 5;  Yizhen Jia1; 2; 3;  Jiaheng Zhang1; 2; 3
1 National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China;
2 Key Laboratory for Polar Acoustics and Application of Ministry of Education, Harbin Engineering University, Harbin 150001, China;
3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
Sparse bayesian learning|Channel estimation|Variational inference|Gibbs sampling
分类号:
-
DOI:
10.1007/s11804-024-00415-4
Abstract:
The estimation of sparse underwater acoustic (UWA) channels can be regarded as an inference problem involving hidden variables within the Bayesian framework. While the classical sparse Bayesian learning (SBL), derived through the expectation maximization (EM) algorithm, has been widely employed for UWA channel estimation, it still differs from the real posterior expectation of channels. In this paper, we propose an approach that combines variational inference (VI) and Markov chain Monte Carlo (MCMC) methods to provide a more accurate posterior estimation. Specifically, the SBL is first re-derived with VI, allowing us to replace the posterior distribution of the hidden variables with a variational distribution. Then, we determine the full conditional probability distribution for each variable in the variational distribution and then iteratively perform random Gibbs sampling in MCMC to converge the Markov chain. The results of simulation and experiment indicate that our estimation method achieves lower mean square error and bit error rate compared to the classic SBL approach. Additionally, it demonstrates an acceptable convergence speed.

References:

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Memo

Memo:
Received date: 2023-06-15;Accepted date: 2023-07-31。
Foundation item: This study is funded by the Excellent Youth Science Fund of Heilongjiang Province (Grant No.YQ2022F001).
Corresponding author: Wei Ge,E-mail:gewei@hrbeu.edu.cn
Last Update: 2024-05-28