首页|Gibbs Sampling-based Sparse Estimation Method over Underwater Acoustic Channels

Gibbs Sampling-based Sparse Estimation Method over Underwater Acoustic Channels

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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.

Sparse bayesian learningChannel estimationVariational inferenceGibbs sampling

Wentao Tong、Wei Ge、Yizhen Jia、Jiaheng Zhang

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National Key Laboratory of Underwater Acoustic Technology,Harbin Engineering University,Harbin 150001,China

Key Laboratory for Polar Acoustics and Application of Ministry of Education,Harbin Engineering University,Harbin 150001,China

College of Underwater Acoustic Engineering,Harbin Engineering University,Harbin 150001,China

Qingdao Innovation and Development Center of Harbin Engineering University,Qingdao,266400,China

State Key Laboratory of Acoustics,Institute of Acoustics,Chinese Academy of Sciences,Beijing 100190,China

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Excellent Youth Science Fund of Heilongjiang Province

YQ2022F001

2024

10.1007/s11804-024-00415-4

哈尔滨工程大学学报(英文版)
哈尔滨工程大学

哈尔滨工程大学学报(英文版)

CSTPCD
影响因子：0.381
ISSN：1671-9433
年,卷(期)：2024.23(2)
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