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METHODS article

Front. Mar. Sci.
Sec. Ocean Observation
Volume 11 - 2024 | doi: 10.3389/fmars.2024.1362416
This article is part of the Research Topic Ocean Observation based on Underwater Acoustic Technology-Volume II View all 23 articles

A TMSBL underwater acoustic channel estimation method based on dictionary learning denoising

Provisionally accepted
Chuanxi Xing Chuanxi Xing 1,2Yanling Ran Yanling Ran 1*Mao Lu Mao Lu 1Guangzhi Tan Guangzhi Tan 1Qiang Meng Qiang Meng 1
  • 1 Yunnan Minzu University, Kunming, Yunnan Province, China
  • 2 Yunnan Key Laboratory of Unmanned Autonomous Systems, Kunming, China

The final, formatted version of the article will be published soon.

    The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse multipath channel. However, the complexity of the algorithm is high, the signal-to-noise ratio (SNR) of shallow-sea underwater acoustic communication is low, and the estimation performance of the TMSBL algorithm is greatly affected by noise. To address this problem, an improved TMSBL underwater acoustic channel estimation method based on a dictionary learning noise reduction algorithm is proposed. Firstly, the K-Singular Value Decomposition (K-SVD) dictionary learning method is used to reduce the noise of the received pilot matrix, reducing the influence of noise on the signal. Then, the Generalized Orthogonal Matching Pursuit (GOMP) channel estimation method is combined to obtain a priori information such as the perceptual matrix and hyperparameter matrix for TMSBL channel estimation; and the noise variance is obtained by using the null subcarrier calculation instead of iteratively updating the noise variance in the TMSBL, to improve the estimation accuracy and reduce the algorithmic complexity. Finally, the TMSBL channel estimation method is used to estimate the underwater acoustic channels of different symbols jointly. The simulation results show that the normalized mean square error of the channel estimation of the improved TMSBL method is reduced by about 92.2% compared with the TMSBL algorithm, obtaining higher estimation accuracy; running time is reduced by about 45.6%, and there is also better performance in terms of the running speed, which provides a reference for underwater acoustic channel estimation.

    Keywords: Underwater acoustic channel estimation, temporally multiple sparse Bayesian learning, K-SVD dictionary learning, underwater sparse channel estimation, orthogonal frequency division multiplexing

    Received: 28 Dec 2023; Accepted: 29 Aug 2024.

    Copyright: © 2024 Xing, Ran, Lu, Tan and Meng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Yanling Ran, Yunnan Minzu University, Kunming, 650031, Yunnan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.