Systems Engineering and Electronics ›› 2023, Vol. 46 ›› Issue (1): 334-344.doi: 10.12305/j.issn.1001-506X.2024.01.38

• Communications and Networks • Previous Articles    

Accurate underwater acoustic channel estimation based on Gaussian likelihood

Guang YANG1,2, Peiyue QIAO1,*, Junyan LIANG1, Zhengchang QIN1, Xiaodong GONG3,4, Xiuhui NI3,4   

  1. 1. Qingdao Technical Innovation Center for Underwater Acoustic Communication and Detection Equipment, School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266525, China
    2. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore
    3. Institute of Oceanographic Instrumentation, Shandong Academy of Sciences, Qingdao 266318, China
    4. National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute", Kyiv 03056, Ukraine
  • Received:2022-10-28 Online:2023-12-28 Published:2024-01-11
  • Contact: Peiyue QIAO

Abstract:

Aiming at the multi-path interference problem of the time-varying underwater acoustic channel, an accurate underwater acoustic channel estimation algorithm based on Gaussian likelihood (GL) is proposed. The Gauss probability density functions of the multiple segments are multiplied, the product result still follows the Gauss distribution, and the variance becomes smaller, leading to the improvement of channel estimation accuracy. The superimposed training (ST) scheme is used, where the training sequence and the symbol sequence are linearly superimposed, so that the training sequence can be continuously transmitted, thereby the real-time tracking of the time-varying channel is realized. The ST scheme, GL algorithm, and Turbo equalization are jointly performed in an iterative manner, where the estimated symbol sequence is used as a virtual training sequence to further improve the estimation and tracking performance of the channel. Accurate estimation and real-time tracking of the time-varying underwater acoustic channel are realized through multiple iteration calculation. Finally, the effectiveness of the proposed algorithm is verified by simulation and experimental results (the horizontal distance of the moving transceivers is 500 m and 5.5 km) in Jiaozhou Bay.

Key words: time-varying underwater acoustic channel, Gaussian likelihood (GL), superimposed training (ST) scheme, virtual training sequence

CLC Number: 

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