电子学报 ›› 2022, Vol. 50 ›› Issue (1): 54-62.DOI: 10.12263/DZXB.20200369

• 学术论文 • 上一篇    下一篇

一种基于RCGAN的水声通信信号降噪方法

李勇斌, 王彬, 邵高平, 邵帅   

  1. 中国人民解放军战略支援部队信息工程大学,河南 郑州 450001
  • 收稿日期:2020-04-15 修回日期:2020-11-13 出版日期:2022-01-25 发布日期:2022-01-25
  • 作者简介:李勇斌 男,1996年1月出生,江西婺源人.2018年毕业于中国人民解放军战略支援部队信息工程大学,获工学学士学位.现为战略支援部队信息工程大学研究生,研究方向为水声通信信号分析与处理. E-mail:xcwwbin@163.com
    王 彬 女,1971年1月出生,河南郑州人.2007年在原信息工程大学获工学博士学位.现为中国人民解放军战略支援部队信息工程大学副教授、硕士生导师.研究方向为软件无线电、无线通信中信道盲均衡等.

A Method of Noise Reduction for Underwater Acoustic Communication Signal Based on RCGAN

LI Yong-bin, WANG Bin, SHAO Gao-ping, SHAO Shuai   

  1. PLA Strategic Support Force Information Engineering University,Zhengzhou,Henan 450001,China
  • Received:2020-04-15 Revised:2020-11-13 Online:2022-01-25 Published:2022-01-25

摘要:

针对复杂海洋环境噪声中水声通信信号数据稀缺条件下的水声通信信号降噪问题,提出一种基于相对条件生成对抗网络(Relativistic Conditional Generative Adversarial Networks,RCGAN)的水声通信信号降噪方法.该方法利用相对条件生成对抗网络具有降噪能力的特点,通过引入扩张卷积结构,构造了适用于水声通信信号的降噪模型,提升了对不同海洋环境噪声的降噪能力;为了解决样本数据稀缺条件下的网络训练问题,根据生成对抗网络特点设计了两步迁移学习策略,并构造了基于数据模型迁移的迁移数据训练集.仿真实验和实际信号测试结果表明,该方法对不同分布特性的海洋环境噪声具有稳健性,能够大幅度降低对目标信号训练数据数量的要求,降噪效果优于现有算法.

关键词: 水声通信信号, 降噪, RCGAN, 扩张卷积, 迁移学习

Abstract:

Aiming at the problem of underwater acoustic communication(UWAC) signal denoising under the conditions of complex marine ambient noise and scarce data, an approach based on relativistic conditional generative adversarial networks(RCGAN) is proposed. Based on the noise reduction capability of the RCGAN, this approach constructs a noise reduction model by introducing the structure of dilated convolution. To overcome the insufficient training data, a two-step transfer learning strategy is designed according to the characteristics of the RCGAN, and a transfer data training set is built based on the presented transfer data model. The results of simulation experiments and practical signal tests both demonstrate the robustness of the proposed method to complex marine environments. Furthermore, the requirements for target signal training data is significantly reduced, with a better performance.

Key words: underwater acoustic communication signal, noise reduction, relativistic conditional generative adversarial networks(RCGAN), dilated convolution, transfer learning

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