摘要
随着我国海洋事业的不断发展,水声信号处理技术在海洋资源开发、海洋环境监测和海洋安全等领域发挥着越来越重要的作用。近年来,机器学习方法在水声信号处理的应用中取得了一定突破。该文全面综述了机器学习方法在水声通信、水声被动定位、水声探测中的研究进展,典型应用包括基于机器学习的水声信道估计、被动声呐定位以及水声目标识别等。海洋声学问题通常具有高度非线性、高度复杂的典型特点,机器学习方法凭借出色的自动化特征提取和模式识别能力,可在这类问题的处理过程中实现优秀的拟合及分类效果。同时需要指出,机器学习方法在水声信号处理中的应用还存在一些挑战和问题,如数据量不足、数据质量低、算法复杂度高等,为此该文提供了进一步研究和探索的思路。
With the continuous development of China’s maritime industry,underwater acoustic(UWA)signal processing technology is playing an increasingly significant role in fields such as marine resource exploitation,environmental monitoring,and maritime safety.In recent years,some progress has been made in the application of machine learning methods in UWA signal processing technology.This paper provides a comprehensive review of the progress of machine learning methods in UWA communication,UWA localization,and UWA detection,with typical applications including machine learning-based UWA channel estimation,passive sonar localization,and UWA target identification.The existing advances illustrate the excellent fitting and classification capabilities of machine learning with its excellent automated feature extraction and pattern recognition capabilities in dealing with such highly nonlinear and highly complex challenges as ocean acoustics.At the same time,it needs to be pointed out that there are still some challenges and problems in the application of machine learning methods in UWA signal processing technology,such as insufficient amount of data,low data quality,and high complexity of algorithms,which need further research and exploration.
作者
孟华
刘杨
李杰
李想
牛奔
MENG Hua;LIU Yang;LI Jie;LI Xiang;NIU Ben(91388 Troops of the Chinese People’s Liberation Army,Zhanjiang 524000,China)
出处
《应用声学》
北大核心
2025年第5期1382-1394,共13页
Journal of Applied Acoustics
关键词
水声信号处理
机器学习
水声通信
水声被动定位
水声探测
Underwater acoustic signal processing
Machine learning
Underwater acoustic communication
Underwater acoustic localization
Underwater acoustic detection
