基于特征模式分解的水声目标特征提取方法

Zipeng Li, Yongqiang Ji, Bingyong Guo, Kunde Yang

科研成果: 期刊稿件文章同行评审

3 引用 (Scopus)

摘要

This work aims to address the classification and identification problem of underwater targets in complex underwater acoustic environments and proposes a novel feature-mode decomposition method for the weak feature extraction of underwater acoustic targets. In this method, correlative kurtosis is used as the optimal decomposition parameter of the optimization target to realize the optimal decomposition of the original underwater acoustic signals. Then, modes are fused in accordance with the similarity of subsignals to enhance feature expression, thus realizing the accurate recognition of underwater acoustic targets in complex environments. Sea experiments show that the accuracy of underwater acoustic target recognition by the proposed method reaches 90. 1%, which is 12. 5% higher than that of underwater acoustic target recognition by traditional methods.

投稿的翻译标题FMD-based feature extraction of underwater acoustic targets
源语言繁体中文
页(从-至)1542-1548
页数7
期刊Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University
44
9
DOI
出版状态已出版 - 9月 2023

关键词

  • correlative kurtosis
  • empirical mode decomposition
  • feature mode decomposition
  • mode fusion
  • parameter optimization
  • radiated noise
  • underwater acoustic target
  • variational mode decomposition

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