• 中国科技核心期刊
  • Scopus收录期刊
  • DOAJ收录期刊
  • JST收录期刊
  • Euro Pub收录期刊
Volume 26 Issue 1
 
Turn off MathJax
Article Contents
Article Navigation >  Journal of Unmanned Undersea Systems  > 2018  >  26(1): 028-34
LI Yu-xing, LI Ya-an, CHEN Xiao, YU Jing. A Feature Extraction Method of Ship-Radiated Noise Based on Sample Entropy and Ensemble Empirical Mode Decomposition[J]. Journal of Unmanned Undersea Systems, 2018, 26(1): 028-34. doi: 10.11993/j.issn.2096-3920.2018.01.005
Citation: LI Yu-xing, LI Ya-an, CHEN Xiao, YU Jing. A Feature Extraction Method of Ship-Radiated Noise Based on Sample Entropy and Ensemble Empirical Mode Decomposition[J]. Journal of Unmanned Undersea Systems, 2018, 26(1): 028-34. doi: 10.11993/j.issn.2096-3920.2018.01.005
shu

A Feature Extraction Method of Ship-Radiated Noise Based on Sample Entropy and Ensemble Empirical Mode Decomposition

doi: 10.11993/j.issn.2096-3920.2018.01.005

  • School of Marine Science and Technology, Northwestern Polytechnical University, Xi′an 710072, China

  • Received Date: 2017-07-06
  • Rev Recd Date: 2017-08-30
  • Publish Date: 2018-02-28
    Abstract
  • To realize the feature extraction of ship-radiated noise in complex ocean environment, the sample entropy is used to extract the features of three types of ship-radiated noise(SRN). Because the sample entropy only analyzes the SRN signal in single scale and it cannot distinguish different types of ships effectively, a new method of SRN feature extraction is presented based on sample entropy and ensemble empirical mode decomposition(EEMD). Firstly, three types of SRN signals are decomposed with EEMD, and the sample entropy of each intrinsic mode function(IMF) is analyzed to select the IMF sample entropy with the highest energy as the feature parameter. Then, by comparing the IMF highest-energy sample entropy of a certain number of the above three types of SRN signals with the sample entropy of SRN signal, it is discovered that the IMF sample entropy with the highest energy is at the same level for similar ship types, but is quite different for different ship types. Test results show that taking the IMF sample entropy with the highest energy as the feature parameter can obtain better separability for ships, compared with SRN sample entropy.

     

    • FullText(HTML)
  • loading
    • References(15)
  • [1]
    李新欣. 船舶及鲸类声信号特征提取和分类识别研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.
    [2]
    Huang N E, Shen Z, Long S R, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J]. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.
    [3]
    Wu Z H, Huang N E. A Study of the Characteristics of White Noise Using the Empirical Mode Decomposition Method[J]. Proceedings of the Royal Society of London A, 2004, 460(2046): 1597-1611.
    [4]
    张志刚, 石晓辉, 陈哲明, 等. 基于改进EMD和滑动峰态算法的滚动轴承故障特征提取[J]. 振动与冲击, 2012, 31(22): 80-83.

    Zhang Zhi-gang, Shi Xiao-hui, Chen Zhe-ming, et al. Fault Feature Extraction of Rolling Element Bearing Based on Improved EMD and Sliding Kurtosis Algorithm[J]. Journal of Vibration and Shock, 2012, 31(22): 80-83.
    [5]
    李小俚, 崔素媛, Sleigh J W. 基于希尔伯特黄熵的麻醉深度估计[J]. 中国生物医学工程学报, 2008, 27(5): 689- 694.

    Li Xiao-Li, Cui Su-Yuan, Sleigh J W. Estimation of Anesthetic Depth with Hilbert-Huang Entropy[J]. Chinese Journal of Biomedical Engineering, 2008, 27(5): 689-694.
    [6]
    薛春芳, 侯威, 赵俊虎, 等. 集合经验模态分解在区域降水变化多尺度分析及气候变化响应研究中的应用[J]. 物理学报, 2013, 62(10): 496-503.

    Xue Chun-Fang, Hou Wei, Zhao Jun-Hu, et al. The Application of Ensemble Empirical Mode Decomposition Method in Multiscale Analysis of Region Precipitation and Its Response to the Climate Change[J]. Acta Physica Sinica, 2013, 62(10): 496-503.
    [7]
    高云超. 希尔伯特-黄变换在水声信号处理中的应用研究[D]. 哈尔滨: 哈尔滨工程大学, 2009.
    [8]
    李秀坤, 谢磊, 秦宇. 应用希尔伯特黄变换的水下目标特征提取[J]. 哈尔滨工程大学学报, 2009, 30(5): 542- 546.

    Li Xiu-kun, Xie Lei, Qin Yu. Underwater Target Feature Extraction Using Hilbert-Huang Transform[J]. Journal of Harbin Engineering University, 2009, 30(5): 542-546.
    [9]
    刘深, 张小蓟, 牛奕龙, 等. 基于IMF能量谱的水声信号特征提取与分类[J]. 计算机工程与应用, 2014, 50(3): 203-206, 226.

    Liu Shen, Zhang Xiao-ji, Niu Yi-long, et al. Feature Ex-traction and Classification Experiment of Underwater Acoustic Signals Based on Energy Spectrum of IMFs[J]. Computer Engineering and Applications, 2014, 50(3): 203-206, 226.
    [10]
    Pincus S M. Approximate Entropy as a Measure of System Complexity[J]. Proceedings of the National Academy of Sciences of the United States of America, 1991, 88(6): 2297-2301.
    [11]
    Wei Q, Liu Q, Fan S Z. Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy[J]. Entropy, 2013, 15(9): 3458- 3470.
    [12]
    杨宏, 李亚安, 李国辉, 等. 基于集合经验模态分解的舰船辐射噪声能量分析[J]. 振动与冲击, 2015, 34(16): 55-59.

    Yang Hong, Li Ya-an, Li Guo-hui, et al. Energy Analysis of Ship Radiated Noise Based on Ensemble Empirical Mode Decomposition[J]. Journal of Vibration and Shock, 2015, 34(16): 55-59.
    [13]
    秦娜, 金炜东, 黄进, 等. 基于EEMD样本熵的高速列车转向架故障特征提取[J]. 西南交通大学学报, 2014, 49(1): 27-32.

    Qin Na, Jin Wei-dong, Huang Jin, et al. Feature Extraction of High Speed Train Bogie Based on Ensemble Empirical Mode Decomposition and Sample Entropy[J]. Journal of Southwest Jiaotong University, 2014, 49(1): 27-32.
    [14]
    赵志宏, 杨绍普. 一种基于样本熵的轴承故障诊断方法[J]. 振动与冲击 2012, 31(6): 136-140.

    Zhao Zhi-hong, Yang Shao-pu. Sample Entropy Based Ro- ller Bearing Fault Diagnosis Method[J]. Journal of Vibration and Shock, 2012, 31(6): 136-140.
    [15]
    Wu Z H, Huang N E. Ensemble Empirical Mode Decom-position: A Noise-assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article Views(810) PDF Downloads(310) Cited by()
    Proportional views
    Related
    Service
    Subscribe
    Copyright：陕ICP备09015163号Address:No. 96, Jinye Road, Xi'an, Shaanxi
    Code：710077Tel: 029-88327279QQ: 767358370
    Email: bianjibu705@sohu.com 767358370@qq.com
    Email Alert RSS
    Taobao
    Micro

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return