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基于经验模态分解的船舶辐射噪声特征提取研究

Research on feature extraction of ship radiation noise based on empirical mode decomposition

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【作者】 崔建忠姜淼

【Author】 CUI Jianzhong;JIANG Miao;College of Computer Science and Technology, Qingdao University;

【机构】 青岛大学计算机科学技术学院

【摘要】 船舶辐射噪声中的线谱分量具有较高的强度和稳定度,通过测定并跟踪线谱,可以精确地估计目标的运动参数。为此,研究基于经验模态分解的船舶辐射噪声特征提取方法。首先,构建船舶辐射噪声数学模型,用于获取船舶辐射噪声信号;然后,运用经验模态分解方法提取船舶辐射噪声的7个经验模态分量和1个余项,并计算船舶辐射噪声经验模态分量的样本熵;最后,选择样本熵最大的船舶辐射噪声检验模态分量作为船舶辐射噪声的特征。实验结果表明,该方法可以准确获得船舶运行设备的辐射噪声,且具备较强的舰船辐射噪声分帧能力。该方法还可以有效地将舰船辐射噪声分解成不同经验模态分量,大大降低了船舶辐射噪声余项接近零。

【Abstract】 The line spectrum component in ship radiated noise has high intensity and stability. By measuring and tracking the line spectrum, the motion parameters of the target can be accurately estimated. To this end, a ship radiation noise feature extraction method based on empirical mode decomposition is studied. Firstly, construct a mathematical model for ship radiated noise to obtain ship radiated noise signals. Then, the empirical mode decomposition method is used to extract 7 empirical mode components and 1 residual term of ship radiation noise, and the sample entropy of the empirical mode components of ship radiation noise is calculated using ice. Finally, select the modal component of ship radiation noise with the highest sample entropy as the feature of ship radiation noise. The experimental results show that this method can accurately obtain the radiated noise of ship operating equipment and has strong ability to frame ship radiated noise. This method can also effectively decompose ship radiation noise into different empirical mode components, greatly reducing the residual of ship radiation noise to close to zero.

【基金】 2022年山东省教育厅科学技术研究项目(一般)(GJJ220312)
  • 【文献出处】 舰船科学技术 ,Ship Science and Technology , 编辑部邮箱 ,2024年18期
  • 【分类号】U661.44
  • 【下载频次】39
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