为解决模态混叠问题，提取更为全面的舰船噪声特征，设计了基于集成经验模态分解算法的舰船噪声特征提取方法。利用非线性局部投影滤波方法处理舰船信号，利用集成经验模态分解算法分解滤波后的噪声信号，提取具有关键噪声特征的固有模态函数（IMF）分量；利用相关系数法计算各IMF分量和信号间的相关系数，保留相关系数大于设置门限阈值的IMF分量，根据排列熵提取全面的舰船噪声特征。实验证明，该方法可有效分解噪声信号，得到相关系数最高的IMF分量，获得理想舰船噪声特征。
To solve the problem of mode mixing and extract more comprehensive ship noise features, a ship noise feature extraction method based on integrated empirical mode decomposition was designed. Using nonlinear local projection filtering method to process ship signals, using integrated empirical mode decomposition algorithm to decompose the filtered noise signal, and extracting intrinsic mode function (IMF) components with key noise features; Using the correlation coefficient method to calculate the correlation coefficients between each IMF component and the signal, retaining IMF components with correlation coefficients greater than the set threshold, and extracting comprehensive ship noise features based on permutation entropy. Experimental results have shown that this method can effectively decompose noise signals, obtain the IMF component with the highest correlation coefficient, and obtain ideal ship noise characteristics.
2025,47(3): 172-175 收稿日期：2024-12-2
DOI：10.3404/j.issn.1672-7649.2025.03.029
分类号：TN911.7
基金项目：国家自然科学基金面上项目（62372397）；山西省自然基金面上项目（202203021221222,202203021221229）
作者简介：陈志强(1975-),男,博士,讲师,研究方向为智能信息处理
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