To improve the feature extraction of ship-radiated noise in a complex ocean environment, a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise (CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy (RCMFDE) is proposed. CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal. In addition, RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory. Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by CEEMDASN. Energy distribution ratio (EDR) and average energy distribution ratio (AEDR) of all IMF components are calculated. Then, the IMF with the minimum difference between EDR and AEDR (MEDR) is selected as characteristic IMF. The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise. Finally, these feature vectors are sent to self-organizing map (SOM) for classifying and identifying. The proposed method is applied to the feature extraction of ship-radiated noise. The result shows its effectiveness and universality.

为提高复杂海洋环境下船舶辐射噪声的特征提取能力，提出了一种基于自适应选择性噪声的完全集成经验模态分解（CEEMDASN）和改进的复合多尺度涨落色散熵的船舶辐射噪声特征提取方法。 RCMFDE) 被提议。CEEMDASN是本文提出的，它在分解信号时考虑了高频间歇分量。此外，本文还提出了RCMFDE，它基于复合多尺度理论对原始信号的预处理过程进行了细化。首先，原始信号被 CEEMDASN 分解为几个固有模式函数（IMF）。计算所有IMF分量的能量分配比（EDR）和平均能量分配比（AEDR）。然后，选择 EDR 和 AEDR (MEDR) 之差最小的 IMF 作为特征 IMF。特征 IMF 的 RCMFDE 被估计为船舶辐射噪声的特征向量。最后，将这些特征向量发送到自组织图（SOM）进行分类和识别。该方法应用于船舶辐射噪声的特征提取。结果显示了它的有效性和普遍性。