Refined composite multi-scale dispersion entropy (RCMDE), as a new and effective nonlinear dynamic method, has been applied in the field of medical diagnosis and fault diagnosis. In this paper, we first introduce RCMDE into the field of underwater acoustic signal processing for complexity feature extraction of ship radiated noise, and then propose a novel classification method for ship-radiated noise based on RCMDE and k-nearest neighbor (KNN), termed RCMDE-KNN. The results of a comparative experiment show that the proposed RCMDE-KNN classification method can effectively extract the complexity features of ship-radiated noise, and has better classification performance under one and two scales than the other three classification methods based on multi-scale permutation entropy (MPE) and KNN, multi-scale weighted-permutation entropy (MW-PE) and KNN, and multi-scale dispersion entropy (MDE) and KNN, termed MPE-KNN, MW-PE-KNN, and MDE-KNN. It is proved that the RCMDE-KNN classification method for ship-radiated noise is feasible and effective, and can obtain a very high recognition rate.

细化复合多尺度色散熵（RCMDE）作为一种新的有效非线性动力学方法，已在医学诊断和故障诊断领域得到应用。在本文中，我们首先将RCMDE引入到水声信号处理领域，用于船舶辐射噪声的复杂特征提取，然后提出了一种基于RCMDE和k最近邻（KNN）的船舶辐射噪声分类方法，称为RCMDE-KNN。对比实验结果表明，所提出的RCMDE-KNN分类方法能够有效提取舰船辐射噪声的复杂特征，在一、二尺度下的分类性能优于其他基于多尺度置换熵的其他三种分类方法。 (MPE) 和 KNN，多尺度加权置换熵 (MW-PE) 和 KNN，多尺度色散熵 (MDE) 和 KNN，称为 MPE-KNN、MW-PE-KNN 和 MDE-KNN。实践证明，RCMDE-KNN对船舶辐射噪声的分类方法是可行和有效的，可以获得非常高的识别率。