Abstract
The data-driven method is an important tool in the field of underwater acoustic signal processing. In order to realize the feature extraction of ship-radiated noise (S-RN), we proposed a data-driven optimization method called improved variational mode decomposition (IVMD). IVMD, as an improved method of variational mode decomposition (VMD), solved the problem of choosing decomposition layers for VMD by using a frequency-aided method. Furthermore, a novel method of feature extraction for S-RN, which combines IVMD and sample entropy (SE), is put forward in this paper. In this study, four types of S-RN signals are decomposed into a group of intrinsic mode functions (IMFs) by IVMD. Then, SEs of all IMFs are calculated. SEs are different in the maximum energy IMFs (EIMFs), thus, SE of the EIMF is seen as a novel feature for S-RN. To verify the effectiveness of the proposed method, a comparison has been conducted by comparing features of center frequency and SE of the EIMF by IVMD, empirical mode decomposition (EMD) and ensemble EMD (EEMD). The analysis results show that the feature of S-RN can be obtain efficiently and accurately by using the proposed method.
Highlights
The data-driven method is an effective research method in both scientific research and practical applications [1,2,3]
improved variational mode decomposition (IVMD) as a novel algorithm combining sample entropy (SE) is first proposed for feature extraction of ship-radiated noise (S-RN) signals
The selection of parameters is the key problem in IVMD
Summary
The data-driven method is an effective research method in both scientific research and practical applications [1,2,3]. The existing feature extraction methods based on Fourier analysis are not suitable for underwater acoustic signals [7]. Was employed as a novel kind of data-driven signal analysis tool, which has better decomposition performance and robustness to noise than improved EMD methods [13]. In reference [14], several different data-driven methods are compared, including empirical wavelet transform, VMD, Vold–Kalman filter order tracking, EMD and its four kinds of improved methods. The VMD-based feature extraction methods for underwater acoustic signal have better performance [16]. IVMD, as a new data-driven method, is first put forward to solve the problem of choosing parameters for VMD by using frequency-aided method. For underwater acoustic signal processing, feature extraction using IVMD and SE is seldom discussed.
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