A Novel Improved Feature Extraction Technique for Ship-Radiated Noise Based on IITD and MDE

Abstract

Ship-radiated noise signal has a lot of nonlinear, non-Gaussian, and nonstationary information characteristics, which can reflect the important signs of ship performance. This paper proposes a novel feature extraction technique for ship-radiated noise based on improved intrinsic time-scale decomposition (IITD) and multiscale dispersion entropy (MDE). The proposed feature extraction technique is named IITD-MDE. First, IITD is applied to decompose the ship-radiated noise signal into a series of intrinsic scale components (ISCs). Then, we select the ISC with the main information through the correlation analysis, and calculate the MDE value as feature vectors. Finally, the feature vectors are input into the support vector machine (SVM) for ship classification. The experimental results indicate that the recognition rate of the proposed technique reaches 86% accuracy. Therefore, compared with the other feature extraction methods, the proposed method provides a new solution for classifying different types of ships effectively.Entropy 2019, 21, 1215 2 of 17 CEEMDAN and EE to extract the hybrid energy feature. Frei [17] proposed an adaptive time-frequency analysis method, which can decompose the nonstationary signal into a series of single component signals with the physical meanings of instantaneous frequencies. In recent decades, these studies have provided rich reference information, which is widely used in fault diagnosis [18,19], biomedicine [20,21], geophysics [22], and hydroacoustics [23,24]. Compared with the EMD method, the ITD method has obvious advantages in terms of computational efficiency and processing edge effects. However, the definition of the baseline of the ITD method is based on the linear transformation of the signal itself, and may cause a glitch and distortion of the proper rotation components obtained by the decomposition. Based on this, we used akima interpolation [25] to improve the ITD method; then, the IITD algorithm was proposed. Therefore, this is a feasible way to decompose the ship-radiated noise signal by IITD to extract effective ISCs.Entropy theory can efficiently evaluate the complexity of the time series and reduce the dimension of the feature vector and fully represent the characteristics of the series. Hence, there are many methods for complexity measurement, including Shannon entropy [26], sample entropy (SampEn) [27,28], permutation entropy (PE) [29], and fuzzy entropy [30], which have been successfully applied in the field of fault diagnosis and the medical field. However, SampEn is time consuming for large data calculations and is susceptible to mutated signals. While the PE is faster, it fails to consider the mean value of amplitudes and differences between the amplitudes value. In order to overcome the drawbacks of SampEn and PE, a new measure of the complexity method, named dispersion entropy (DE), was proposed by Mostafa Rostaghi and Hamed Azami in 2016 [31]. The advantage of the DE algorithm is that the calculation speed is fast, the influence of the noisy signal is small, ...

Multi-scale permutation Lempel-Ziv complexity and its application in feature extraction for Ship-radiated noise

“…Ship-radiated noise signal (S-S) as a kind of underwater acoustic signal (Li et al, 2019;Zhang et al, 2020;Esmaiel et al, 2022), which can indicate the physical characteristics of ships (Wang et al, 2017;Yang et al, 2022). The extraction of nonlinear dynamic indexes from S-Ss are helpful to the classification and recognition of different ships (Bao et al, 2010).…”

Feature extraction method of ship radiated noise based on BOA-VMD and slope entropy

“…In the field of underwater acoustic signal processing, entropy is used to describe the complexity of the time series and is often used as eigenvalues for feature extraction [17][18][19][20], among which permutation entropy (PE) [21], dispersion entropy (DE) [22], fluctuation dispersion entropy (FDE) [23] and others have been widely used in this field. Moreover, a large number of experimental studies have also proved that the entropy-based feature extraction method [24] is more effective than traditional methods [25][26][27].…”

A feature extraction method of ship-radiated noise based on mathematical morphological filtering

“…Many research studies have discussed the application of nonlinear approaches including the application of wavelet transform (Zhang et al, 1997), Hilbert-Huang transform (Huang et al, 1998), independent component analysis (Hyvarinen and Oja, 2000), etc. (Li, 2020;Li et al, 2019a;Li et al, 2019b;Wu and Huang, 2004) and have achieved certain results in recent years. However, the computation of nonlinear feature is relatively complicated.…”