A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

Li, Yuxing; Chen, Xiao; Yu, Jing; Yang, Xiaohui

doi:10.3390/electronics8010061

Cited by 41 publications

(30 citation statements)

References 30 publications

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“…Feature extraction method MSAF-15-MULTIEXPANDED-8-GROUPS (Method of Selection of Amplitudes of Frequency Multiexpanded 8 Groups) was introduced [1]. Processing and feature extraction of an underwater acoustic signal was shown in the paper [2]. The authors proposed a feature extraction method for an underwater acoustic signal.…”

Section: The Present Special Issuementioning

confidence: 99%

Signal Processing and Analysis of Electrical Circuit

Głowacz

Daviu

2019

Electronics

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Section: The Present Special Issuementioning

confidence: 99%

Signal Processing and Analysis of Electrical Circuit

Głowacz

Daviu

2019

Electronics

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“…Due to the marine environmental noise and the time-varying characteristics of the underwater acoustic channel, the measured SR-N signal is a nonlinear and non-stationary chaotic signal [5,6]. Noise reduction of SR-N signal is beneficial to further feature extraction, classification and detection [7]. However, traditional linear and frequency-domain denoising methods cannot be directly applied to SR-N signal.…”

Section: Introductionmentioning

confidence: 99%

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

Yang

2019

Electronics

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Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modified complete ensemble empirical mode decomposition (EMD) with adaptive noise (CEEMDAN), dispersion entropy (DE), and interval thresholding is proposed. The proposed denoising method has the following advantages: (1) as an improved version of CEEMDAN, modified CEEMDAN (MCEEMDAN) combines the advantages of EMD and CEEMDAN, and it is more reliable than CEEMDAN and has less consuming time; (2) as a fast complexity measurement technology, DE can effectively identify the type of intrinsic mode function (IMF); and (3) interval thresholding is used for SR-N signal denoising, which avoids loss of amplitude information compared with traditional denoising methods. Firstly, the original signal is decomposed into a series of IMFs using MCEEMDAN. According to the DE value of IMF, the modes are divided into three types: noise IMF, noise-dominated IMF and pure IMF. After noise IMFs are removed, the noise-dominated IMFs are denoised using interval thresholding. Finally, the pure IMF and the processed noise-dominated IMFs are reconstructed to obtain the final denoised signal. The denoising experiments with the Chen's chaotic system show that the proposed method has a higher signal-to-noise ratio (SNR) than the other three methods. Applying the proposed method to denoise the real SR-N signal, the topological structure of chaotic attractor can be recovered clearly. It is proved that the proposed method can effectively suppress the high-frequency noise of SR-N signal.Empirical mode decomposition (EMD) [8] is an adaptive signal decomposition method, which can decompose the signal into a series of intrinsic mode functions (IMFs). Therefore, EMD provides a new idea for nonlinear and non-stationary signal processing, but EMD has problems such as the mode mixing and residual noise. In order to alleviate the above problems, some noise-assisted versions have been proposed, such as ensemble EMD (EEMD) [9], complementary EEMD (CEEMD) [10] and complete EEMD with adaptive noise (CEEMDAN) [11]. These optimized EMD algorithms are widely used in various fields, especially in medicine [12,13], fault diagnosis [14,15], chaotic signal processing [16], wind power prediction [17] and so on.CEEMDAN can overcome the mode mixing of EMD and reduce the reconstruction error of EEMD. However, each time an IMF is extracted, white Gaussian noise is added multiple times in the signal, which increases the algorithm complexity. In fact, in the decomposition process, the high-frequency intermittent components and noises that cause the mode mixing are usually decomposed first [18]. After separating these components, the extreme values of the remaining signal are evenly distributed [19]. Therefore, there is no need to use white Gaussian noise as an auxiliary for residual signal, the EMD decomposition can be directly perf...

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“…Due to the rapid development of ship-radiated noise signal processing technology, some researchers have proposed many nonlinear and nonstationary signal processing methods for the feature extraction of underwater acoustic target signals, such as empirical mode decomposition (EMD) [5,6], intrinsic time-scale decomposition (ITD) [7,8], local mean decomposition (LMD) [9], and their improved algorithms [10][11][12][13][14]. Hong [15] proposed ensemble EMD (EEMD) and energy distribution to extract the energy difference, which is an efficient feature extraction technique for ship-radiated noise.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2019

Entropy

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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, ...

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A Fusion Frequency Feature Extraction Method for Underwater Acoustic Signal Based on Variational Mode Decomposition, Duffing Chaotic Oscillator and a Kind of Permutation Entropy

Cited by 41 publications

References 30 publications

Signal Processing and Analysis of Electrical Circuit

Signal Processing and Analysis of Electrical Circuit

A Denoising Method of Ship Radiated Noise Signal Based on Modified CEEMDAN, Dispersion Entropy, and Interval Thresholding

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

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