Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

Li, Guohui; Guan, Qian-ru; Yang, Huirong

doi:10.3390/e21010011

Cited by 29 publications

(15 citation statements)

References 37 publications

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“…(ii) it is determined that the high-frequency components contain useful information and its noise reduction is performed. Then the high-frequency components after noise reduction and the low-frequency components without noise reduction are reconstructed [43], [44], [45]. Our research found that both high-frequency components and low-frequency components have useful information and noise information, so there are problems in removing high-frequency components directly and reducing noise only in high-frequency components.…”

Section: Introductionmentioning

confidence: 85%

A New Denoising Method for Underwater Acoustic Signal

Yang

2020

IEEE Access

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In recent years, the rapid development of marine science has put forward higher and higher requirements for the processing of ship-radiated noise signal. Ship-radiated noise is the noise signal generated by the vibration of various mechanical equipment or the movement of the hull and radiated into the sea when the ship is traveling. Ship-radiated noise signal contains a large number of time-varying, nonlinear and non-stationary components. The denoising processing of ship-radiated noise is the most critical part of underwater acoustic signal processing. In order to more effective reduce the noise of the ship-radiated noise signal, a new denoising method for underwater acoustic signal based on mutual information variational mode decomposition (MIVMD), multivariate multiscale dispersion entropy (mvMDE), and lift wavelet threshold (LWTD) and Savitzky Golay filter (S-G filter), named MIVMD-mvMDE-LWTD-SG, is proposed. Firstly, MIVMD is used to decompose the original signal into n subsignals. Secondly, the mvMDE value of each sub-signal is calculated, and the n sub-signals are divided into high-frequency components and low-frequency components according to the threshold. Then, S-G filter and LWTD method are used to reduce the noise of low-frequency components and high-frequency components respectively. Finally, the low-frequency components and high-frequency components after the denoising processing are reconstructed to obtain the denoising signal. In order to verify the effectiveness of the proposed method, the proposed method is used to reduce the noise of chaotic signal under different signalto-noise ratios (SNR), and compared with the EMD-mvMDE-LWTD and MIVMD-mvMDE-LWTD method. The results show that the proposed method can effective remove the noise in the chaotic signal, better distinguish the adjacent trajectories in the phase space, approximate the real chaotic attractor trajectory, and better retain the useful information in the chaotic signal. The proposed method is further applied to the actual ship-radiated noise signal, and the experimental analysis shows its effectiveness, which lays a solid foundation for further prediction and detection. INDEX TERMS Mutual information variational mode decomposition; multivariate multiscale dispersion entropy; lift wavelet threshold; chaotic signal; denoising

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Section: Introductionmentioning

confidence: 85%

A New Denoising Method for Underwater Acoustic Signal

Yang

2020

IEEE Access

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“…When calculating DE, m and d need to be set in advance. It is suggested in [43] that m usually takes 2 or 3, c is an integer between [4,8], and the time delay d is generally 1. In this paper, m, d, and c are set to 3, 1, and 6, respectively.…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

“…It was widely used in military and civil applications [2]. Underwater acoustic signal in the marine environment has been widely concerned by some scholars at home and abroad [3,4]. Taroudakis et al [5,6] analyzed the statistical characteristics of underwater acoustic signal and studied the application of the statistical characteristics of signal in geoacoustic inversions and applications in ocean acoustic tomography, Li et al [7] established a model to extract the characteristics of underwater acoustic signal.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Model for Underwater Acoustic Signal Prediction

Chang

Yang

2020

Complexity

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The prediction of underwater acoustic signal is the basis of underwater acoustic signal processing, which can be applied to underwater target signal noise reduction, detection, and feature extraction. Therefore, it is of great significance to improve the prediction accuracy of underwater acoustic signal. Aiming at the difficulty in underwater acoustic signal sequence prediction, a new hybrid prediction model for underwater acoustic signal is proposed in this paper, which combines the advantages of variational mode decomposition (VMD), artificial intelligence method, and optimization algorithm. In order to reduce the complexity of underwater acoustic signal sequence and improve operation efficiency, the original signal is decomposed by VMD into intrinsic mode components (IMFs) according to the characteristics of the signal, and dispersion entropy (DE) is used to analyze the complexity of IMF. The subsequences (VMD-DE) are obtained by adding the IMF with similar complexity. Then, extreme learning machine (ELM) is used to predict the low-frequency subsequence obtained by VMD-DE. Support vector regression (SVR) is used to predict the high-frequency subsequence. In addition, an artificial bee colony (ABC) algorithm is used to optimize model performance by adjusting the parameters of SVR. The experimental results show that the proposed new hybrid model can provide enhanced accuracy with the reduction of prediction error compared with other existing prediction methods.

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“…Zhang et al [19] proposed a signal denoising method named EMD-AdaptiveP, it combined similar strategy of wavelet filtering and EMD and had better noise reduction performance than traditional EMD method. Li et al [20], [21] proposed complete ensemble empirical mode decomposition with adaptive noise on the basis of EMD and uniform phase empirical mode decomposition to implement noise reduction processing on underwater acoustic signals and achieved good results. Although studies have been done on noise reduction using many EMD-based methods, it has problems such as modal mixing and poor processing effect on low signal-tonoise ratio signals [22], [23].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Noise Reduction Method of Synchronous Hydraulic Motor Acoustic Signal Based on Improved Dislocation Superposition Method

Ning

Sun

et al. 2020

IEEE Access

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Acoustic signal contains information on the state of mechanical motion, but the disadvantage of low signal-to-noise ratio (SNR) is that acoustic signal without noise reduction is difficult to apply to fault diagnosis directly. This study proposes an adaptive noise reduction method based on the dislocation superposition method (DSM), which can realize automatic noise reduction for acoustic signals of low SNR synchronous hydraulic motors. First, the theoretical rotation period of the synchronous hydraulic motor is obtained according to the flow meter. Then, the actual signal is subjected to DSM processing according to the measurement accuracy of the flow meter and the theoretical rotation period to adjust the exact superposition length and the initial position of the actual signal. Finally, when the stop condition is not satisfied, the number of superposition is increased and the above process is repeated. The superimposed signal satisfying the stop condition is taken as the noise reduction signal. According to experimental results, the proposed method has good noise reduction effect on the acoustic signals of the synchronous hydraulic motor health state, wear out state of gear and end cover, and rust state of the gear. The noise reduction signal has been verified to have higher accuracy than the actual signal. Therefore, the proposed automatic noise reduction method can be applied to the noise reduction processing of other kinds of rotating mechanical acoustic signals.

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Noise Reduction Method of Underwater Acoustic Signals Based on CEEMDAN, Effort-To-Compress Complexity, Refined Composite Multiscale Dispersion Entropy and Wavelet Threshold Denoising

Cited by 29 publications

References 37 publications

A New Denoising Method for Underwater Acoustic Signal

A New Denoising Method for Underwater Acoustic Signal

A New Hybrid Model for Underwater Acoustic Signal Prediction

Adaptive Noise Reduction Method of Synchronous Hydraulic Motor Acoustic Signal Based on Improved Dislocation Superposition Method

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