scholarly journals Extraction method of weak underwater acoustic signal based on the combination of wavelet transform and empirical mode decomposition

2021 ◽  
Vol 12 ◽  
pp. 7
Author(s):  
Junbing Shi ◽  
Yingmin Wang ◽  
Xiaoyong Zhang ◽  
Libo Yang
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Threshold Method ◽  
Underwater Acoustic ◽  
Baseline Drift ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Combined Algorithm

When studying underwater acoustic exploration, tracking and positioning, the target signals collected by hydrophones are often submerged in strong intermittent noise and environmental noise. In this paper, an algorithm that combines empirical mode decomposition and wavelet transform is proposed to achieve the efficient extraction of target signals in the environment with strong noise. First the calibration of baseline drift is performed on the algorithm, and then it is decomposed into different intrinsic mode functions via empirical mode. The wavelet threshold processing is conducted according to the correlation coefficient of each mode component and the original signal, and finally the signals are reconstructed. The simulation and experiment results show that compared with the conventional empirical mode decomposition method and wavelet threshold method, when the signal-to-noise ratio is low and there exist high-frequency intermittent jamming and baseline drift, the combined algorithm can better extract the target signal, laying the foundation for direction-of-arrival estimation and target positioning in the next step.

scholarly journals Empirical Mode Decomposition in Discrete Time Signals Denoising

2019 ◽  
Vol 16 (1) ◽  
pp. 10-13 ◽  
Author(s):  
Zoltán Germán-Salló
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Discrete Wavelet ◽  
Intrinsic Mode Functions ◽  
Nonlinear Signal Processing ◽  
Mode Decomposition ◽  
Time Signals ◽  
Nonlinear Signal ◽  
Filtering Procedure

Abstract This study explores the data-driven properties of the empirical mode decomposition (EMD) for signal denoising. EMD is an acknowledged procedure which has been widely used for non-stationary and nonlinear signal processing. The main idea of the EMD method is to decompose the analyzed signal into components without using expansion functions. This is a signal dependent representation and provides intrinsic mode functions (IMFs) as components. These are analyzed, through their Hurst exponent and if they are found being noisy components they will be partially or integrally eliminated. This study presents an EMD decomposition-based filtering procedure applied to test signals, the results are evaluated through signal to noise ratio (SNR) and mean square error (MSE). The obtained results are compared with discrete wavelet transform based filtering results.

Application of the Empirical Mode Decomposition Method to the Identification of Disc Brake Squeal

2002 ◽  
Author(s):  
Fulun Yang ◽  
Chin An Tan ◽  
Frank Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Disc Brake ◽  
Intrinsic Mode Functions ◽  
Brake Squeal ◽  
Mode Decomposition ◽  
Out Of Plane ◽  
Empirical Mode Decomposition Method ◽  
Original Application ◽  
The Hilbert Transform

This paper investigates the identification of mechanisms of disc brake squeal by the application of a recently developed Empirical Mode Decomposition method (EMD). A known strength of the EMD is its adaptive nature in analyzing nonstationary data, with success in its original application to ocean mechanics. The EMD decomposes an original signal into a number of intrinsic mode functions (IMFs), with each IMF often containing distinct physical significance. Several sets of disc brake squeal data were obtained and processed by EMD. A typical set data is presented in this paper for discussion. Employing a sifting process in the EMD, four prominent squeal-related IMFs are identified in this set of data. The Hilbert transform is then used to analyze the frequency and amplitude contents of the four IMFs, and it is shown that the first IMF is dominant. The spectrogram method is applied to analyze the time-evolution of the frequency components of the IMFs in the squeal process. This analysis procedure confirms an important squeal mechanism, i.e., the squeal condition is governed by the coupling of in-plane and out-of-plane vibration modes of the rotor and the coalescence of their natural frequencies. The inverse approach outlined in this paper is shown to be useful for providing new insights and confirming established hypotheses of disc brake squeal.

Pipeline Leak Detection Based on Acoustic Emission Using Empirical Mode Decomposition Method

2008 ◽  
Author(s):  
Yibo Li ◽  
Junlin Li ◽  
Liying Sun ◽  
Shijiu Jin ◽  
Shenghua Han
Keyword(s):  
Acoustic Emission ◽  
Empirical Mode Decomposition ◽  
Signal Analysis ◽  
Leak Detection ◽  
Detection Sensitivity ◽  
Intrinsic Mode Functions ◽  
Location Accuracy ◽  
Noise Interference ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

Corrosion in pipeline is a significant problem in the oil industry and there is also much interest in reducing leak due to corrosion. Correlation techniques are widely used in leak detection, and these have been extremely effective when attempting to locate leaks in metal pipes. Acoustic emission is a new non-destructive pipeline inspection technology which can be used to monitor crucial part of pipelines and detect pipe corrosion or leak in real time. However, AE signals causing by corrosion and leak are liable to noise interference on field. Aiming at solving the noise interference problems and increase the detection sensitivity and location accuracy of the leak, advanced signal analysis method based on Empirical Mode Decomposition were researched. Empirical Mode Decomposition is a great breakthrough in non-stable signal analysis and it decomposes the signals into a sum of finite intrinsic mode functions (IMF), which have real physical meaning. In the experiment, the leak signals from a 30 m pipeline were decomposed into 9 intrinsic mode functions by EMD, among which some IMF components containing typical AE characteristic can be selected to reconstruct the signal, and thus intrinsic characteristic of leak signal could be extracted and noise interference would be eliminated. Location accuracy of the leaking hole calculated with the reconstructed signals based on EMD algorithm was increased 64%.

scholarly journals A VARIANT OF THE EMD METHOD FOR MULTI-SCALE DATA

2009 ◽  
Vol 01 (04) ◽  
pp. 483-516 ◽  
Author(s):  
THOMAS Y. HOU ◽  
MIKE P. YAN ◽  
ZHAOHUA WU
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Separation Property ◽  
Intrinsic Mode Functions ◽  
Scale Separation ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Emd Method ◽  
Scale Data

In this paper, we propose a variant of the Empirical Mode Decomposition method to decompose multiscale data into their intrinsic mode functions. Under the assumption that the multiscale data satisfy certain scale separation property, we show that the proposed method can extract the intrinsic mode functions accurately and uniquely.

The application of threshold empirical mode decomposition de-noising algorithm for battlefield ambient noise

2018 ◽  
Vol 09 (04) ◽  
pp. 1850027
Author(s):  
Shaocheng Zhu ◽  
Limin Liu ◽  
Zhigang Yao
Keyword(s):  
Empirical Mode Decomposition ◽  
Acoustic Signal ◽  
Ambient Noise ◽  
Signal To Noise Ratio ◽  
Intrinsic Mode Functions ◽  
Noisy Signal ◽  
Mode Decomposition ◽  
Smoothness Index ◽  
Acoustic Target ◽  
Low Altitude

The detection of the low-altitude acoustic target is an important way to compensate for the weakness of radar. Removing the noise mixed in acoustic signal as much as possible to retain the useful information is a challenging task. Inspired by the wavelet threshold, the de-noising method for low-altitude battlefield acoustic signal based on threshold empirical mode decomposition (EMD-T) is proposed in this paper. Firstly, the noisy signal is decomposed by empirical mode decomposition (EMD) to get the intrinsic mode functions (IMFs). Then the IMFs, whose actual energy exceeds its estimated energy, are processed by the EMD threshold. Finally, the processed IMFs are summed to reconstruct the de-noised signal. To evaluate the performance of the proposed method, extensive simulations are performed using helicopter sound corrupted with four types of typical low-altitude ambient noise under different signal-to-noise ratio (SNR) input values. The performance is evaluated in terms of SNR, root mean square error (RMSE) and smoothness index (SI). The simulations results reveal that the proposed method de-noising method has the perspective of the highest SNR, smallest RMSE and SI in de-noising low-altitude ambient noise compared to other methods, including the wavelet transform (WT) and conventional EMD.

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

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 11 ◽  
Author(s):  
Guohui Li ◽  
Qianru Guan ◽  
Hong Yang
Keyword(s):  
Noise Reduction ◽  
Empirical Mode Decomposition ◽  
Reduction Method ◽  
Signal To Noise Ratio ◽  
Acoustic Signals ◽  
Ensemble Empirical Mode Decomposition ◽  
Underwater Acoustic ◽  
Real Signal ◽  
Mode Decomposition ◽  
Soft Threshold

Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), effort-to-compress complexity (ETC), refined composite multiscale dispersion entropy (RCMDE) and wavelet threshold denoising is proposed. Firstly, the original signal is decomposed into several IMFs by CEEMDAN and noise IMFs can be identified according to the ETC of IMFs. Then, calculating the RCMDE of remaining IMFs, these IMFs are divided into three kinds of IMFs by RCMDE, namely noise-dominant IMFs, real signal-dominant IMFs, real IMFs. Finally, noise IMFs are removed, wavelet soft threshold denoising is applied to noise-dominant IMFs and real signal-dominant IMFs. The denoised signal can be obtained by combining the real IMFs with the denoised IMFs after wavelet soft threshold denoising. Chaotic signals with different signal-to-noise ratio (SNR) are used for denoising experiments by comparing with EMD_MSE_WSTD and EEMD_DE_WSTD, it shows that the proposed algorithm has higher SNR and smaller root mean square error (RMSE). In order to further verify the effectiveness of the proposed method, which is applied to noise reduction of real underwater acoustic signals. The results show that the denoised underwater acoustic signals not only eliminate noise interference also restore the topological structure of the chaotic attractors more clearly, which lays a foundation for the further processing of underwater acoustic signals.

scholarly journals A New Underwater Acoustic Signal Denoising Technique Based on CEEMDAN, Mutual Information, Permutation Entropy, and Wavelet Threshold Denoising

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 563 ◽  
Author(s):  
Yuxing Li ◽  
Yaan Li ◽  
Xiao Chen ◽  
Jing Yu ◽  
Hong Yang ◽  
...  
Keyword(s):  
Mutual Information ◽  
Empirical Mode Decomposition ◽  
Acoustic Signal ◽  
Simulated Data ◽  
Permutation Entropy ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Underwater Acoustic ◽  
Mode Decomposition ◽  
Underwater Acoustic Signal

Owing to the complexity of the ocean background noise, underwater acoustic signal denoising is one of the hotspot problems in the field of underwater acoustic signal processing. In this paper, we propose a new technique for underwater acoustic signal denoising based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), mutual information (MI), permutation entropy (PE), and wavelet threshold denoising. CEEMDAN is an improved algorithm of empirical mode decomposition (EMD) and ensemble EMD (EEMD). First, CEEMDAN is employed to decompose noisy signals into many intrinsic mode functions (IMFs). IMFs can be divided into three parts: noise IMFs, noise-dominant IMFs, and real IMFs. Then, the noise IMFs can be identified on the basis of MIs of adjacent IMFs; the other two parts of IMFs can be distinguished based on the values of PE. Finally, noise IMFs were removed, and wavelet threshold denoising is applied to noise-dominant IMFs; we can obtain the final denoised signal by combining real IMFs and denoised noise-dominant IMFs. Simulation experiments were conducted by using simulated data, chaotic signals, and real underwater acoustic signals; the proposed denoising technique performs better than other existing denoising techniques, which is beneficial to the feature extraction of underwater acoustic signal.

scholarly journals Empirical Mode Decomposition De-Noising Method Based on Improved Soft-Threshold

2011 ◽  
Vol 1 ◽  
pp. 421-425 ◽  
Author(s):  
Jian Hui Xi ◽  
Jia Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Adaptive Threshold ◽  
Threshold Function ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Soft Threshold ◽  
Simulation Results ◽  
Improved Function ◽  
Constant Deviation

In this paper, an improved soft-threshold function is constructed, combined the improved function and empirical mode decomposition (EMD) methods, a new de-noising method has been proposed. Set the adaptive threshold for the intrinsic mode functions (IMFs) of the EMD, and then de-noise the each IMF respectively. Finally, the de-noised signal is reconstructed by the de-noised IMF components. Through the simulation results of quantitative analysis by signal-to-noise ratio (SNR) and mean square error (MSE), the algorithm in this paper has better de-noising effect. Also, this method can effectively improve the constant deviation between the original signal and the de-noised signal by traditional soft-threshold.

scholarly journals Ultrasonic Flaw Echo Enhancement Based on Empirical Mode Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 236 ◽  
Author(s):  
Wei Feng ◽  
Xiaojun Zhou ◽  
Xiang Zeng ◽  
Chenlong Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Signal To Noise Ratio ◽  
Electronic Noise ◽  
Intrinsic Mode Functions ◽  
Simulated Signal ◽  
Mode Decomposition ◽  
Ultrasonic Flaw ◽  
Otsu’S Method ◽  
Different Depths ◽  
Backscattered Signals

The detection of flaw echoes in backscattered signals in ultrasonic nondestructive testing can be challenging due to the existence of backscattering noise and electronic noise. In this article, an empirical mode decomposition (EMD) methodology is proposed for flaw echo enhancement. The backscattered signal was first decomposed into several intrinsic mode functions (IMFs) using EMD or ensemble EMD (EEMD). The sample entropies (SampEn) of all IMFs were used to select the relevant modes. Otsu’s method was used for interval thresholding of the first relevant mode, and a window was used to separate the flaw echoes in the relevant modes. The flaw echo was reconstructed by adding the residue and the separated flaw echoes. The established methodology was successfully employed for simulated signal and experimental signal processing. For the simulated signals, an improvement of 9.42 dB in the signal-to-noise ratio (SNR) and an improvement of 0.0099 in the modified correlation coefficient (MCC) were achieved. For experimental signals obtained from two cracks at different depths, the flaw echoes were also significantly enhanced.

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