MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Yan, Huichao; Xu, Ting; Wang, Peng; Zhang, Linmei; Hu, Hongping; Bai, Yanping

doi:10.3390/s19214622

Cited by 34 publications

(14 citation statements)

References 39 publications

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“…Correlation Coefficient. The correlation coefficient (CC) [30] is an important parameter in statistics, which can measure the correlation between the denoised signal and the original signal. CC can distinguish whether the signal components obtained by VMD contain the main characteristics of the original signal for performing the signal denoising.…”

Section: Multiverse Optimizer Multiverse Optimizer (Mvo)mentioning

confidence: 99%

“…In particular, the proposed intelligence algorithms are utilized to obtain the optimal parameters k and α of VMD. For instance, in [29], genetic algorithm is employed to optimize k and α by taking the envelope entropy as the fitness function, in [30], whale optimization algorithm is used to optimize k and α by taking the power spectral entropy (PSE) as the fitness function, and in [31], spectral aggregation factor method is proposed to adjust penalty factor adaptively.…”

Section: Introductionmentioning

confidence: 99%

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Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

Yan

et al. 2021

Journal of Sensors

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To eliminate the noise from the signals received by MEMS vector hydrophone, a joint algorithm is proposed in this paper based on wavelet threshold (WT) denoising, variational mode decomposition (VMD) optimized by a hybrid algorithm of Multiverse Optimizer (MVO) and Particle Swarm Optimization (PSO), and correlation coefficient (CC) judgment to perform the signal denoising of MEMS vector hydrophone, named as MVO-PSO-VMD-CC-WT, whose fitness function is the root mean square error (RMSE) and whose individual is the parameters of VMD. For every individual, the original signal is decomposed by VMD into pure components, noisy components, and noise components in terms of CC judgment, where the pure components are directly retained, the noisy components are denoised by WT denoising, and the noise components are discarded, and then, the denoised noisy components and the pure components are reconstructed to be the denoised signal of the original signal. Then, the obtained optimal individual is utilized to perform the signal denoising by MVO-PSO-VMD-CC-WT by the use of the above repeated signal processing. Two simulated experimental results show that the MVO-PSO-VMD-CC-WT algorithm which has the highest signal-to-noise ratio and the least RMSE is superior to the other compared algorithms. And the proposed MVO-PSO-VMD-CC-WT algorithm is effectively applied to perform the signal denoising of the actual lake experiments. Therefore, the proposed MVO-PSO-VMD-CC-WT is suitable for the signal denoising and can be applied into the actual experiments in signal processing.

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Section: Multiverse Optimizer Multiverse Optimizer (Mvo)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

Yan

et al. 2021

Journal of Sensors

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“…For example, the sound made by a ship engine when it is running is regarded as a kind of noise for tourists or underwater creatures. When we use a sound to judge the type, size and direction of the ship, the sound emitted by its engine is the useful signal we need, and other sounds become useless noise [ 37 ]. In reality, we only need part of the information in the signal for DOA estimation, and the signal is usually composed of various frequency components and distributions.…”

Section: Theoretical Basis and Analysismentioning

confidence: 99%

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Yan

Chen

Wang

et al. 2021

Sensors

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Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters; Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate; Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

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“…Owing to the advantages of small end effect, high operation efficiency and good noise robustness, VMD has gained much attention by researchers since it was proposed [ 19 , 20 , 21 , 22 ]. However, the key step in the decomposition algorithm is to find the appropriate parameters K and α, where K is the number of intrinsic mode functions and α is the penalty factor, which affect the decomposition precision of IMFs [ 23 ]. Huang et al [ 24 , 25 ] proposed an improved scale space guided VMD algorithm which included dividing resonance frequency bands of signal frequency band in scale space to determine the number of intrinsic modes in VMD, estimate the initial center frequency and corresponding penalty factor of each intrinsic function of VMD according to the boundary of resonance frequency band, and improve the adaptability and accuracy of VMD.…”

Section: Introductionmentioning

confidence: 99%

Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings

Wang

et al. 2021

Sensors

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In view of the fact that vibration signals of rolling bearings are much contaminated by noise in the early failure period, this paper presents a new denoising SVD-VMD method by combining singular value decomposition (SVD) and variational mode decomposition (VMD). SVD is used to determine the structure of the underlying model, which is referred to as signal and noise subspaces, and VMD is used to decompose the original signal into several band-limited modes. Then the effective components are selected from these modes to reconstruct the denoised signal according to the difference spectrum (DS) of singular values and kurtosis values. Simulated signals and experimental signals of roller bearing faults have been analyzed using this proposed method and compared with SVD-DS. The results demonstrate that the proposed method can effectively retain the useful signals and denoise the bearing signals in extremely noisy backgrounds.

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MEMS Hydrophone Signal Denoising and Baseline Drift Removal Algorithm Based on Parameter-Optimized Variational Mode Decomposition and Correlation Coefficient

Cited by 34 publications

References 39 publications

Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

Signal Denoising Based on Wavelet Threshold Denoising and Optimized Variational Mode Decomposition

A Direction-of-Arrival Estimation Algorithm Based on Compressed Sensing and Density-Based Spatial Clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

Utilizing SVD and VMD for Denoising Non-Stationary Signals of Roller Bearings

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