Enhancement method of weak Lidar signal based on adaptive variational modal decomposition and wavelet threshold denoising

Gu, Lin; Fei, Zhongwen; Xu, Xiaobin

doi:10.1016/j.infrared.2021.103991

Cited by 19 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, the wavelet transform is employed to reduce the noise in the angle signal. The basic idea of wavelet noise reduction is to decompose the original signal by wavelet transform to obtain wavelet coefficients containing noise signal's real signal and wavelet coefficients [39]. Subsequently, it undergoes thresholding using a specific threshold function.…”

Section: Wavelet Noise Reductionmentioning

confidence: 99%

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

Ma,

Hu,

Wang

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Despite the availability of pipeline bending strain detection technologies based on inertial measurement unit (IMU), there is a lack of intelligent and efficient methods for accurately identifying pipeline features by bending strain. Therefore, this paper proposes a novel method for identifying features in natural gas pipelines based on shapelet and blending fusion model. Specifically, the shape features of the bending strain data are extracted and transformed by shapelet. Then a blending fusion model with SVM, Decision Tree and Gradient Boosting as base learners and Random Forest as meta-learner is constructed. Finally, the extracted features are fed into the blending fusion model for pipeline feature recognition. The model is trained with bending strain data obtained from a real natural gas pipeline, the results indicate that the recognition accuracy of the proposed method is 97.17%. Compared with other models, the superiority of the proposed model is verified, and it is proved that the proposed method has better accuracy than the existing models (over 1.3%). Overall, the method proposed in this paper can be effectively combined with the in-line inspection system to provide a reference for pipeline companies to carry out pipeline integrity management.

show abstract

Section: Wavelet Noise Reductionmentioning

confidence: 99%

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

Ma,

Hu,

Wang

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…To effectively remove noise from underwater acoustic signals, this paper incorporates a correlation function into the CEEMDAN algorithm to determine the optimal decomposition layer, denoted as N. The signal is then decomposed into multiple IMFs ranging from high to low frequencies. [8]- [12] A wavelet threshold is set to filter out noise in the high-frequency IMFs that contain more noise. The result is a reconstructed underwater acoustic signal.…”

Section: Introductionmentioning

confidence: 99%

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Jiang,

Zhang,

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Due to the complex noise in the ocean environment, the signal-to-noise ratio of the hydrophone receiving signal is often low, making subsequent signal processing difficult. To solve this problem, this paper proposes using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) decomposition algorithm combined with an improved wavelet threshold algorithm to process the signal, and obtain the reconstructed signal after denoising. In this method, the noise-containing signal is transformed by the function and decomposed into multiple natural mode components with frequencies ranging from high to low using the CEEMDAN algorithm. The correlation component and the non-correlation component are then determined using the cross-correlation function. The non-correlated compinents are denoised using the improved wavelet threshold method and the denoised signal is obtained by reconstructing the signal. Experimental results show that this method can improve the performance of underwater acoustic signal denoising.

show abstract

“…Hu et al used variational modal decomposition to decompose noisy signals, selected the decomposed noise components through correlation coefficients, and then denoised the noise components using wavelet soft thresholding to obtain the denoised signal 12 . Gu et al improved the wavelet threshold based on variational mode decomposition and calculated the optimal denoising threshold by minimizing Stein unbiased risk estimation, achieving good denoising results 13 . Liu et al used wavelet packets to decompose chaotic signals and then used fuzzy analysis to construct wavelet packets the denoising thresholds to achieve the denoising of chaotic signals 14 .…”

Section: Introductionmentioning

confidence: 99%

Chaotic signal denoising based on energy selection TQWT and adaptive SVD

Yang,

Wang

2023

Sci Rep

View full text Add to dashboard Cite

Aiming at the problem of denoising chaotic signals with low signal-to-noise ratio and unknown dynamic system parameters, a new chaotic signal denoising algorithm is proposed, which combines adjustable Q-factor wavelet transform (TQWT) and adaptive singular value decomposition (ASVD). This method uses TQWT to decompose the noisy chaotic signal. According to the maximum wavelet entropy theory and energy threshold rule, the subband of TQWT is accurately divided into signal subband and noise subband. For noise subbands, adaptive SVD is used to denoise them, to achieve preliminary denoising. In ASVD, the standard deviation of the singular value subset is used to determine the effective reconstruction order to improve the noise suppression effect. To further remove noise in the signal subband, TQWT reconstruction is performed on the preliminarily denoised signal, and ASVD is used to denoise the reconstructed signal again to obtain the chaotic signal after secondary denoising. Chua's simulated signal and four kinds of underwater radiated noise measured by TQWT-ASVD were denoised, and compared with the SVD denoising method, TQWT denoising method, complete ensemble empirical mode decomposition with adaptive noise and threshold denoising method (CEEMDAN-WT) and modified ensemble empirical mode decomposition combined with least squares denoising method (MEEMD-LMS), The experimental results show that the TQWT-ASVD method can reduce the noise of chaotic signals more effectively. Compared with SVD, TQWT, CEEMDAN-WT, MEEMD-LMS, and Chua's signal denoising method, the signal-to-noise ratio (SNR) of this method increased by 23.22%, 26.46%, 18.79%, 16.11% the root mean square error (RMSE) decreased by 32.53%,39.48%, 30.96%, 27.94%, and the row entropy (PE) decreased by 40.44%, 41.96%, 22.78%, 20.59%; After reducing the radiation noise of cargo ships, the PE value of this method is reduced by 13.91%, 10.18%, 10.88%, 8.68% respectively, and the FE value is reduced by 33.66%, 31.42%, 26.98%, 21.32% respectively.

show abstract

Enhancement method of weak Lidar signal based on adaptive variational modal decomposition and wavelet threshold denoising

Cited by 19 publications

References 20 publications

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

An intelligent feature recognition method of natural gas pipelines based on shapelet and blending fusion model

Research on Noise Reduction Method of Underwater Acoustic Signal Based on CEEMDAN Decomposition-Improved Wavelet Threshold

Chaotic signal denoising based on energy selection TQWT and adaptive SVD

Contact Info

Product

Resources

About