Explosion Shock Wave Denoising Algorithm Based on Fusion of CEEMDAN-GMM and Wavelet Threshold Method

YANG Zhifei; CUI Chunsheng; DU Guiyun; LIU Shuangfeng; ZHAO Haixia

doi:10.15892/j.cnki.djzdxb.2023.05.013

Journal of Projectiles, Rockets, Missiles and Guidance >

2023 , Vol. 43 >Issue 5: 80 - 86

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.05.013

Explosion Shock Wave Denoising Algorithm Based on Fusion of CEEMDAN-GMM and Wavelet Threshold Method

YANG Zhifei ¹^,² ,
CUI Chunsheng ¹^,² ,
DU Guiyun ¹^,² ,
LIU Shuangfeng ¹^,² ,
ZHAO Haixia ³

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¹ State key Laboratory of Dynamic Measurement Technology, North University of China, Taiyuan 030051, Shanxi, China
² School of Electrical and Control Engineering, North University of China, Taiyuan 030051, Shanxi, China
³ School of Environment and Safety Engineering, North University of China, Taiyuan 030051, shanxi, China

Received date: 2023-06-14

Online published: 2024-12-30

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Abstract

In response to the large number of noise signals in the measured explosion shock wave signal and the problem of signal distortion that may occur during the noise reduction process using the CEEMDAN method. A noise reduction algorithm for explosive shock waves based on adaptive white noise complete set empirical mode decomposition (CEEMDAN) and Gaussian mixture model (GMM) is proposed. This algorithm first decomposes the original explosion shock wave data into several intrinsic mode components (IMF) through CEEMDAN, and uses the GMM clustering algorithm to classify the IMF components into multiple categories. Verify the clustering results by calculating the variance contribution rates of different IMF components, and eliminate IMF components in high-frequency noise categories. For IMF components affected by noise interference, wavelet thresholding method is used to filter and remove noise. Finally, the filtered IMF component is reconstructed together with the IMF component that is not affected by noise to obtain the denoised explosion shock wave signal. The experimental results show that the SNR and RMSE values of CEEMDAN-GMM and wavelet thresholding algorithms are 10.19 dB and 2.99 dB higher than CEEMDAN and CEEMDAN-Wavelet thresholding algorithms, respectively, with a decrease of 0.42×10^-4 and 0.14×10^-4.

Key words： explosion shock wave signal; CEEMDAN; noise reduction; GMM

Cite this article

YANG Zhifei , CUI Chunsheng , DU Guiyun , LIU Shuangfeng , ZHAO Haixia . Explosion Shock Wave Denoising Algorithm Based on Fusion of CEEMDAN-GMM and Wavelet Threshold Method[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2023 , 43(5) : 80 -86 . DOI: 10.15892/j.cnki.djzdxb.2023.05.013

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