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弹箭与制导学报 >

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

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

融合CEEMDAN-GMM和小波阈值方法的爆炸冲击波降噪算法

  • 杨志飞 1, 2 ,
  • 崔春生 1, 2 ,
  • 杜桂云 1, 2 ,
  • 刘双峰 1, 2 ,
  • 赵海霞 3
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  • 1 中北大学省部共建动态测试技术国家重点实验室,山西 太原 030051
  • 2 中北大学电气与控制工程学院,山西 太原 030051
  • 3 中北大学环境与安全工程学院,山西 太原 030051

杨志飞(1997—),男,硕士研究生,研究方向:动态测试与智能仪器。

收稿日期: 2023-06-14

  网络出版日期: 2024-12-30

基金资助

中北大学重点实验室开放研究基金(DXMBJJ2021-01)

中国应用物理化学科学与技术实验室项目(614260209)

收起

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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摘要

针对实测爆炸冲击波信号中存在大量噪声信号以及自适应白噪声完整集合经验模态分解(CEEMDAN)方法在降噪过程中容易出现信号失真的问题,提出了一种基于CEEMDAN方法和高斯混合模型(GMM)的爆炸冲击波降噪算法。该算法首先将原始爆炸冲击波数据通过CEEMDAN方法分解得到若干个本征模态分量IMF,并利用GMM聚类算法将IMF分量分为多个类别。通过计算不同IMF分量的方差贡献率校核聚类结果,剔除高频噪声类别的IMF分量。对受噪声干扰的IMF分量,采用小波阈值方法进行滤波去除噪声。最后,将滤波后的IMF分量与未受噪声干扰的IMF分量一起重构,并获得降噪后的爆炸冲击波信号。实验结果表明:CEEMDAN-GMM与小波阈值算法信噪比值RSNR和均方根误差值MRMSE分别比CEEMDAN方法和CEEMDAN-小波阈值算法提高了10.19 dB和2.99 dB,降低了0.42×10-4和0.14×10-4。

关键词： 爆炸冲击波信号; CEEMDAN; 降噪; GMM

本文引用格式

杨志飞 , 崔春生 , 杜桂云 , 刘双峰 , 赵海霞 . 融合CEEMDAN-GMM和小波阈值方法的爆炸冲击波降噪算法[J]. 弹箭与制导学报, 2023 , 43(5) : 80 -86 . DOI: 10.15892/j.cnki.djzdxb.2023.05.013

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

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参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序
[1]
祖静, 马铁华, 裴东兴, 等. 新概念动态测试[M]. 北京: 国防工业出版社, 2016.

ZU J, MA T H, PEI D X. Dynamic testing of new concepts[M]. Beijing: National Defence Industry Press, 2016.

[2]
尤文斌, 丁永红, 张超颖, 等. 高精度冲击波测试压电适配器的研究[J]. 火炮发射与控制学报, 2021, 42(1): 15-18.

YOU W B, DING Y H, ZHANG C Y, et al. Research on high-precision shock wave testing piezoelectric adapter[J]. Journal of Gun Launch & Control, 2021, 42(1): 15-18.

[3]
YOU W B, DING Y H. W-MOPSO in adaptive circuits for blast wave measurements[J]. IEEE Sensors Journal, 2021, 21(7): 9323-9332.

[4]
WANG L Q, KONG D R. Research on the distribution characteristics of explosive shock waves at different altitudes[J]. Defence Technology, 2023, 24: 340-348.

[5]
GAO S, TIAN G, DAI X, et al. A lightweight wireless overpressure node based efficient monitoring for shock waves[J]. IEEE/ASME Transactions on Mechatronics, 2020, 26(1): 448-457.

[6]
张衍芳, 杜红棉, 祖静. 压力测试系统修正方法的研究[J]. 电子测试, 2011(2): 47-51.

ZHANG Y F, DU H M, ZU J. Research on correction methods for pressure testing systems[J]. Electronic Test, 2011(2): 47-51.

[7]
刘嘉慧. 战斗部爆炸冲击波超压测试与数据处理技术研究[D]. 太原: 中北大学, 2019.

LIU J H. Research on overpressure testing and data processing technology for warhead explosion shock waves[D]. Taiyuan: North University of China, 2019.

[8]
ZHAI M Y. Seismic data denoising based on the fractional Fourier transformation[J]. Journal of Applied Geophysics, 2014, 109: 62-70.

[9]
张冉, 张鹏, 赵锋. 基于CEEMDAN-SG的爆炸冲击波去噪算法研究[J]. 国外电子测量技术, 2022, 41(10): 119-125.

ZHANG R, ZHANG P, ZHAO F. Research on explosion shock wave denoising algorithm based on CEEMDAN-SG[J]. Foreign Electronic Measurement Technology, 2022, 41(10): 119-125.

[10]
张健, 尤文斌, 丁永红, 等. 基于CEEMDAN-PCA的冲击波信号降噪研究[J]. 弹箭与制导学报, 2022, 42(4): 24-28.

ZHANG J, YOU W B, DING Y H, et al. Research on denoising of shock wave signals based on CEEMDAN-PCA[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2022, 42(4): 24-28.

[11]
WATANABE H, MURAMATSU S, KIKUCHI H. Interval calculation of EM algorithm for GMM parameter estimateo-n[C]// IEEE. Proceedings of 2010 IEEE International Symposium on Circuits and Systems. New York: IEEE, 2010: 2686-2689.

[12]
洪晓丹. 基于复杂噪声的高斯过程回归建模方法研究[D]. 上海: 东华大学, 2018.

HONG X D. Research on gaussian process regression modeling method based on complex noise[D]. Shanghai: Donghua University, 2018.

[13]
王海龙, 赵岩, 王海军, 等. 基于CEEMDAN-小波包分析的隧道爆破信号去噪方法[J]. 爆炸与冲击, 2021, 41(5): 125-137.

WANG H L, ZHAO Y, WANG H J, et al. A denoising method for tunnel blasting signals based on CEEMDAN wavelet packet analysis[J], Explosion and Shock Waves, 2021, 41(5): 125-137.

[14]
黄智刚, 吕虎波, 林一庚, 等. 基于CEEMDAN-MPE算法的隧道爆破地震波信号降噪方法及应用[J]. 爆破, 2020, 37(4): 138-144.

HUANG Z G, LYV H B, LIN Y G, et al. Noise reduction method and application of tunnel blasting Seismic wave signal based on CEEMDAN-MPE algorithm[J]. Blasting, 2020, 37(4): 138-144.

[15]
易文华, 刘连生, 闫雷, 等. 基于EMD改进算法的爆破振动信号去噪[J]. 爆炸与冲击, 2020, 40(9): 77-87.

YI W H, LIU L S, YAN L, et al. Denoising of blasting vibration signals based on improved EMD algorithm[J]. Explosion and Shock Waves, 2020, 40(9): 77-87.

[16]
梁晶, 熊振宇, 裴东兴, 等. 爆炸冲击波信号的降噪处理[J]. 火力与指挥控制, 2022, 47(8): 79-84.

LIANG J, XIONG Z Y, PEI D X, et al. Noise reduction processing of explosion shock wave signals[J]. Fire Control & Command Control, 2022, 47(8): 79-84.

[17]
尹世庄, 王韬, 谢方方, 等. 基于互信息和轮廓系数的聚类结果评估方法[J]. 兵器装备工程学报, 2020, 41(8): 207-213.

YIN S Z, WANG T, XIE F F, et al. Clustering result evaluation method based on mutual information and contour coefficients[J]. Journal of Ordnance Equipment Engineering, 2020, 41(8): 207-213.

[18]
马晓峰. 低信噪比微地震事件辨识与震相初至自动拾取方法[D]. 青岛: 山东科技大学, 2018.

MA X F. Identification of low signal-to-noise ratio microseismic events and automatic pickup of seismic phase first breaks[D]. Qingdao: Shandong University of Science and Technology, 2018.

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