A New Denoising Method for Strong Pulse Electromagnetic Interference Signals Based on Orthogonal Matching Pursuit

PDF(1796 KB)

Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (15) : 33-37.

ZHU Hui-jie 1 WANG Xin-qing 1 RUI Ting1 ZHAO Yang 1 LI Yan-feng 1

Author information +

History +

Abstract

Because strong pulse electromagnetic interference (PEMI) has high energy and no regularity, and it pollutes signals seriously, we proposed a new denoising method to solve this problem. According to the different structure between signal and PEMI, the unit pulse atom was constructed to match PEMI, and the sine atom, cosine atom and wavelet atom were chosen to match signal. Unlike common filtering ways of sparse decomposition, the PEMI was firstly taken as signal component, and all the atoms above were used to decompose the original signal. Second, only the component matched by sine atom, cosine atom and wavelet atom was used to reconstruct the unpolluted signal, and the component matched by unit pulse atom was eliminated as PEMI. Simulations and applications testified that this technology could filter strong PEMI, the filtered signal not only has high signal to noise ratio, but also retain details.

Key words

orthogonal matching pursuit / denoise / pulse electromagnetic interference / multi atoms

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

ZHU Hui-jie 1 WANG Xin-qing 1 RUI Ting1 ZHAO Yang 1 LI Yan-feng 1. A New Denoising Method for Strong Pulse Electromagnetic Interference Signals Based on Orthogonal Matching Pursuit[J]. Journal of Vibration and Shock, 2015, 34(15): 33-37

References

[1]. Liang J, Wang Y, Huang Y, etc. Electromagnetic interference shielding of graphene/epoxy composites[J]. Carbon, 2009, 47 (3): 922-925.
[2]. Al-Saleh M H, Sundararaj U. Electromagnetic interference shielding mechanisms of CNT/polymer composites[J]. Carbon, 2009, 47 (7): 1738-1746.
[3]. Tarateeraseth V, See K Y, Canavero F G, etc. Systematic electromagnetic interference filter design based on information from in-circuit impedance measurements[J]. Electromagnetic Compatibility, IEEE Transactions on, 2010, 52(3): 588-598.
[4]. Aggarwal R, Rathore S,Singh J K, etc. Noise Reduction of Speech Signal using Wavelet Transform with Modified Universal Threshold[J]. International Journal of Computer Applications, 2011, 20(5):14-19.
[5]. Inoue T, Saruwatari H, Takahashi Y, etc. Theoretical Analysis of Musical Noise in Generalized Spectral Subtraction Based on Higher Order Statistics[J]. Audio, Speech, and Language Processing, IEEE Transactions on, 2011, 19 (6): 1770-1779.
[6]. Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in adaptive data analysis, 2009, 1 (01): 1-41.
[7]. Feng Z, Chu F. Application of atomic decomposition to gear damage detection[J]. Journal of sound and vibration, 2007, 302 (1): 138-151.
[8]. Olshausen B A. Emergence of simple-cell receptive field properties by learning a sparse code for natural images[J]. Nature, 1996, 381 (6583): 607-609.
[9]. Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries[J]. Signal Processing, IEEE Transactions on, 1993, 41(12): 3397-3415.
[10]. Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. Information Theory, IEEE Transactions on, 2007, 53(12): 4655-4666.
[11]. Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit[J]. SIAM journal on scientific computing, 1998, 20(1): 33-61.
[12]. Donoho D L, Tsaig Y, Drori I, etc. Sparse solution of underdetermined systems of linear equations by stagewise orthogonal matching pursuit[J]. Information Theory, IEEE Transactions on, 2012, 58(2): 1094-1121.
[13]. Needell D, Vershynin R. Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit[J]. Foundations of Computational Mathematics, 2007, 9(3): 317-334
[14]. Wright J, Yang A Y, Ganesh A, etc. Robust face recognition via sparse representation[J]. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2009, 31(2): 210-227.
[15]. Flandrin P, Rilling G, Goncalves P. Empirical mode decomposition as a filter bank[J]. Signal Processing Letters, IEEE, 2004, 11(2): 112-114.
[16]. Huang N E, Wu M-L C, Long S R, etc. A confidence limit for the empirical mode decomposition and Hilbert spectral analysis[J]. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 2003, 459(2037): 2317-2345.
[17]. 沈长青,谢伟达,朱忠奎, etc. 基于 EEMD 和改进的形态滤波方法的轴承故障诊断研究[J]. 振动与冲击, 2013, 32 (2): 39-43.
SHEN Chang-qing, ZHU Zhong-kui, LIU Fang, et al.Rolling element bearing fault diagnosis based on EEMD and improved morphological filtering method[J].Journal ofVibration and Shock,2014,33(4):11-16.
[18]. 钟先友,赵春华,陈保家, etc. 基于形态自相关和时频切片分析的轴承故障诊断方法[J]. 振动与冲击, 2014, 33 (4): 11-16.
ZHONG Xian-you, ZHAO Chun-hua, CHEN Bao-jia, et al.Bearing fault diagnosis method based on morphological filtering,time-delayed autocorrelation and time-frequency slice analysis[J].Journal ofVibration and Shock,2014,33(4):11-16