De-noising nonstationary signal based on sparse representation and particle swarm optimization

Ye Hua; Tan Guanzheng; Li Guang; Liu Xiaoqiong; Li Jin; Zhou Cong; Zhu Huijie

doi:10.3788/IRLA201847.0726005

Volume 47 Issue 7

Jul. 2018

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Ye Hua, Tan Guanzheng, Li Guang, Liu Xiaoqiong, Li Jin, Zhou Cong, Zhu Huijie. De-noising nonstationary signal based on sparse representation and particle swarm optimization[J]. Infrared and Laser Engineering, 2018, 47(7): 726005-0726005(8). doi: 10.3788/IRLA201847.0726005

Citation:

Ye Hua, Tan Guanzheng, Li Guang, Liu Xiaoqiong, Li Jin, Zhou Cong, Zhu Huijie. De-noising nonstationary signal based on sparse representation and particle swarm optimization[J]. Infrared and Laser Engineering, 2018, 47(7): 726005-0726005(8). doi: 10.3788/IRLA201847.0726005

De-noising nonstationary signal based on sparse representation and particle swarm optimization

doi: 10.3788/IRLA201847.0726005

1.
School of Information Science and Engineering,Central South University,Changsha 410083,China;
2.
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University),Ministry of Education,Changsha 410083,China;
3.
School of Geophysics and Measurement-control Technology,East China University of Technology,Nanchang 330013,China;
4.
Institute of Physics and Information Science,Hunan Normal University,Changsha 410081,China;
5.
PLA 63983 Army,Wuxi 214035,China

Received Date: 2018-02-05
Rev Recd Date: 2018-03-03
Publish Date: 2018-07-25

Abstract

It is difficult and important to de-noise nonstationary signal. To this end, a new noise attenuation method for nonstationary signal was proposed based on sparse representation and Particle Swarm Optimization(PSO). A redundant dictionary which is insensitive to useful signal was developed for the representation of cultural noises. PSO was used to improve the search strategy of Matching Pursuit(MP). Simulated experiments and real MT data were used to test the proposed scheme. As a conclusion, not only charge-discharge-like noise can be effectively removed, spikes and some other irregular noise can also be well suppressed. The apparent resistivity and phase curves obtained after applying our scheme are greatly improved over previous.
- sparse representation,
- nonstationary signal,
- particle swarm optimization,
- noise attenuation,
- redundant dictionary

References

[1]	Chen Zhe, Wang Rong, Zhou Wenying, et al. Review on measurement parametrics and methods for nonstationary signal[J]. Journal of Data Acquisition Processing, 2017, 32(4):667-683. (in Chinese)
[2]	Feng Weiting, Liang Qing, Gu Jing. Time-frequency analysis of non-stationary signal based on sparse representation algorithm[J]. Journal of Xi'an University of Posts Telecommunications, 2016, 21(6):88-92. (in Chinese)
[3]	Larnier H, Sailhac P, Chambodut A. New application of wavelets in magnetotelluric data processing:reducing impedance bias[J]. Earth Planets Space, 2016, 68:70.
[4]	Trad D O, Travassos J M. Wavelet filtering of magnetotelluric data[J]. Geophysics, 2000, 65:482-491.
[5]	Neukirch M, Garcia X. Nonstationary magnetotelluric data processing with instantaneous parameter[J]. Journal of Geophysical Research Solid Earth, 2014, 119:1634-1654.
[6]	Tang J T, Hua X R, Cao Z M, et al. Hilbert-huang transformation and noise suppression of magnetotelluric sounding data[J]. Chinese J Geophys, 2008, 51:603-610. (in Chinese)
[7]	Tang J T, Li J, Xiao X, et al. Mathematical morphology filtering an noise suppression of magnetotelluric sounding data[J]. Chinese J Geophys, 2012, 55:1784-1793. (in Chinese)
[8]	Li G, Xiao X, Tang J T, et al. Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering[J]. Applied Geophysics, 2017, 14:581-589.
[9]	Cands E J, Tao T. Near-optimal signal recovery from random projections:universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52:5406-5425.
[10]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52:1289-1306.
[11]	Cands E J, Wakin M B. An introduction to compressive sampling[J]. Signal Processing Magazine, IEEE, 2008, 25:21-30.
[12]	Tang J T, Li G, Xiao X, et al. Strong noise separation for magnetotelluric data based on a signal reconstruction algorithm of compressive sensing[J]. Chinese J Geophys, 2017, 60:3642-3654. (in Chinese)
[13]	Cai J H, Tang J T, Hua X R, et al. An analysis method for magnetotelluric data based on the Hilbert-Huang Transform[J]. Exploration Geophysics, 2009, 40:197-205.
[14]	Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing,1993, 41:3397-3415.
[15]	Cui L, Kang C, Wang H, et al. Application of composite dictionary multi-atom matching in gear fault diagnosis[J]. Sensors, 2011, 11:5981-6002.
[16]	Cui L, Wang J, Lee S. Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis[J]. Journal of Sound Vibration, 2014, 333:2840-2862.
[17]	Wang X, Zhu H, Wang D, et al. The diagnosis of rolling bearing based on the parameters of pulse atoms and degree of cyclostationarity[J]. Journal of Vibroengineering, 2013, 15:1560-1575.
[18]	Stefanoiu D, Lonescu F. A genetic matching pursuit algorithm[C]//International Symposium on Signal Processing and ITS Applications, Proceedings. IEEE, 2003:577-580.
[19]	Wang C G, Liu J J, Sun J X. Algorithm of searching for the best matching atoms based on particle swarm optimization in sparse decomposition[J]. Journal of National University of Defense Technology, 2008, 30:83-87. (in Chinese)
[20]	Zhang Y, Wang H L, Lu J H, et al. Calibration method of optical errors for star sensor based on particle swarm optimization algorithm[J]. Infrared and Laser Engineering, 2017, 46(10):1017002. (in Chinese)
[21]	Kennedy J, Eberhart R. Particle swarm optimization[C]//IEEE International Conference on Neural Networks, 1995. Proceedings, 1995, 4:1942-1948.
[22]	Li Y, Chen Q. Sound event recognition based on optimized orthogonal matching pursuit[J]. Journal of Electronics Information Technology, 2017, 39:183-190. (in Chinese)

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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De-noising nonstationary signal based on sparse representation and particle swarm optimization

1. School of Information Science and Engineering,Central South University,Changsha 410083,China;

2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University),Ministry of Education,Changsha 410083,China;

3. School of Geophysics and Measurement-control Technology,East China University of Technology,Nanchang 330013,China;

4. Institute of Physics and Information Science,Hunan Normal University,Changsha 410081,China;

5. PLA 63983 Army,Wuxi 214035,China

Received Date: 2018-02-05

Rev Recd Date: 2018-03-03

Publish Date: 2018-07-25

Key words:

Abstract: It is difficult and important to de-noise nonstationary signal. To this end, a new noise attenuation method for nonstationary signal was proposed based on sparse representation and Particle Swarm Optimization(PSO). A redundant dictionary which is insensitive to useful signal was developed for the representation of cultural noises. PSO was used to improve the search strategy of Matching Pursuit(MP). Simulated experiments and real MT data were used to test the proposed scheme. As a conclusion, not only charge-discharge-like noise can be effectively removed, spikes and some other irregular noise can also be well suppressed. The apparent resistivity and phase curves obtained after applying our scheme are greatly improved over previous.

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Reference (22)

[1]	Chen Zhe, Wang Rong, Zhou Wenying, et al. Review on measurement parametrics and methods for nonstationary signal[J]. Journal of Data Acquisition Processing, 2017, 32(4):667-683. (in Chinese)
[2]	Feng Weiting, Liang Qing, Gu Jing. Time-frequency analysis of non-stationary signal based on sparse representation algorithm[J]. Journal of Xi'an University of Posts Telecommunications, 2016, 21(6):88-92. (in Chinese)
[3]	Larnier H, Sailhac P, Chambodut A. New application of wavelets in magnetotelluric data processing:reducing impedance bias[J]. Earth Planets Space, 2016, 68:70.
[4]	Trad D O, Travassos J M. Wavelet filtering of magnetotelluric data[J]. Geophysics, 2000, 65:482-491.
[5]	Neukirch M, Garcia X. Nonstationary magnetotelluric data processing with instantaneous parameter[J]. Journal of Geophysical Research Solid Earth, 2014, 119:1634-1654.
[6]	Tang J T, Hua X R, Cao Z M, et al. Hilbert-huang transformation and noise suppression of magnetotelluric sounding data[J]. Chinese J Geophys, 2008, 51:603-610. (in Chinese)
[7]	Tang J T, Li J, Xiao X, et al. Mathematical morphology filtering an noise suppression of magnetotelluric sounding data[J]. Chinese J Geophys, 2012, 55:1784-1793. (in Chinese)
[8]	Li G, Xiao X, Tang J T, et al. Near-source noise suppression of AMT by compressive sensing and mathematical morphology filtering[J]. Applied Geophysics, 2017, 14:581-589.
[9]	Cands E J, Tao T. Near-optimal signal recovery from random projections:universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52:5406-5425.
[10]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52:1289-1306.
[11]	Cands E J, Wakin M B. An introduction to compressive sampling[J]. Signal Processing Magazine, IEEE, 2008, 25:21-30.
[12]	Tang J T, Li G, Xiao X, et al. Strong noise separation for magnetotelluric data based on a signal reconstruction algorithm of compressive sensing[J]. Chinese J Geophys, 2017, 60:3642-3654. (in Chinese)
[13]	Cai J H, Tang J T, Hua X R, et al. An analysis method for magnetotelluric data based on the Hilbert-Huang Transform[J]. Exploration Geophysics, 2009, 40:197-205.
[14]	Mallat S G, Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing,1993, 41:3397-3415.
[15]	Cui L, Kang C, Wang H, et al. Application of composite dictionary multi-atom matching in gear fault diagnosis[J]. Sensors, 2011, 11:5981-6002.
[16]	Cui L, Wang J, Lee S. Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis[J]. Journal of Sound Vibration, 2014, 333:2840-2862.
[17]	Wang X, Zhu H, Wang D, et al. The diagnosis of rolling bearing based on the parameters of pulse atoms and degree of cyclostationarity[J]. Journal of Vibroengineering, 2013, 15:1560-1575.
[18]	Stefanoiu D, Lonescu F. A genetic matching pursuit algorithm[C]//International Symposium on Signal Processing and ITS Applications, Proceedings. IEEE, 2003:577-580.
[19]	Wang C G, Liu J J, Sun J X. Algorithm of searching for the best matching atoms based on particle swarm optimization in sparse decomposition[J]. Journal of National University of Defense Technology, 2008, 30:83-87. (in Chinese)
[20]	Zhang Y, Wang H L, Lu J H, et al. Calibration method of optical errors for star sensor based on particle swarm optimization algorithm[J]. Infrared and Laser Engineering, 2017, 46(10):1017002. (in Chinese)
[21]	Kennedy J, Eberhart R. Particle swarm optimization[C]//IEEE International Conference on Neural Networks, 1995. Proceedings, 1995, 4:1942-1948.
[22]	Li Y, Chen Q. Sound event recognition based on optimized orthogonal matching pursuit[J]. Journal of Electronics Information Technology, 2017, 39:183-190. (in Chinese)

De-noising nonstationary signal based on sparse representation and particle swarm optimization

doi: 10.3788/IRLA201847.0726005

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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