首页 > 论文 > 红外与激光工程 > 47卷 > 7期(pp:726005--1)

基于稀疏表示与粒子群优化算法的非平稳信号去噪研究

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

  • 摘要
  • 论文信息
  • 参考文献
  • 被引情况
  • PDF全文
分享:

摘要

非平稳信号的去噪是信号处理中的热点和难点。文中以冲击原子作为稀疏表示基, 构建了仅对人文噪声敏感的冗余字典。并使用粒子群优化算法对匹配追踪算法进行优化, 提出了基于稀疏表示与粒子群优化算法的非平稳信号去噪方法。为检验方法的有效性, 论文首先进行了针对性的仿真实验。然后将所述方法用于实测的大地电磁信号处理。结果表明, 所述方法可以在保留有用信号的前提下, 有效分离出类充放电噪声、脉冲噪声以及其它多种不规则噪声, 显著提高非平稳信号的信噪比。

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.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP29

DOI:10.3788/irla201847.0726005

所属栏目:信息获取与辨识

基金项目:国家863计划(2014AA06A602); 有色金属成矿预测与地质环境监测教育部重点实验室开放基金(2017YSJS09)

收稿日期:2018-02-05

修改稿日期:2018-03-03

网络出版日期:--

作者单位    点击查看

叶 华:中南大学 信息科学与工程学院, 湖南 长沙 410083
谭冠政:中南大学 信息科学与工程学院, 湖南 长沙 410083
李 广:有色金属成矿预测与地质环境监测教育部重点实验室(中南大学), 湖南 长沙 410083东华理工大学 地球物理与测控技术学院, 武汉 南昌 330013
刘晓琼:湖南师范大学 物理与信息科学学院, 湖南 长沙 410081
李 晋:湖南师范大学 物理与信息科学学院, 湖南 长沙 410081
周 聪:有色金属成矿预测与地质环境监测教育部重点实验室(中南大学), 湖南 长沙 410083
朱会杰:中国人民解放军63983部队, 江苏 无锡 214035

联系人作者:叶华(yehuawuhan@163.com)

备注:叶华(1977-), 女, 讲师, 博士生, 主要从事人体行为识别、视觉认知计算等方面的研究。

【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】Candès 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】Candès 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)

引用该论文

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): 0726005

叶 华,谭冠政,李 广,刘晓琼,李 晋,周 聪,朱会杰. 基于稀疏表示与粒子群优化算法的非平稳信号去噪研究[J]. 红外与激光工程, 2018, 47(7): 0726005

您的浏览器不支持PDF插件,请使用最新的(Chrome/Fire Fox等)浏览器.或者您还可以点击此处下载该论文PDF