A time varying filter approach for empirical mode decomposition

被引:178
|
作者
Li, Heng [1 ,2 ]
Li, Zhi [1 ,3 ]
Mo, Wei [1 ]
机构
[1] Xidian Univ, Sch Mechanoelect Engn, Xian 710071, Peoples R China
[2] Guangxi Transportat Res Inst Co Ltd, 6,Gaoxin 2 Rd, Nanning 530007, Guangxi, Peoples R China
[3] Guilin Univ Aerosp Technol, Guilin 541004, Peoples R China
来源
SIGNAL PROCESSING | 2017年 / 138卷
关键词
Empirical mode decomposition; Time varying filter; Adaptive signal analysis; Time-frequency analysis; Mode mixing; SIGNAL; FREQUENCY; DESIGN;
D O I
10.1016/j.sigpro.2017.03.019
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
A modified version of empirical mode decomposition (EMD) is presented to solve the mode mixing problem. The sifting process is completed using a time varying filter technique. In this paper, the local cut-off frequency is adaptively designed by fully facilitating the instantaneous amplitude and frequency information. Then nonuniform B-spline approximation is adopted as a time varying filter. In order to solve the intermittence problem, a cut-off frequency realignment algorithm is also introduced. Aimed at improving the performance under low sampling rates, a bandwidth criterion for intrinsic mode function (IMF) is proposed. The proposed method is fully adaptive and suitable for the analysis of linear and non-stationary signals. Compared with EMD, the proposed method is able to improve the frequency separation performance, as well as the stability under low sampling rates. Besides, the proposed method is robust against noise interference. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:146 / 158
页数:13
相关论文
共 50 条
  • [41] A Novel Boundary Extension Approach for Empirical Mode Decomposition
    Liu, Zhuofu
    [J]. INTELLIGENT COMPUTING, PART I: INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING, ICIC 2006, PART I, 2006, 4113 : 299 - 304
  • [42] A New Approach For Nonlinear Signals Empirical Mode Decomposition
    Kore, G. V.
    Kore, S. N.
    [J]. 1ST INTERNATIONAL CONFERENCE ON COMPUTING COMMUNICATION CONTROL AND AUTOMATION ICCUBEA 2015, 2015, : 805 - 810
  • [43] Empirical Mode Decomposition in a Time-Scale Framework
    Colominas, Marcelo A.
    Schlotthauer, Gaston
    [J]. 2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2016, : 155 - 159
  • [44] A review on empirical mode decomposition in forecasting time series
    Awajan, Ahmad M.
    Ismail, Mohd Tahir
    AL Wadi, S.
    [J]. ITALIAN JOURNAL OF PURE AND APPLIED MATHEMATICS, 2019, (42): : 301 - 323
  • [45] A review on empirical mode decomposition in forecasting time series
    Awajan, Ahmad M.
    Ismail, Mohd Tahir
    Wadi, S.A.L.
    [J]. Italian Journal of Pure and Applied Mathematics, 2019, (42): : 301 - 323
  • [46] Using filter bank property to simplify the calculations of Empirical Mode Decomposition
    Abd-el-Malek, Mina B.
    Hann, Samer S.
    [J]. COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2018, 62 : 429 - 444
  • [47] Improved Goldstein SAR Interferogram Filter Based on Empirical Mode Decomposition
    Song, Rui
    Guo, Huadong
    Liu, Guang
    Perski, Zbigniew
    Fan, Jinghui
    [J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2014, 11 (02) : 399 - 403
  • [48] Implementation of Empirical Mode Decomposition Based Algorithm for Shunt Active Filter
    Shukla, Stuti
    Mishra, Sukumar
    Singh, Bhim
    Kumar, Shailendra
    [J]. IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2017, 53 (03) : 2392 - 2400
  • [49] Identification of linear time-varying dynamical systems using Hilbert transform and empirical mode decomposition method
    Shi, Z. Y.
    Law, S. S.
    [J]. JOURNAL OF APPLIED MECHANICS-TRANSACTIONS OF THE ASME, 2007, 74 (02): : 223 - 230
  • [50] Pure harmonics extracting from time-varying power signal based on improved empirical mode decomposition
    Wu, Jiangwei
    Wang, Xue
    Sun, Xinyao
    Liu, Youda
    [J]. MEASUREMENT, 2014, 49 : 216 - 225