搜索

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于复数据经验模态分解的噪声辅助信号分解方法

曲建岭 王小飞 高峰 周玉平 张翔宇

引用本文:
Citation:

基于复数据经验模态分解的噪声辅助信号分解方法

曲建岭, 王小飞, 高峰, 周玉平, 张翔宇

Noise assisted signal decomposition method based on complex empirical mode decomposition

Qu Jian-Ling, Wang Xiao-Fei, Gao Feng, Zhou Yu-Ping, Zhang Xiang-Yu
PDF
导出引用
  • 集成经验模态分解(EEMD)在一定程度上减轻了经验模态分解(EMD)中的模态混叠,但集成平均会带来新的模态混叠、频谱丢失和运算量增大等问题,影响到对信号物理特征的分析与提取. 因此,本文提出一种基于复数据经验模态分解(CEMD)的噪声辅助信号分解方法,在CEMD中以白噪声分解的内禀模态函数(IMF)在指定方向上的投影为基函数来辅助观测信号分解过程中的极值选取,从而减小模态混叠,同时利用噪声投影的影响在求包络质心时被消除的特性,减小EEMD因集成平均带来的相关问题. 仿真结果表明,本文方法在进一步降低模态混叠效应的同时,明显提高了运算速度,并且在一定程度上减轻了频谱丢失问题.
    The ensemble empirical mode decomposition has been proposed in order to alleviate mode mixing in empirical mode decomposition, but the ensemble average in it can always result in new mode mixing, spectrum losing, and computational cost increasing, which can affect the analysis and extraction of signal physical characteristics. To tackle these problems, a noise-assisted signal decomposition method based on complex empirical mode decomposition is proposed, in which the mode mixing is reduced by taking the projection of intrinsic mode functions decomposed from white noise as basis functions for signal extrema extraction. While the problems result from ensemble average are reduced because the effects of noise projection are eliminated in the process of calculating the envelope barycenter. Simulation results show that our method has further reduced mode mixing, and speeded up the operation rate visibly and alleviated spectrum losing to a certain degree.
    • 基金项目: 国家自然科学基金(批准号:61372027)资助的课题.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61372027).
    [1]

    Nathaniel E U, Beloff N, George N J 2013 Chin. Phys. B 22 084701

    [2]

    Yang Y F, Wu Y F, Ren X M 2010 Acta Phys. Sin. 59 3778 (in Chinese) [杨永锋, 吴亚锋, 任兴民 2010 物理学报 59 3778]

    [3]

    Hou W B, Liu T Q, Li X Y 2010 Acta Phys. Sin. 59 3531 (in Chinese) [侯王宾, 刘天琪, 李兴源 2010 物理学报 59 3531]

    [4]

    Wang W B, Zhang X D, Wang X L 2013 Acta Phys. Sin. 62 069701 (in Chinese) [王文波, 张晓东, 汪祥莉 2013 物理学报 62 069701]

    [5]

    Zou M W, Feng G L, Gao X Q 2006 Chin. Phys. B 15 1384

    [6]

    Wang W B, Wang X L 2013 Acta Phys. Sin. 62 209701 (in Chinese)[王文波, 汪祥莉 2013 物理学报 62 209701]

    [7]

    Sweeney K T, McLoone S F 2013 IEEE Trans. Biomed. Eng. 60 97

    [8]

    Huang N E, Shen Z, Long S R 1999 Ann. Rev. Fluid Mech 31 417

    [9]

    Wu Z H, Huang N E 2009 Advances in Adaptive Data Analysis 1 1

    [10]

    Fu M J, Zhuang J J, Hou F Z 2010 Chin. Phys. B 19 058701

    [11]

    Tang J 2013 Acta Phys. Sin. 62 129701 (in Chinese)[唐洁 2013 物理学报 62 129701]

    [12]

    Xue C F, Hou W, Zhao J H 2013 Acta Phys. Sin. 62 109203 (in Chinese) [薛春芳, 侯威, 赵俊虎 2013 物理学报 62 109203]

    [13]

    Tang J 2014 Acta Phys. Sin. 63 049701 (in Chinese) [唐洁 2014 物理学报 63 049701]

    [14]

    Torres M E, Colominas M A, Schlotthauer G 2011 IEEE ICASSP Prague, May22-27, 2011 p4144

    [15]

    Tanaka T, Mandic D P 2006 IEEE Signal Process Lett. 14 101

    [16]

    Altaf M U B, Gautama T, Tanaka T 2007 IEEE ICASSP 3 1009

    [17]

    Rilling G, Flandrin P, Gonalves P 2007 IEEE Signal Process Lett. 14 936

    [18]

    Yang W X, Court T, Tavner P 2011 J. Sound Vib. 330 3766

    [19]

    Ahrabian A, Rehman A U, Mandic D 2013 IEEE Signal Process Lett. 20 245

    [20]

    Gao Y C, Sang E F, Shen Z Y 2008 CISP'08. Congress on Image and Signal Processing Sanya Hainan, May 27-30, 2008 p141

    [21]

    Rilling G, Flandrin P, Gonçalves P 2003 Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing Grado (I), June 2003 p1

    [22]

    Li X J 2008 Acta Phys. Sin. 57 5366 (in Chinese) [李晓静 2008 物理学报 57 5366]

    [23]

    Cao X Q, Song J Q, Zhu X Q 2012 Chin. Phys. B 21 020203

    [24]

    Zhang W F, Zhao Q 2013 Chin. Phys. B 22 120201

    [25]

    Wang W, Xu Y, Lu S P 2011 Acta Phys. Sin. 60 030205 (in Chinese) [王雯, 徐燕, 鲁世平 2011 物理学报 60 030205]

    [26]

    Lin W T, Chen L H, Ouyang C 2012 Acta Phys. Sin. 61 080204 (in Chinese)[林万涛, 陈丽华, 欧阳成 2012 物理学报 61 080204]

    [27]

    Du Z J, Lin W T, Mo J Q 2012 Chin Phys. B 21 090201

    [28]

    Newman M, Compo G P, Alexander M A 2003 J. Clim. 16 3853

  • [1]

    Nathaniel E U, Beloff N, George N J 2013 Chin. Phys. B 22 084701

    [2]

    Yang Y F, Wu Y F, Ren X M 2010 Acta Phys. Sin. 59 3778 (in Chinese) [杨永锋, 吴亚锋, 任兴民 2010 物理学报 59 3778]

    [3]

    Hou W B, Liu T Q, Li X Y 2010 Acta Phys. Sin. 59 3531 (in Chinese) [侯王宾, 刘天琪, 李兴源 2010 物理学报 59 3531]

    [4]

    Wang W B, Zhang X D, Wang X L 2013 Acta Phys. Sin. 62 069701 (in Chinese) [王文波, 张晓东, 汪祥莉 2013 物理学报 62 069701]

    [5]

    Zou M W, Feng G L, Gao X Q 2006 Chin. Phys. B 15 1384

    [6]

    Wang W B, Wang X L 2013 Acta Phys. Sin. 62 209701 (in Chinese)[王文波, 汪祥莉 2013 物理学报 62 209701]

    [7]

    Sweeney K T, McLoone S F 2013 IEEE Trans. Biomed. Eng. 60 97

    [8]

    Huang N E, Shen Z, Long S R 1999 Ann. Rev. Fluid Mech 31 417

    [9]

    Wu Z H, Huang N E 2009 Advances in Adaptive Data Analysis 1 1

    [10]

    Fu M J, Zhuang J J, Hou F Z 2010 Chin. Phys. B 19 058701

    [11]

    Tang J 2013 Acta Phys. Sin. 62 129701 (in Chinese)[唐洁 2013 物理学报 62 129701]

    [12]

    Xue C F, Hou W, Zhao J H 2013 Acta Phys. Sin. 62 109203 (in Chinese) [薛春芳, 侯威, 赵俊虎 2013 物理学报 62 109203]

    [13]

    Tang J 2014 Acta Phys. Sin. 63 049701 (in Chinese) [唐洁 2014 物理学报 63 049701]

    [14]

    Torres M E, Colominas M A, Schlotthauer G 2011 IEEE ICASSP Prague, May22-27, 2011 p4144

    [15]

    Tanaka T, Mandic D P 2006 IEEE Signal Process Lett. 14 101

    [16]

    Altaf M U B, Gautama T, Tanaka T 2007 IEEE ICASSP 3 1009

    [17]

    Rilling G, Flandrin P, Gonalves P 2007 IEEE Signal Process Lett. 14 936

    [18]

    Yang W X, Court T, Tavner P 2011 J. Sound Vib. 330 3766

    [19]

    Ahrabian A, Rehman A U, Mandic D 2013 IEEE Signal Process Lett. 20 245

    [20]

    Gao Y C, Sang E F, Shen Z Y 2008 CISP'08. Congress on Image and Signal Processing Sanya Hainan, May 27-30, 2008 p141

    [21]

    Rilling G, Flandrin P, Gonçalves P 2003 Proceedings of IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing Grado (I), June 2003 p1

    [22]

    Li X J 2008 Acta Phys. Sin. 57 5366 (in Chinese) [李晓静 2008 物理学报 57 5366]

    [23]

    Cao X Q, Song J Q, Zhu X Q 2012 Chin. Phys. B 21 020203

    [24]

    Zhang W F, Zhao Q 2013 Chin. Phys. B 22 120201

    [25]

    Wang W, Xu Y, Lu S P 2011 Acta Phys. Sin. 60 030205 (in Chinese) [王雯, 徐燕, 鲁世平 2011 物理学报 60 030205]

    [26]

    Lin W T, Chen L H, Ouyang C 2012 Acta Phys. Sin. 61 080204 (in Chinese)[林万涛, 陈丽华, 欧阳成 2012 物理学报 61 080204]

    [27]

    Du Z J, Lin W T, Mo J Q 2012 Chin Phys. B 21 090201

    [28]

    Newman M, Compo G P, Alexander M A 2003 J. Clim. 16 3853

  • [1] 景鹏, 张学军, 孙知信. 基于弹性变分模态分解的癫痫脑电信号分类方法. 物理学报, 2021, 70(1): 018702. doi: 10.7498/aps.70.20200904
    [2] 牛晓东, 卢莉蓉, 王鉴, 韩星程, 郭树言, 王黎明. 基于改进经验模态分解域内心动物理特征识别模式分量的心电信号重建. 物理学报, 2021, 70(3): 038702. doi: 10.7498/aps.70.20201122
    [3] 许子非, 岳敏楠, 李春. 优化递归变分模态分解及其在非线性信号处理中的应用. 物理学报, 2019, 68(23): 238401. doi: 10.7498/aps.68.20191005
    [4] 杜义浩, 齐文靖, 邹策, 张晋铭, 谢博多, 谢平. 基于变分模态分解-相干分析的肌间耦合特性. 物理学报, 2017, 66(6): 068701. doi: 10.7498/aps.66.068701
    [5] 谢平, 杨芳梅, 李欣欣, 杨勇, 陈晓玲, 张利泰. 基于变分模态分解-传递熵的脑肌电信号耦合分析. 物理学报, 2016, 65(11): 118701. doi: 10.7498/aps.65.118701
    [6] 曾彭, 刘红星, 宁新宝, 庄建军, 张兴敢. 总体经验模态分解能量向量用于ECG能量分布的研究. 物理学报, 2015, 64(7): 078701. doi: 10.7498/aps.64.078701
    [7] 张玉燕, 周航, 闫美素. 基于经验模态分解的自混合干涉相位提取方法研究. 物理学报, 2015, 64(5): 054203. doi: 10.7498/aps.64.054203
    [8] 唐洁. 基于集合经验模态分解的类星体光变周期及其混沌特性分析. 物理学报, 2014, 63(4): 049701. doi: 10.7498/aps.63.049701
    [9] 王小飞, 曲建岭, 高峰, 周玉平, 张翔宇. 基于噪声辅助非均匀采样复数据经验模态分解的混沌信号降噪. 物理学报, 2014, 63(17): 170203. doi: 10.7498/aps.63.170203
    [10] 薛春芳, 侯威, 赵俊虎, 王式功. 集合经验模态分解在区域降水变化多尺度分析及气候变化响应研究中的应用. 物理学报, 2013, 62(10): 109203. doi: 10.7498/aps.62.109203
    [11] 唐洁. 基于聚合经验模态分解方法的OJ 287 射电流量变化周期分析. 物理学报, 2013, 62(12): 129701. doi: 10.7498/aps.62.129701
    [12] 王文波, 张晓东, 汪祥莉. 基于独立成分分析和经验模态分解的混沌信号降噪. 物理学报, 2013, 62(5): 050201. doi: 10.7498/aps.62.050201
    [13] 王文波, 张晓东, 汪祥莉. 脉冲星信号的经验模态分解模态单元比例萎缩消噪算法. 物理学报, 2013, 62(6): 069701. doi: 10.7498/aps.62.069701
    [14] 张学清, 梁军. 基于EEMD-近似熵和储备池的风电功率混沌时间序列预测模型. 物理学报, 2013, 62(5): 050505. doi: 10.7498/aps.62.050505
    [15] 王文波, 汪祥莉. 噪声模态单元预判的经验模态分解脉冲星信号消噪. 物理学报, 2013, 62(20): 209701. doi: 10.7498/aps.62.209701
    [16] 裴利军, 邱本花. 模态分解法在非恒同耦合系统同步研究中的推广. 物理学报, 2010, 59(1): 164-170. doi: 10.7498/aps.59.164
    [17] 侯王宾, 刘天琪, 李兴源. 基于经验模态分解滤波的低频振荡Prony分析. 物理学报, 2010, 59(5): 3531-3537. doi: 10.7498/aps.59.3531
    [18] 杨永锋, 吴亚锋, 任兴民, 裘焱. 随机噪声对经验模态分解非线性信号的影响. 物理学报, 2010, 59(6): 3778-3784. doi: 10.7498/aps.59.3778
    [19] 龚志强, 邹明玮, 高新全, 董文杰. 基于非线性时间序列分析经验模态分解和小波分解异同性的研究. 物理学报, 2005, 54(8): 3947-3957. doi: 10.7498/aps.54.3947
    [20] 李鸿光, 孟 光. 基于经验模式分解的混沌干扰下谐波信号的提取方法. 物理学报, 2004, 53(7): 2069-2073. doi: 10.7498/aps.53.2069
计量
  • 文章访问数:  5819
  • PDF下载量:  661
  • 被引次数: 0
出版历程
  • 收稿日期:  2014-01-15
  • 修回日期:  2014-02-26
  • 刊出日期:  2014-06-05

/

返回文章
返回