期刊文献+

基于算子和局部正交约束的信号自适应分解方法 被引量:6

An Approach of Adaptive Signal Separation Based on Operator and Locally Orthogonal Constraint
在线阅读 下载PDF
导出
摘要 该文利用局部正交约束,采用反向投影策略,提出一种基于算子的信号自适应分解方法。该方法将输入信号建模为多个基本信号和一个残差信号之和,并且基本信号落在所定义算子的零空间中。通过仿真和实际信号的实验,展示了所提算法对于解决信号处理中的模式混叠问题的可行性,有效性和实用性。 An operator-based approach for adaptive signal separation is proposed by using the locally orthogonal constraint and adopting back projection strategy. The approach adaptively separates a signal into additive subcomponents and a residual signal, where the subcomponents are in the null space of the operators. Experiments, including simulated signals and a real-life signal, demonstrate the feasibility, efficiency, and practicability of the proposed approach for solving the mode mixing phenomenon.
出处 《电子与信息学报》 EI CSCD 北大核心 2015年第11期2613-2620,共8页 Journal of Electronics & Information Technology
基金 国家自然科学基金(61032007 61201375)~~
关键词 自适应信号分解 局部正交 反向投影策略 零空间追踪 Adaptive signal separation Locally orthogonal Back projection strategy Null space pursuit
  • 相关文献

参考文献18

  • 1Huang N E, Shen Z, and Long S R. A new view of nonlinear water waves: the Hilbert spectrum[J]. Annual Review of Fluid Mechanics, 1999, 31: 417-457.
  • 2Yu D J, Cheng J S, and Yang Y. Application of emd method and hilbert spectrum to the fault diagnosis of roller bearings[J]. Mechanical Systems and Signal Processing, 2005, 19(2): 259-270.
  • 3Pai P F and Palazotto A N. Hht-based nonlinear signal processing method for parametric and non-parametric identification of dynamical systems[J]. International Journal of Mechanical Sciences, 2008, 50(12): 1619-1635.
  • 4Huang N E, Shen Z, Long S R, et al.. The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proceedings of the Royal Society of London(series A),1998, 454(1971): 903-995.
  • 5Peng S L and Hwang W L. Adaptive signal decomposition based on local narrow band signals[J]. IEEE Transactions on Signal Processing, 2008, 56(7): 2669-2676.
  • 6Peng S L and Hwang W L. Null space pursuit: An operator-based approach to adaptive signal separation[J].IEEE Transactions on Signal Processing, 2010, 58(5): 2475-2483.
  • 7Mallat S and Zhang Z. Matching pursuits withtime-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
  • 8Vese L and Osher S. Modeling textures with total variation minimization and oscillating patterns in image processing[J]. Journal of Scientific Computing, 2003, 19(3): 553-572.
  • 9Bobin J, Starck J L, Fadili J M, et al.. Morphological component analysis: an adaptive thresholding strategy[J]. IEEE Transactions on Image Processing, 2007, 16(11): 2675-2683.
  • 10Yi X L, Hu X Y, and Peng S L. An operator-based and sparsity-based approach to adaptive signal separation[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP), Vancouver, BC, 2013: 6186-6190.

二级参考文献9

  • 1Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proceedings of the Royal Society of London (Series A), 1998, 454: 903-995.
  • 2Rilling G, Flandrin P. One or two frequencies? The empirical mode decomposition answers[J]. IEEE Transactions on Signal Processing, 2008, 56(1): 85-95.
  • 3Wu Z, Huang N E. A study of the characteristics of white noise using the empirical mode decomposition method[J]. Proceedings of the Royal Society of London (Series A), 2004, 460: 1597-1611.
  • 4Mallat S, Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
  • 5Tropp J A. Greed is good: Algorithmic results for sparse approximation[J]. IEEE Transactions on Information Theory, 2004, 50(10): 2231-2242.
  • 6Peng S L, Hwang W L. Adaptive signal decomposition based on local narrow band signals[J]. IEEE Transactions on Signal Processing, 2008, 56(7): 2669-2676.
  • 7Peng S L, Hwang W L. Null space pursuit: An operator-based approach to adaptive signal separation[J]. IEEE Transactions on Signal Processing, 2010, 58(5): 2475-2483.
  • 8张华,任若恩.基于小波分解和残差GM(1,1)-AR的非平稳时间序列预测[J].系统工程理论与实践,2010,30(6):1016-1020. 被引量:20
  • 9李振,孙新利,雷俊牛,姬国勋,刘志勇.基于d-最小割集的多状态网络可靠度矩阵分解算法[J].系统工程理论与实践,2012,32(9):1986-1995. 被引量:3

共引文献4

同被引文献44

  • 1VESE L, OSHER S. Modeling textures with total variation minimiza- tion and oscillating patterns in image processing[ J]. Journal of Sci- entific Compnting, 2003, 19(3): 553 -572.
  • 2YIX L, HU X Y, PENG S L. An operator-based and sparsity- based approach to adaptive signal separation[ C]//IEEE Internation- al Conference on Acoustics, Speech and Signal Processing, (IC- ASSP), Vancouver, BC :IEEE, 2013 : 6186 -6190.
  • 3HU X Y, PENG S L, HWANG W L. An integral operator based a- daptive signal separation approach [ C ]// IEEE International Confer- encc on Acoustics, Speech and Signal Processing, (ICASSP), Van- couver, BC:IEEE, 2013:6103 -6107.
  • 4ItUANG Y , G AO X. Clustering on heterogeneous networks [ J]. Wi- ley interdisciplinary reviews : Data mining and knowledge discovery, 2014, 4(3) : 213 -233.
  • 5Boden B, Ester M, Seidl T. Density - Based Subspaee Clustering in tleterogeneous Networks[ A]. Toon Calders. Floriana Esposito, Eyke Htillenneier, et al. Machine learning and Knowledge Discovery. in Datahases[ C]. Berlin Heidelberg: Springer, 2014:149 - 164.
  • 6Peng S L, Huang W L. Null space pursuit: An operator- based approach to adaptive signal separation [ J ]. IEEE Transactions on Signal Processing, 2010, 58 ( 5 ) : 2475 - 2483.
  • 7Kumar A, Pooja R, Singh G K. Design and performance of closed form method for cosine modulated filter bank using different windows functions [ J ]. International Journal of Speech Technology, 2014,17 (4) :427 - 441.
  • 8Rajapaksha N, Madanayake A, Bruton L T. 2D space- time wave-digital multi fan filter banks for signals consistingof multiple plane waves [ J ]. Multidimensional Systems and Sig- nal Processing,2014,25 ( 1 ) : 17 - 39.
  • 9韩慧鹏,梁红,胡旭娟.自适应IIR陷波器在信号检测中的应用[J].弹箭与制导学报,2008,28(2):315-317. 被引量:19
  • 10于晗,钟志勇,黄杰波,张建华.采用拉丁超立方采样的电力系统概率潮流计算方法[J].电力系统自动化,2009,33(21):32-35. 被引量:135

引证文献6

二级引证文献52

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部