基于算子和局部正交约束的信号自适应分解方法

衣晓蕾; 彭思龙; 栾世超

doi:10.11999/JEIT150318

基于算子和局部正交约束的信号自适应分解方法

doi: 10.11999/JEIT150318

1.
(中国科学院自动化研究所北京 100190) ②(中国航空综合技术研究所北京 100028)

基金项目:

国家自然科学基金(61032007, 61201375)

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出版历程
- 收稿日期: 2015-03-17
- 修回日期: 2015-06-12
- 刊出日期: 2015-11-19

An Approach of Adaptive Signal Separation Based on Operator and Locally Orthogonal Constraint

1.
(Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China)
2.
(China Aero-polytechnology Establishment, Beijing 100028, China)

Funds:

The National Natural Science Foundation of China (61032007, 61201375)

摘要

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。
- 自适应信号分解 /
- 局部正交 /
- 反向投影策略 /
- 零空间追踪
Abstract: 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.
- Adaptive signal separation /
- Locally orthogonal /
- Back projection strategy /
- Null space pursuit

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参考文献(17)

Huang 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.

Yu 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.

Pai 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.

Huang 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.

Peng 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.

Peng 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.

Mallat S and Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.

Vese 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.

Bobin 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.

Yi 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.

Hu X Y, Peng S L, and Hwang W L. Multicomponent am-fm signal separation and demodulation with null space pursuit[J]. Signal, Image and Video Processing, 2013, 7(6): 1093-1102.

肖维维, 栾卫军, 彭思龙. 基于三阶线性微分算子的零空间追踪算法[J]. 系统工程理论与实践, 2013, 33(5): 1283-1288.

Xiao Wei wei, Luan Wei jun, and Peng si long. Null space pursuit based on the three order linear differentialoperator[J]. Systems EngineeringTheory Practice, 2013, 33(5): 1283-1288.

Hu X Y, Peng S L, and Hwang W L. An integral operator based adaptive signal separation approach[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP), Vancouver, BC, 2013: 6103-6107.

Hu X Y, Peng S L, and Hwang W L. Adaptive integral operators for signal separation[J]. Signal Processing Letters, 2015, 22(9): 1383-1387.

Wu Z H and Huang N E. Ensemble empirical mode decomposition: A noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.

Deering R and Kaiser J F. The use of a masking signal to improve empirical mode decomposition[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, (ICASSP), Philadelphia, Pennsylvania, USA, 2005: 18-23.

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