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

Abstract
Figure/Table
References (18)
Related Citation (15)

Download: PDF (2860 KB)
Export: BibTeX | EndNote (RIS)

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.

Key words： Adaptive signal separation Locally orthogonal Back projection strategy Null space pursuit

Received: 17 March 2015 Published: 27 July 2015

PACS:

TN911.7

Fund:

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

Corresponding Authors: Yi Xiao-lei E-mail: yixiaoleibj@163.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yi Xiao-lei
	Peng Si-long
	Luan Shi-chao

Cite this article:

Yi Xiao-lei,Peng Si-long,Luan Shi-chao. An Approach of Adaptive Signal Separation Based on Operator and Locally Orthogonal Constraint[J]. JEIT, 2015, 37(11): 2613-2620.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150318 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I11/2613

[1]	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.
[2]	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.
[3]	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.
[4]	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.
[5]	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.
[6]	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.
[7]	Mallat S and Zhang Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415.
[8]	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.
[9]	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.
[10]	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.
[11]	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.
[12]	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 Engineering—Theory & Practice, 2013, 33(5): 1283-1288.
[13]	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.
[14]	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.
[15]	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.
[16]	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.
[17]	NSP codes in Matlab[OL]. http://mda.ia.ac.cn /English/ publications/publicationsindex.htm, 2010.12.
[18]	Fast EMD/EEMD code[OL]. http://rcada.ncu.edu.tw/ research1.htm, 2014.9.