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.
衣晓蕾, 彭思龙,栾世超. 基于算子和局部正交约束的信号自适应分解方法[J]. 电子与信息学报, 2015, 37(11): 2613-2620.
Yi Xiao-lei, Peng Si-long, Luan Shi-chao. An Approach of Adaptive Signal Separation Based on Operator and Locally Orthogonal Constraint. JEIT, 2015, 37(11): 2613-2620.
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.
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.