A method to detect and filter the cross terms in the Wigner-Ville distribution

2017 ◽  
Author(s):  
Dorel Aiordachioaie ◽  
Theodor D. Popescu
Keyword(s):  
Cross Terms ◽  
The Cross

On the cross-terms in LPPs

2009 ◽  
Author(s):  
Xinbo Li ◽  
Youyi Wang ◽  
Guoan Bi ◽  
Yaowu Shi ◽  
Xiumei Li
Keyword(s):  
Cross Terms ◽  
The Cross

scholarly journals Separating body and Rayleigh waves with cross terms of the cross‐correlation tensor of ambient noise

2014 ◽  
Vol 119 (3) ◽  
pp. 2005-2018 ◽  
Author(s):  
Ryota Takagi ◽  
Hisashi Nakahara ◽  
Toshio Kono ◽  
Tomomi Okada
Keyword(s):  
Rayleigh Waves ◽  
Ambient Noise ◽  
Cross Correlation ◽  
Correlation Tensor ◽  
Cross Terms ◽  
The Cross

scholarly journals Estimating the Rayleigh-wave impulse response between seismic stations with the cross terms of the Green tensor

2011 ◽  
Vol 38 (16) ◽  
pp. n/a-n/a ◽  
Author(s):  
Kasper van Wijk ◽  
T. Dylan Mikesell ◽  
Vera Schulte-Pelkum ◽  
Josh Stachnik
Keyword(s):  
Rayleigh Wave ◽  
Impulse Response ◽  
Green Tensor ◽  
Seismic Stations ◽  
Cross Terms ◽  
The Cross

A New Joint Algorithm Based on EMD-ICA to Reduce the Cross-Term in Wigner-Vill Distribution

2013 ◽  
Vol 347-350 ◽  
pp. 2407-2411
Author(s):  
Wei Liu ◽  
Zeng Li Liu ◽  
Xin Xin He ◽  
Hao Ran Yao
Keyword(s):  
Cross Term ◽  
Time Frequency ◽  
Frequency Representation ◽  
Cross Terms ◽  
Fastica Algorithm ◽  
The Cross

Wigner-Vill distribution (WVD) will inevitably have cross-terms when it used for time-frequency representation in a multi-component signal. In order to suppress the cross-terms in Wigner-Vill distribution, this paper proposes a joint algorithm based on EMD-ICA. This algorithm resolves a multi-component signal into several IMF components used by EMD at first, and then each IMF component is used FastICA algorithm for processing, and next seeks the Wigner-Vill distribution of each component, finally, add up the results. This method effectively inhibited the emergence of cross-terms in Wigner-Vill distribution, and keeps the properties of time-frequency concentration higher.

Time-Frequency Feature Extraction without Cross-Terms Based on Acoustic Signal in Rotor Malfunctions

2005 ◽  
Vol 293-294 ◽  
pp. 467-474 ◽  
Author(s):  
F.Q. Wu ◽  
Guang Meng
Keyword(s):  
Feature Extraction ◽  
Independent Component Analysis ◽  
Empirical Mode Decomposition ◽  
Wigner Distribution ◽  
Component Analysis ◽  
Independent Component ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Cross Terms ◽  
The Cross

An effective approach is presented to eliminate the cross-terms in Wigner distribution by ICA (independent component analysis) and EMD (empirical mode decomposition), through which the cross-terms caused by the uncorrelated mixing signals can be removed successfully. This method is used for time-varying signal analysis and is powerful in signal feature extraction, especially for joint time frequency resolution, which is demonstrated by numerical examples. To further understand the method and its application, a detailed analysis about abrupt unbalance experimental example is shown to explain the cause of malfunction as well as its occurrence and phenomenon. In addition, the proposed approach based upon independent component analysis, empirical mode decomposition method and wigner distribution allows the separation and analysis of the sources with nonlinear and non-stationary properties. In this method, the main conceptual innovations are the associated introduction of ‘source separation’ and ‘intrinsic mode functions’ based on the local properties of the mixed signals, which makes the instantaneous frequency meaningful; the method serves to illustrate the roles played by the nonlinear and non-stationary effects in the energy-time-frequency distribution. At the same time, the method can also be expanded and applied in other fields.

Sign in / Sign up

Export Citation Format

Share Document