A decentralized blind source separation algorithm for ambient modal identification in the presence of narrowband disturbances

2013 ◽  
Vol 21 (3) ◽  
pp. 282-302 ◽  
Author(s):  
A. Sadhu ◽  
S. Narasimhan
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Separation Algorithm

Extraction of fetal ECG a semi-blind source separation algorithm combined with parametrized kalman filter

2021 ◽  
Author(s):  
J. Sakubar Sadiq ◽  
G. Arunmani ◽  
P. Ravivarma ◽  
N. Karthika Devi ◽  
A. Hemalatha ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Blind Source Separation ◽  
Source Separation ◽  
Separation Algorithm ◽  
Fetal Ecg

scholarly journals An Underdetermined Blind Source Separation Method with Application to Modal Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Gang Yu
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Modal Identification ◽  
Modal Parameters ◽  
Underdetermined Blind Source Separation ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Modal Response ◽  
Experimental Validations ◽  
Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

Blind Complex Source Separation Based on Cyclostationary Statistics

2014 ◽  
Vol 519-520 ◽  
pp. 1051-1056
Author(s):  
Jie Guo ◽  
An Quan Wei ◽  
Lei Tang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Performance ◽  
Separation Algorithm ◽  
Complex Signals ◽  
Cyclic Frequency ◽  
Complex Source ◽  
Simulation Results ◽  
Better Than ◽  
Separation Model

This paper analyzed a blind source separation algorithm based on cyclic frequency of complex signals. Under the blind source separation model, we firstly gave several useful assumptions. Then we discussed the derivation of the BSS algorithm, including the complex signals and the normalization situation. Later, we analyzed the complex WCW-CS algorithm, which was compared with NGA, NEASI and NGA-CS algorithms. Simulation results show that the complex WCW-CS algorithm has the best convergence and separation performance. It can also effectively separate mixed image signals, whose performance was better than NGA algorithm.

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