scholarly journals Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency-Hopping Signals Based on Tensor Decomposition Method

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 69407-69414 ◽  
Author(s):  
Chaozhu Zhang ◽  
Yu Wang ◽  
Fulong Jing
Keyword(s):  
Blind Source Separation ◽  
Decomposition Method ◽  
Source Separation ◽  
Tensor Decomposition ◽  
Frequency Hopping ◽  
Underdetermined Blind Source Separation

scholarly journals Underdetermined Blind Source Separation Combining Tensor Decomposition and Nonnegative Matrix Factorization

Symmetry ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 521 ◽  
Author(s):  
Yuan Xie ◽  
Kan Xie ◽  
Junjie Yang ◽  
Shengli Xie
Keyword(s):  
Blind Source Separation ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Source Separation ◽  
Nonnegative Matrix ◽  
Tensor Decomposition ◽  
Model Parameters ◽  
Separation Performance ◽  
Underdetermined Blind Source Separation ◽  
Source Signals

Underdetermined blind source separation (UBSS) is a hot topic in signal processing, which aims at recovering the source signals from a number of observed mixtures without knowing the mixing system. Recently, expectation-maximization algorithm shows a great potential in the UBSS. However, the final separation results depend strongly on the parameter initialization, leading to poor separation performance. In this paper, we propose an effective algorithm that combines tensor decomposition and nonnegative matrix factorization (NMF). In the proposed algorithm, we first employ tensor decomposition to estimate the mixing matrix, and NMF source model is used to estimate the source spectrogram factors. Then a series of iterations are derived to update the model parameters. At the same time, the spatial images of source signals are estimated with Wiener filters constructed from the learned parameters. Therefore, time-domain sources can be obtained through inverse short-time Fourier transform. Finally, plenty of experimental results demonstrate the effectiveness and advantages of our proposed algorithm over the compared algorithms.

Frequency-hopping signals sorting based on underdetermined blind source separation

2013 ◽  
Vol 7 (14) ◽  
pp. 1456-1464 ◽  
Author(s):  
Zhi-Chao Sha ◽  
Yi-Yu Zhou ◽  
Feng-Hua Wang ◽  
Zhi-Tao Huang
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Frequency Hopping ◽  
Underdetermined Blind Source Separation

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.

Sign in / Sign up

Export Citation Format

Share Document