Blind separation of coherent multipath signals with impulsive interference and Gaussian noise in time-frequency domain

2021 ◽  
Vol 178 ◽  
pp. 107750
Author(s):  
Yiming Xiao ◽  
Wenzhen Lu ◽  
Qinmengying Yan ◽  
Haijian Zhang
Keyword(s):  
Frequency Domain ◽  
Gaussian Noise ◽  
Blind Separation ◽  
Time Frequency ◽  
Impulsive Interference ◽  
Multipath Signals

Blind Source Separation Based on Wavelet and Cross-Wavelet

2011 ◽  
Vol 328-330 ◽  
pp. 2064-2068 ◽  
Author(s):  
Jing Hui Wang ◽  
Yuan Chao Zhao
Keyword(s):  
Frequency Domain ◽  
Blind Source Separation ◽  
Time Scale ◽  
Source Separation ◽  
Blind Separation ◽  
Time Frequency ◽  
Non Stationary Signal ◽  
Cross Wavelet

In this paper, a novel blind separation approach using wavelet and cross-wavelet is presented. This method extends the separate technology from time-frequency domain to time-scale domain. The simulation showed that this method is suitable for dealing with non-stationary signal.

scholarly journals Underdetermined Blind Separation of Nondisjoint Sources in the Time-Frequency Domain

2007 ◽  
Vol 55 (3) ◽  
pp. 897-907 ◽  
Author(s):  
Abdeldjalil Aissa-El-Bey ◽  
Nguyen Linh-Trung ◽  
Karim Abed-Meraim ◽  
Adel Belouchrani ◽  
Yves Grenier
Keyword(s):  
Frequency Domain ◽  
Blind Separation ◽  
Time Frequency

scholarly journals Blind separation of complex-valued satellite-AIS data for marine surveillance: a spatial quadratic time-frequency domain approach

2019 ◽  
Vol 9 (3) ◽  
pp. 1732
Author(s):  
Omar Cherrak ◽  
Hicham Ghennioui ◽  
Nadege Thirion Moreau ◽  
El Hossein Abarkan
Keyword(s):  
Frequency Domain ◽  
Block Diagonalization ◽  
Blind Separation ◽  
Time Frequency ◽  
High Magnitude ◽  
Frequency Domain Methods ◽  
Stationary Signals ◽  
Mixing Matrix ◽  
Frequency Domain Approach ◽  
Complex Valued

<p>In this paper, the problem of the blind separation of complex-valued Satellite-AIS data for marine surveillance is addressed. Due to the specific properties of the sources under consideration: they are cyclo-stationary signals with two close cyclic frequencies, we opt for spatial quadratic time-frequency domain methods. The use of an additional diversity, the time delay, is aimed at making it possible to undo the mixing of signals at the multi-sensor receiver. The suggested method involves three main stages. First, the spatial generalized mean Ambiguity function of the observations across the array is constructed. Second, in the Ambiguity plane, Delay-Doppler regions of high magnitude are determined and Delay-Doppler points of peaky values are selected. Third, the mixing matrix is estimated from these Delay-Doppler regions using our proposed non-unitary joint zero-(block) diagonalization algorithms as to perform separation.</p>

Blind Separation of Non-Stationary Convoluted Mixtures Based on Time-Frequency Analysis

2012 ◽  
Vol 538-541 ◽  
pp. 2571-2575
Author(s):  
Peng Wang ◽  
Ji Hua Cao ◽  
Xiao Chang Ni
Keyword(s):  
Frequency Domain ◽  
Frequency Analysis ◽  
Time Varying ◽  
Blind Separation ◽  
Frequency Method ◽  
Distribution Method ◽  
Time Frequency Analysis ◽  
Joint Diagonalization ◽  
Time Frequency ◽  
Analysis Methods

The signals of convoluted mixtures have a stated of non-stationary identity, and the change of their spectrum with time-varying usually could not be observed from the frequency domain, but they can be observed by the time-frequency method. Therefore, the blind separation of non-stationary convoluted mixtures based on time-frequency analysis is proposed in this paper. For the non-stationary identity, the space-albinism of the mixed matrices and the joint diagonalization of the time-frequency matrices are simulated to separate the convoluted mixtures. Two kinds of time-frequency analysis methods, Wigner-Ville distribution and improved Wigner-Ville distribution, are introduced, which are calculated with MATLAB 7.0 software. The simulated results show the improved Wigner-Ville distribution method has a better performance for blind separating of non-stationary convoluted and mixed signals.

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