Blind Separability of Reverberation and Target Echo Based on Spatial Correlation

2011 ◽  
Vol 128-129 ◽  
pp. 538-543
Author(s):  
Xiu Kun Li ◽  
Qi Yong Wang
Keyword(s):  
Signal Processing ◽  
Spatial Correlation ◽  
Blind Source Separation ◽  
Time Domain ◽  
Source Separation ◽  
Sea Bottom ◽  
Transmitted Signal ◽  
Signal Processing Methods ◽  
Nonstationary Source ◽  
Different Sources

The detection of buried targets in shallow water is a tough task in the presence of sea-bottom reverberation. Because both target echo and reverberation are caused by the transmitted signal, they are mixed together in both time domain and frequency domain, which makes traditional signal processing methods inefficient. Blind Source Separation (BSS) is expected to isolate the reverberation from the target echo. However, the feasibility should be proved before separation. In this paper, a method based on spatial correlation is proposed to determine whether reverberation and target echo can be separated as different sources. Then, considering the nonstationarity of the reverberation, SONS (Second Order Nonstationary Source Separation) is applied to separate the original received signals. The sea experiment result shows that BSS is not only feasible but also valid to separate target echo and reverberation, and the target echo after BSS is of higher SRR which makes further process more credible.

Applications to Signal Processing - Blind Source Separation

2012 ◽  
pp. 119-138
Author(s):  
Hime Aguiar e Oliveira Junior ◽  
Lester Ingber ◽  
Antonio Petraglia ◽  
Mariane Rembold Petraglia ◽  
Maria Augusta Soares Machado
Keyword(s):  
Signal Processing ◽  
Blind Source Separation ◽  
Source Separation

Convolutive Blind Source Separation Based on Wavelet De-Noising

2013 ◽  
Vol 756-759 ◽  
pp. 3356-3361 ◽  
Author(s):  
Hong Bin Zhang ◽  
Peng Fei Xu
Keyword(s):  
Blind Source Separation ◽  
Time Domain ◽  
Source Separation ◽  
Noise Pollution ◽  
Processing Method ◽  
Separation System ◽  
Communication Signals ◽  
The Time Domain ◽  
Ica Algorithm ◽  
Convolutive Blind Source Separation

The paper discusses the time-domain blind seperation applied to communication signals, using an ICA algorithm EFICA together with a wavelet de-noising processing method. In the Blind source separation system, regardless of the mixed signals and separated signals, noise pollution occurs frequently, it increases the complexity of BSS and the difficulty of dealing with the aftermath. So an automatic method of and wavelet de-noising processing is proposed finally. It yields good results in the experiment and improves the performance of BSS system.

Robust Blind Source Separation by Beta Divergence

2002 ◽  
Vol 14 (8) ◽  
pp. 1859-1886 ◽  
Author(s):  
Minami Mihoko ◽  
Shinto Eguchi
Keyword(s):  
Signal Processing ◽  
Data Analysis ◽  
Blind Source Separation ◽  
Financial Market ◽  
Source Separation ◽  
Robust Method ◽  
Communication Engineering ◽  
Biological Signal Processing ◽  
Simulation Results ◽  
Linear Mixtures

Blind source separation is aimed at recovering original independent signals when their linear mixtures are observed. Various methods for estimating a recovering matrix have been proposed and applied to data in many fields, such as biological signal processing, communication engineering, and financial market data analysis. One problem these methods have is that they are often too sensitive to outliers, and the existence of a few outliers might change the estimate drastically. In this article, we propose a robust method of blind source separation based on theβ divergence. Shift parameters are explicitly included in our model instead of the conventional way which assumes that original signals have zero mean. The estimator gives smaller weights to possible outliers so that their influence on the estimate is weakened. Simulation results show that the proposed estimator significantly improves the performance over the existing methods when outliers exist; it keeps equal performance otherwise.

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