An image processing approach for underdetermined blind separation of nonstationary sources

Abed-Meraim, K., Linh-Trung, N., Sucic, V., Tupin, F. and Boashash, B. (2004). An image processing approach for underdetermined blind separation of nonstationary sources. In: ISPA 2003: Proceedings of the 3nd International Symposium on Image and Signal Processing and Analysis. 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003., Rome, Italy, (347-352). 18-20 September 2003. doi:10.1109/ISPA.2003.1296856


Author Abed-Meraim, K.
Linh-Trung, N.
Sucic, V.
Tupin, F.
Boashash, B.
Title of paper An image processing approach for underdetermined blind separation of nonstationary sources
Conference name 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003.
Conference location Rome, Italy
Conference dates 18-20 September 2003
Proceedings title ISPA 2003: Proceedings of the 3nd International Symposium on Image and Signal Processing and Analysis
Journal name Ispa 2003: Proceedings of the 3rd International Symposium On Image and Signal Processing and Analysis, Pts 1 and 2
Place of Publication Piscataway NJ, USA
Publisher IEEE
Publication Year 2004
Sub-type Fully published paper
DOI 10.1109/ISPA.2003.1296856
ISBN 953-184-061-X
ISSN 1330-1012
Volume 1
Start page 347
End page 352
Total pages 7
Language eng
Abstract/Summary This paper presents a new approach for blind separation of nonstationary frequency-modulated (FM) sources in the underdetermined case (i.e., more sources than sensors) using their time-frequency distributions (TFDs). The underlying idea of the proposed blind source separation (BSS) method is based on the observation that a monocomponent FM signal is represented by a linear feature corresponding to the 'energy concentration points' in the time-frequency (TF) image. Therefore, we propose to adapt an existing 'road network extraction' method [Tupin et al., (1998)] for the detection and separation of the source signal components from the spatially averaged TF image of their mixtures. The sources spatial signatures are then used to group together (classify) the components of the same source (or equivalently, the same spatial direction). Simulation examples are provided to assess the performance of the proposed algorithm in various scenarios.
Subjects 1005 Communications Technologies
Keyword blind source separation
feature extraction
frequency modulation
Q-Index Code E1

 
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Created: Wed, 10 Mar 2010, 17:24:49 EST by Maria Campbell on behalf of Faculty Of Health Sciences