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Underdetermined blind separation by combining sparsity and independence of sources

Chen, Peng, Peng, Dezhong, Zhen, Liangli, Luo, Yifan and Xiang, Yong 2017, Underdetermined blind separation by combining sparsity and independence of sources, IEEE access, vol. 5, pp. 21731-21742, doi: 10.1109/ACCESS.2017.2764044.

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Title Underdetermined blind separation by combining sparsity and independence of sources
Author(s) Chen, Peng
Peng, Dezhong
Zhen, Liangli
Luo, Yifan
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Journal name IEEE access
Volume number 5
Start page 21731
End page 21742
Total pages 12
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2017-10-24
ISSN 2169-3536
Keyword(s) underdetermined blind source separation
sparsity
independence
source recovery
blind identification
science & technology
technology
computer science, information systems
engineering, electrical & electronic
telecommunications
computer science
engineering
Summary In this paper, we address underdetermined blind separation of N sources from their M instantaneous mixtures, where N>M , by combining the sparsity and independence of sources. First, we propose an effective scheme to search some sample segments with the local sparsity, which means that in these sample segments, only Q(Q < M) sources are active. By grouping these sample segments into different sets such that each set has the same Q active sources, the original underdetermined BSS problem can be transformed into a series of locally overdetermined BSS problems. Thus, the blind channel identification task can be achieved by solving these overdetermined problems in each set by exploiting the independence of sources. In the second stage, we will achieve source recovery by exploiting a mild sparsity constraint, which is proven to be a sufficient and necessary condition to guarantee recovery of source signals. Compared with some sparsity-based UBSS approaches, this paper relaxes the sparsity restriction about sources to some extent by assuming that different source signals are mutually independent. At the same time, the proposed UBSS approach does not impose any richness constraint on sources. Theoretical analysis and simulation results illustrate the effectiveness of our approach.
Language eng
DOI 10.1109/ACCESS.2017.2764044
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2017, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30113468

Document type: Journal Article
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.