Novel Z-Domain precoding method for blind separation of spatially correlated signals

Xiang, Yong, Peng, Dezhong, Xiang, Yang and Guo, Song 2013, Novel Z-Domain precoding method for blind separation of spatially correlated signals, IEEE transactions on neural networks and learning systems, vol. 24, no. 1, pp. 94-105.

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Title Novel Z-Domain precoding method for blind separation of spatially correlated signals
Author(s) Xiang, Yong
Peng, Dezhong
Xiang, Yang
Guo, Song
Journal name IEEE transactions on neural networks and learning systems
Volume number 24
Issue number 1
Start page 94
End page 105
Total pages 12
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2013
ISSN 2162-237X
2162-2388
Keyword(s) blind source separation
correlated sources
second-order statistics
Z-domain precoding
Summary In this paper, we address the problem of blind separation of spatially correlated signals, which is encountered in some emerging applications, e.g., distributed wireless sensor networks and wireless surveillance systems. We preprocess the source signals in transmitters prior to transmission. Specifically, the source signals are first filtered by a set of properly designed precoders and then the coded signals are transmitted. On the receiving side, the Z-domain features of the precoders are exploited to separate the coded signals, from which the source signals are recovered. Based on the proposed precoders, a closed-form algorithm is derived to estimate the coded signals and the source signals. Unlike traditional blind source separation approaches, the proposed method does not require the source signals to be uncorrelated, sparse, or nonnegative. Compared with the existing precoder-based approach, the new method uses precoders with much lower order, which reduces the delay in data transmission and is easier to implement in practice.
Language eng
Field of Research 090609 Signal Processing
Socio Economic Objective 890104 Mobile Telephone Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30053656

Document type: Journal Article
Collection: School of Information Technology
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