• DocumentCode
    80715
  • Title

    Novel Z-Domain Precoding Method for Blind Separation of Spatially Correlated Signals

  • Author

    Yong Xiang ; Dezhong Peng ; Yang Xiang ; Song Guo

  • Author_Institution
    Sch. of Inf. Technol., Deakin Univ., Melbourne, VIC, Australia
  • Volume
    24
  • Issue
    1
  • fYear
    2013
  • fDate
    Jan. 2013
  • Firstpage
    94
  • Lastpage
    105
  • Abstract
    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.
  • Keywords
    blind source separation; correlation methods; filtering theory; precoding; closed-form algorithm; coded signal separation; coded signal transmission; data transmission delay reduction; distributed wireless sensor networks; source signal filtering; spatially correlated signal blind separation; transmitters; wireless surveillance systems; z-domain precoding method; Matrix decomposition; Relays; Sensors; Source separation; Vectors; Wireless communication; Wireless sensor networks; Blind source separation; Z-domain precoding; correlated sources; second-order statistics;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2012.2224671
  • Filename
    6365334