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Open Access Publications from the University of California

Blind source separation using second-order cyclostationary statistics

  • Author(s): Abed-Meraim, Karim
  • Xiang, Yong
  • Manton, Jonathan H
  • Hua, Yingbo
  • et al.

This paper studies the blind source separation

(BSS) problem with the assumption that the source signals are

cyclostationary. Identifiability and separability criteria based on

second-order cyclostationary statistics (SOCS) alone are derived.

The identifiability condition is used to define an appropriate

contrast function. An iterative algorithm (ATH2) is derived to

minimize this contrast function. This algorithm separates the

sources even when they do not have distinct cycle frequencies.

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