Underdetermined Blind Source Separation Based on Time-frequency Method Using Cyclostationary Characteristic

doi:10.3969/j.issn.1000-1093.2015.04.019

Abstract

Abstract: Quadratic time-frequency distribution (TFD) is an effective method to solve the underdetermined blind source separation problems. In the proposed method, the cyclic spectrum density (CSD) is calculated using the piecewise average periodogram method, which is used to reconstruct the Wigner-Ville distribution (WVD). The auto-term TF points are detected after computing the matrixes of TFDs, and a new three-order tensor is folded by the chosen TFD matrixes. At last, PARAFAC decomposition is applied to separate the sources directly, which does not assume that the number of active sources at any TF point is not larger than the sensor number. Simulation results demonstrate that the proposed method can suppress the noise effectively and separate the sources directly with only one step, avoiding the superposition of error of “two-step” methods, which improves the performance and efficiency of separation.

Key words: information processing technology, underdetermined blind source separation, cyclostation, quadratic time-frequency distribution, Wigner-Ville distribution, PARAFAC decomposition

ZHANG Liang-jun, YANG Jie, LU Kai-wang, SUN Ya-dong. Underdetermined Blind Source Separation Based on Time-frequency Method Using Cyclostationary Characteristic[J]. Acta Armamentarii, 2015, 36(4): 703-709.

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