A blind source separation technique using second-order statistics

TLDR

Separation of sources requires recovering signals from instantaneous linear mixtures, often without prior knowledge of the mixing matrix, a situation common in narrowband array processing where the array manifold is unknown or distorted. This paper introduces a new source‑separation technique that exploits the time coherence of source signals. The approach relies solely on stationary second‑order statistics, employing joint diagonalization of covariance matrices rather than higher‑order statistics. Asymptotic performance analysis and numerical simulations confirm the effectiveness of the proposed method.

Abstract

Separation of sources consists of recovering a set of signals of which only instantaneous linear mixtures are observed. In many situations, no a priori information on the mixing matrix is available: The linear mixture should be "blindly" processed. This typically occurs in narrowband array processing applications when the array manifold is unknown or distorted. This paper introduces a new source separation technique exploiting the time coherence of the source signals. In contrast with other previously reported techniques, the proposed approach relies only on stationary second-order statistics that are based on a joint diagonalization of a set of covariance matrices. Asymptotic performance analysis of this method is carried out; some numerical simulations are provided to illustrate the effectiveness of the proposed method.