Model selection using limiting distributions of second-order blind source separation algorithms

Model selection using limiting distributions of second-order blind source separation algorithms

Signals, recorded over time, are often observed as mixtures of multiple source signals. To extract relevant information from such measurements one needs to determine the mixing coefficients. In case of weakly stationary time series with uncorrelated source signals, this separation can be achieved by jointly diagonalizing sample autocovariances at different lags, and several algorithms address t...

Blind source-separation using second-order cyclostationary statistics

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 funct...

A blind source separation technique using second-order statistics

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 sourc...

Blind source separation using order statistics

This paper shows the possibility to blindly separate instantaneous mixtures of sources by means of a criteria exploiting order statistics. Properties of higher order statistics and second order methods are first underlined. Then a brief description of the order statistics shows that they gather all these properties and a new criteria is proposed. Next an iterative algorithm able to simultaneous...

Novel blind source separation algorithms using cumulants