Adaptive blind separation of convolutive mixtures of independent linear signals
JITENDRA K. TUGNAIT · 2002
This paper is concerned with the problem of blind separation of independent signals (sources) from their linear convolutive mixtures. The various signals are assumed to be linear non-Gaussian but not necessarily i.i.d. An iterative, normalized higher-order cumulant maximization based approach was developed previously using the fourth-order normalized cumulants of the: "beamformed" data. A byproduct of this approach is a decomposition of the given data, at each sensor into its independent signal components. In this paper an adaptive implementation of the above approach is developed using a stochastic gradient approach. Some further enhancements including a Wiener filter implementation for signal separation and adaptive filter reinitialization are also provided. A computer simulation example is presented.