Adaptive blind separation of convolutive mixtures of independent linear signals

This paper is concerned with the problem of blind separation of independent signals (sources) from their linear convolutive mixtures. The problem cons ists of recovering the sources up to shaping filters from the observations of MIMO system output. The various signals are assumed to be linear non-Gau ssian but not necessarily i.i.d. (independent and identically distributed). Recently an iterative, normalized higher-order cumulant maximization based approach was developed using the fourth-order normalized cumulants of the "beamformed" data. This approach was source-iterative, i.e., the sources we re extracted (at each sensor) and cancelled one by one, in the process yiel ding a decomposition of the given data at each sensor into its independent signal components. In this paper an adaptive implementation of the above ap proach is developed using a stochastic gradient approach. Some further enha ncements including a Wiener filter implementation for signal separation and adaptive filter reinitialization are also provided. Computer simulation ex amples are presented to illustrate the proposed approach. (C) 1999 Elsevier Science B.V. All rights reserved.