Cascade neural networks for multichannel blind deconvolution

Abstract

The authors present a new simple but efficient and powerful extension of Bussgang-type blind equalisation algorithms which can extract multiple source signals from their unknown convolutive mixtures. A cascade neural network is proposed, where each module consists of an equalisation subnetwork and a deflation subnetwork. This approach can adopt any blind equalisation algorithm (which has been developed for the equalisation of a single channel). It can also be applied when the number of source signals is not known in advance. Extensive computer simulation results confirm the validity and high efficiency of the proposed method.