Blind separation and filtering using state space models

Blind signal processing, especially independent component analysis (ICA) and multichannel blind deconvolution/equalization (MBD) problems have recently gained much interest due to many applications, especially in processing of biomedical signals (e.g. EEG, MEG, EMG, EOG, ECG), in wireless communications, `cocktail party´ problem, speech enhancement, geophysics, and source localization. In this paper both linear and nonlinear state space models for blind and semi-blind separation of linearly/nonlinearly mixed and filtered independent source signals are proposed. New unsupervised adaptive learning algorithms performing mutual independence of output signals are developed. For nonlinear mixture hyper radial basis function (HRBF), neural network proposed by Poggio and Girosi is employed and associated supervised-unsupervised learning rules for its parameters are developed