Fast ICA for Blind Source Separation and its Implementation

Abstract

Independent Component Analysis (ICA) is a statistical signal processing technique having emerging new practical application areas, such as blind signal separation such as mixed voices or images, analysis of several types of data or feature extraction. Fast independent component analysis (Fast ICA ) is one of the most efficient ICA technique. Fast ICA algorithm separates the independent sources from their mixtures by measuring non-gaussian. Fast ICA is a common method to identify aircrafts and interference from their mixtures such as electroencephalogram (EEG), magnetoencephalography (MEG), and electrocardiogram (ECG). Therefore, it is valuable to implement Fast ICA for real-time signal processing. In this thesis, the Fast ICA algorithm is implemented by hand coding HDL code. In addition, in order to increase the number of precision, the floating point (FP) arithmetic units are also implemented by HDL coding.To verify the algorithm, MATLAB simulations are also performed for both off line signal rocessing and real-time signal processing.