FPGA Implementation of Blind Source Separation using FastICA

Abstract

Fast Independent Component Analysis (FastICA) is a statistical method used to separate signals from an unknown mixture without any prior knowledge about the signals. This method has been used in many applications like the separation of fetal and maternal Electrocardiogram (ECG) for pregnant women. This thesis presents an implementation of a fixed-point FastICA in field programmable gate array (FPGA). The proposed design can separate up to four signals using four sensors. QR decomposition is used to improve the speed of evaluation of the eigenvalues and eigenvectors of the covariance matrix. Moreover, a symmetric orthogonalization of the unit estimation algorithm is implemented using an iterative technique to speed up the search algorithm for higher order data input. The hardware is implemented using Xilinx virtex5-XC5VLX50t chip. The proposed design can process 128 samples for the four sensors in less than 63 ns when the design is simulated using 10 MHz clock