A Novel Floating Point Fast Confluence Adaptive Independent Component Analysis for Signal Processing Applications

摘要

Independent component analysis (ICA) is a technique that separates the independent source signals from their mixtures by minimizing the statistical dependence between components. This paper presents a floating point implementation of a novel fast confluence adaptive independent component analysis (FCAICA) technique with reduced number of iterations that provides the high convergence speed. Fixed point ICA algorithms cover only smaller range of numbers. To handle large as well as tiny numbers and hence to improve the dynamic range of the signal values, floating point operations are performed in ICA. The high convergence speed is achieved by a novel optimization scheme that adaptively changes the weight vector based on the kurtosis value. To validate the performance of the proposed FCAICA, simulation and synthesis are performed with super-gaussian mixtures and sub Gaussian mixtures and experimental results provided. The proposed FCAICA processor separates the super-Gaussian signals with a maximum operating frequency of 2.91MHz with improved convergence speed.