Research On Gaussian Moment for Noisy Independent Component Analysis
Pla Zhengzhou · Control & Automation · 2005
The Independent Component Analysis as a method widely used in blind source separation is a hotspot in signal processing. In fact, actual signals comprised noise more or less. And when the signal-to-noise rate is under some value, separated signals will be bad. This paper define the Gaussian function with different scale parameters, and show how the Gaussian moments of a random variable can be estimated from noisy observations. This enable us to use gaussian moments as one-unit contrast function that have asymptotic bias even in the presence of noise. To implement efficiently the maximization of the contrast functions based on Gaussian moments, a modification of our FastICA algorithom-noisyICA is introduced.