獨立分量分析( ) ICA作為有效的盲源分離技術( ) BSS是信號處理領域的熱點。實際信號或多或少的都含有噪聲，如果信噪比低于某值將得不到良好的分離效果。該文定義不同參量的高斯函數的期望為隨機向量的高斯矩，證明隨機向量的高斯矩可作為無偏估計的單值對照函數應用于帶高斯噪聲的模型。由此利用最大化基于高斯矩的對照函數，得到改進算法—noisICAFastICAyICA。
關鍵詞：多維信號處理，帶噪獨立分量分析，高斯矩
Research On Gaussian Moment for Noisy Independent Component Analysis HE weiwei , XIAO jun，Lou jiandong，Wang yingmin (Information Engineering College, Information Engineering University of PLA Zhengzhou 450002) Abstract：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.
Key words: Multidimensional signal processing, noisyICA，Gaussian moments