基于高斯矩的NoisyICA研究

Research On Gaussian Moment for Noisy Independent Component Analysis

独立分量分析(ICA)作为有效的盲源分离技术(BSS)是信号处理领域的热点。实际信号或多或少的都含有噪声,如果信噪比低于某值将得不到良好的分离效果。该文定义不同参量的高斯函数的期望为随机向量的高斯矩,证明随机向量的高斯矩可作为无偏估计的单值对照函数应用于带高斯噪声的ICA模型。由此利用最大化基于高斯矩的对照函数,得到FastICA改进算法—noisyICA。

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.