摘要
盲源分离(BSS)问题是在缺少先验知识的情况下,从接收到的观测信号中恢复统计独立的源信号。独立分量分析(ICA)方法把多维随机矢量转换为尽可能统计独立的分量,是现代解决盲源分离问题最主要的方法之一。本文给出了一种基于峰度的盲源分离算法,与用Comon的方法求解Givens矩阵相比,结构清晰、实现简单,而且几乎没有对源信号的概率密度函数做任何假设,可以对几乎所有概率密度的源信号进行分离,还借鉴了Comon的成对处理原则,把算法推广到了解决一般的盲源分离问题。仿真证明了该算法的有效性。
The Blind Source Separation (BSS) problem consists of recovery sources from the observed signals without adequate a prior knowledge.Independent Component Analysis(ICA)is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible, and it is one of the most important approaches to the BSS problem. An algorithm for BSS based on kurtosis is presented in this paper, which is simpler and easier to implement in solving Givens Matrix compared with Comon′s. As no other assumption of the Probability Density Function (PDF) is made, the algorithm can be used for virtually any PDF. It is also extended to the general BSS problems in light of Comon′s pair wise principle. The simulation justifies its effectiveness.
出处
《现代电子技术》
2005年第14期103-104,107,共3页
Modern Electronics Technique
