English

应用科学学报 ›› 2001, Vol. 19 ›› Issue (3): 210-213.

• 论文 • 上一篇    下一篇

线性混迭信号中独立源的盲提取

刘琚1, 聂开宝1, 何振亚2   

  1. 1. 山东大学电子工程系, 山东济南 250100;
    2. 东南大学无线电工程系, 江苏南京 210096
  • 收稿日期:2000-06-12 修回日期:2000-12-12 出版日期:2001-09-30 发布日期:2001-09-30
  • 作者简介:刘琚(1965-),男,山东临沂人,教授,博士;何振亚(1923-),男,江苏盐城人,教授,博导.
  • 基金资助:
    国家自然科学基金、山东省自然科学基金和山东大学青年科学基金重点基金资助项目

Blind Extraction of Independent Signals from Their Linear Mixtures

LIU Ju1, NIE Kai-bao1, HE Zhen-ya2   

  1. 1. Department of Electronic Engineering, Shandong Uuiversity, Jinan 250100, China;
    2. Department of Radio Engineering, Southeast University, Nanjing 210096, China
  • Received:2000-06-12 Revised:2000-12-12 Online:2001-09-30 Published:2001-09-30

PDF

90

被引次数

54

摘要： 基于源信号统计独立的假设,提出一种基于四阶累积量的分离判据,由此得出一种可以顺序逐个盲提取独立源信号的ICA算法,算法中利用去冗余技术剔除先前已经提取的信号.计算机仿真结果表明算法的性能良好.

关键词: 独立分量分析, 盲源分离, 高阶统计

Abstract: Observed signals are always the linear mixture of some independent components. Independent component analysis (ICA) is a novel technique for dealing with such a problem. Most of the existing algorithms separate individual independent sources simultaneously. In this paper, basing on the independence assumption of the original sources, we propose a new blind separating criterion, where the square of fourth-order cumulants of the sources are employed. We next develop an ICA approach which can sequentially extract independent components blindly one by one. A new deflation technique is used in this approach for removing the previously extracted signals from the mixture. Computer simulations show the validity of the proposed approach.

Key words: independent component analysis, blind source separation, higher order statistics

中图分类号: