极值斜率经验模式分解结合独立分量分析的单通道盲分离
Single channel blind separation based on extremum slope empirical mode decomposition and ICA
张纯 1杨俊安 1叶丰2
作者信息
- 1. 解放军电子工程学院信息工程系,合肥230037;安徽省电子制约技术重点实验室,合肥230037
- 2. 解放军电子工程学院信息工程系,合肥230037
- 折叠
摘要
针对单通道盲分离领域先验信息不足的问题,提出了一种基于极值斜率经验模式分解和独立分量分析的单通道盲分离算法.首先通过极值斜率经验模式分解算法以不同尺度逐次分解混合信号的波动和趋势,得到一组固有模态函数.然后将其作为独立分量分析算法的输入信号,从得到的独立分量中萃取出与源信号相似的独立分量,通过重构算法恢复源信号,达到分离目的.实验信号采取仿真信号和实际信号,实验结果表明,该算法不需任何先验信息,鲁棒性强,能较快地得到良好的分离效果.
Abstract
Aimed at the problem of prior information deficiency in single channel blind separation field,a novel single channel blind separation algorithm based on extremum slope empirical mode decomposition and ICA is presented.Extremum slope empirical mode decomposition algorithm is firstly used to decompose mixed signal's fluctuation and trend of different scales step by step and generates a series of data sequence which called intrinsic mode function.And then these intrinsic mode functions are regarded as input signals of ICA algorithm.Finally,independent components which are similar to source signals are extracted and source signals are recovered.Validation of the proposed method is performed with extensive experiments on toy and real-life datasets respectively.Experiment results show that the proposed algorithm does not require any prior information and has strong robustness.Meanwhile it can reach favorable separation performance in a fast speed.
关键词
单通道/盲分离/经验模式分解/固有模态函数/独立分量分析Key words
single channel/blind separation/empirical mode decomposition (EMD)/intrinsic mode function (IMF)/independent component analysis (ICA)引用本文复制引用
出版年
2013
NETL
NSTL
维普
万方数据