杨维娜, 裴以建, 蔡光卉, 肖敏. 基于信息论的盲源信号分离[J]. 云南大学学报(自然科学版), 2008, 30(5): 460-464,471.
引用本文: 杨维娜, 裴以建, 蔡光卉, 肖敏. 基于信息论的盲源信号分离[J]. 云南大学学报(自然科学版), 2008, 30(5): 460-464,471.
YANG Wei-na, PEI Yi-jian, CAI Guang-hui, XIAO Min. Blind source separation based on information theory[J]. Journal of Yunnan University: Natural Sciences Edition, 2008, 30(5): 460-464,471.
Citation: YANG Wei-na, PEI Yi-jian, CAI Guang-hui, XIAO Min. Blind source separation based on information theory[J]. Journal of Yunnan University: Natural Sciences Edition, 2008, 30(5): 460-464,471.

基于信息论的盲源信号分离

Blind source separation based on information theory

  • 摘要: 从混合观测数据向量中恢复出不可直接观测的各个源信号是阵列处理和数据分析的典型问题.独立分量分析是解决这一类问题的新技术,而基于信息论方法的分离技术是独立分量算法中最常用的分离算法.基于信息论算法中主流的FastICA算法和自然梯度优化算法,使用几组不同的信号进行分离,从理论分析和仿真结果表明了FastICA算法的优越性.

     

    Abstract: Recovering the unobserved source signals from their mixtures is a typical problem in array processing and analysis.Independent component analysis(ICA) is a new method to solve this problem.The most common way in independent component analysis is the separation based on information theory.FastICA algorithm and nature step algorithm are the main way in it.Some groups of signals were separated.The analysis and simulations suggest that the FastICA algorithm is the best way.

     

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