基于滑动窗口的独立分量分析算法  

Independent Component Analysis Based on Sliding Window

作　　者：吴小培[1,2] 叶中付[3] 郭晓静[1] 张道信[1] 胡人君[1] 

机构地区：[1]安徽大学教育部计算智能与信号处理重点实验室 [2]中国科学技术大学信号统计处理研究室合肥230027 [3]中国科学技术大学信号统计处理研究室

出　　处：《计算机研究与发展》2007年第1期185-191,共7页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(60271024);安徽省人才基金项目(2004Z028);安徽大学模式识别创新团队基金项目

摘　　要：针对时变混合模型的独立分量分析(ICA)问题,提出了基于滑动窗口的ICA算法.给出了基于滑动窗的分离矩阵递归学习算法,提高了算法的运算效率,因此可应用于独立分量的在线提取和动态独立分量分析等应用场合.另外,针对独立分量排序不确定性所带来的问题,提出了利用峭度值大小对输出信号进行动态排序的思路.仿真实验证明了这一思路是可行的.对窗函数长度的选择问题还进行了探讨,得出了一些有参考价值的结论.实验结果表明,基于滑动窗ICA算法能较好地应用于时变混合模型的独立分量提取,具有良好的盲分离性能.Independent component analysis （ICA） has become a hotspot in signal processing area, and its computation algorithms and application are widely studied recently. In this paper, a sliding window ICA algorithm is studied to deal with time variant mixing model that the traditional ICA algorithms fail to work. For solving the problem caused by the indeterminacy of ICs in applying sliding window ICA algorithm, the value of kurtosis is employed as the index to sort the independent components at each window position. This idea is proved to be effective in most cases. In addition, a recursive learning rule of separation matrix based on sliding window is given, which can reduce the computation load of the algorithm obviously. The selection of window length is also discussed in this paper. Furthermore, the separation performance of the proposed algorithm are compared with the batch ICA algorithm. Experiment results show that the proposed algorithm can work well in the time variant mixing model and can be used for online blind source separation and dynamic independent component analysis.

关 键 词：盲源分离 独立分量分析 滑动窗口 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]