Journal of Computer Applications

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An improved FastICA algorithm and its application

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  • Received:2007-10-31 Revised:2007-12-11 Online:2008-04-01 Published:2008-04-01
  • Contact: Wu GU

一种改进的FastICA算法及其应用

郭武 朱长仁 王润生   

  1. 国防科技大学 国防科技大学
  • 通讯作者: 郭武

Abstract: Independent Component Analysis (ICA) is a signal analysis method based on high order cumulants of signals and it can find out the latent independent components in data. Recently ICA has been widely used in many fields such as speech recognition, image processing, telecommunication system etc. The FastICA is the most popular algorithm for ICA at present, and it uses Newton rule to optimize the objective function. This algorithm can converge speedily but is not robust to initialization. In order to overcom the drawbacks, one dimension search was imposed on the direction of Newton iterative. The improved algorithm can ensure the convergence of the results and is robust to initialization. When the improved algorithm is used to detect the moving target, the experimental results show that it is a robust method.

Key words: Independent Component Analysis (ICA), FastICA, detection of moving target

摘要: 独立分量分析是基于信号高阶统计量的信号分析方法,它可以找到隐含在数据中的独立分量,已经广泛应用到语音信号处理、图像处理及信息通信等方面。目前应用较多的快速独立分量分析(FastICA)利用了牛顿迭代法原理,具有较快的收敛速度,但对初始值的选择比较敏感。为克服其缺点,改进其优化学习算法,在牛顿迭代方向增加一维搜索,使改进后的算法的收敛性不依赖于初始值的选择。将改进的FastICA算法应用到运动目标检测中,取得稳定性较强的结果。

关键词: 独立分量分析, 快速独立分量分析, 运动目标检测