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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。
