独立分量分析是信号处理技术的新发展,它作为盲信号分离的一种有效的方法而受到广泛的关注,并在许多方面获得成功应用.讨论了独立分量分析的基本原理、判断条件和算法,并在此基础上,介绍了独立分量分析的一种快速算法——FastICA算法;对FastICA算法的核心迭代过程进行改进,得到M-FastICA算法,改进算法减少了独立分量分析的迭代次数,从而提高了算法的收敛速度.最后将M-FastICA算法应用到图象的分离上,实验结果表明,改进算法在分离效果相当的前提下,串行算法迭代次数减少了9％,并行算法迭代次数减少了27％,收敛速度更快.

Independent Component Analysis (ICA) is a new development of signal processing. As an effective approach to the separation of blind signal, Independent Component Analysis has attracted broad attention and has been successfully used in many fields. The fundamental, discrimination condition and practical algorithm of Independent Component Analysis are discussed. Then, a fast Independent Component Analysis algorithm (FastICA) is introduced, and it is known that the time-consuming course is computing Jacobian Matrix. Reducing the time of Jacobian Matrix will improve the performance of FastICA algorithm. So a modified FastICA (M-FastICA) algorithm is developed. By modifying kernel iterate course, several iterations of FastICA are merged into one iteration of M-FastICA, then M-FastICA algorithm only need to compute Jacobian Matrix once time and achieves the correspondent effect of FastICA. So the convergence of ICA will be accelerated. Finally, M-FastICA is applied to image separation. The experiment images are mixed with a random matrix. Independent Component Analysis can separate the mixed images and obtain the approximate of source images. The experiment results show that the iterations of serial modified algorithm reduces 9 percent, and the iterations of parallel modified algorithm reduces 27 percent with the correspondent separation performance.