Research On The Algorithm Of Blind Signal Separation Based On Independent Component Analysis

Blind signal separation (BSS) is a new research field recently rising and as a an effective method for the separation of blind signals, Independent Component Analysis(ICA) has attracted broad attention and becomes more and more important while using in wide fields. Under the condition without knowing the source signals and the mixing matrix, independent component analysis can solve the problem of blind signal separation soundly with the simple assumption that the sources are mutual independent. The work of this thesis is extended from independent component analysis algorithm.In this paper, after a brief introduction to the development history and current research status and applications of ICA, simple mathematical preliminaries of ICA technique was given, including the assumptions made about ICA problems, mathematical definition, the mathematical theory and methods commonly used in ICA, etc. Among the prime process of independent component analysis, centering and whitening was studied. At the same time, images simulations showed the effectiveness of the whitening theory.The basal theory and method of ICA algorithm and its fast algorithm (FastICA) was introduced. I-FastICA was advanced based analyzing kernel iterate course of the FastICA algorithm. I-FastICA improved convergence performance and reduced iterations. Aimed at the convergent speed of I-FastICA was dependent on initial weights, LI-FastICA was advanced by imported loosen factor, and reduced the dependence on initial weights. A modified fast ICA algorithm based on four-order cumulant was studied. Using the four-order cumulant as criterion, we analysised the traditional fast ICA algorithm; then proposed a new modified fast ICA algorithm which replaceed the iteration steps of fast ICA algorithm by improved Newton iteration method. Compared with two kinds of algorithm’s results on wave signals and imagin signals simulations, dates showed the validity of the modified algorithm.At the end of this paper, we give conclusions and introduce our future works.