An improved FastICA algorithm and its application
一种改进的FastICA算法及其应用

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.