Study On Independent Component Analysis And Its Applications

Independent Component Analysis (ICA) is an approach to blind signal separation, which has been developed during the past twenty years. It is a statistical method, which aims to recover independent original signals from observed signals given by sensors which are linear mixtures of the independent original signals, and attempts to make the separated signals as independent as possible. It is widely useful in signal processing such as speech recognition system, telecommunications, and medical signal processing. At present, ICA is an issue in blind signal processing, artificial neural network etc.This paper briefly shows the development, applications and state of ICA, particularly treats basic principle and implementation of ICA, roundly introduces current several main ICA algorithms and the relation of them. Following the above, this paper probes into a fast fixed-point algorithm, and gives the results of the simulated experiment. At the same time, with the think and method of ICA, this paper put forward an approach utilizing FastICA to the problem that the parallel signals of two way automatic communication system is apt to interfere with each other in strong noise surroundings, designs a simulated experiment, verifies the validity of this approach. Chapter 5 describes the basic principle of CDMA communication system, expatiates upon the multiple access interfere and "near-far"effect's influence on CDMA communication system, analyzes the feasibility of use of FastICA in multiple users detective, and designs a simulated experiment. Finally, sums up this paper and discusses the further research issues and potential applications.