A Study On Blind Signal Separation And Its Application

Blind signal separation is a new technique of signal processing aimed at retrieving a system's unknown information from its output only, which can be used in array processing and data analysis. There are many potential application, especially in wireless communication, medical signal processing, image processing and radar signal processing. In recent years, blind signal separation has been an attractive trend in the area of academia. In this thesis, the theory and applications of blind source separation has been studied. The thesis consists of following parts:1. The paper reviews systematically the present research situation of blind source separation in the world. The basic principles and concepts of blind source separation and some algorithms are introduced.2. A minimum mutual information algorithm based recurrent neural networks are given. The main principle, derivation has been presented. Computation simulation experiments are also given to illustrate the performances of the algorithm.3. The traffic sign recognition and digital watermarking based blind source separation technique has been demonstrated on this paper. It indicated that blind source separation have a good result on the realization of this recognition system and its application to digital water marking. The experiment on research of digital watermarking based blind source separation also validated this method's application on the signal-process domain.