Study On Underdetermined Blind Signal Separation Algorithm Based On Sparse Representation

Blind Signal Separation (BSS) is a fundamental and challenging research topic in signal processing field. Because of the promising applications in speech recognition, image processing, sonar problem, medical and biological data analysis (EEG,MEG,ECG), data mining and wireless communication et al, BSS has become one of the hottest spots in signal processing field and neural network field. After nearly twenty years, the theories and algorithms about BSS have got great developments. Many effective algorithms have been presented, and their performances are different from the ability to separate source signals, time-consuming et al. As the development and the wider applications of BSS, the researchers have known the shortage and limit of BSS algorithms. Recently, some researchers give their attention to research the ill-posed or underdetermined (less observations than sources, i.e m