Title :
A Novel Adaptive Algorithm for Blind Signal Separation of Linear Mixture
Author :
Zhu Yi-yong ; Zhu Yong-gang ; Yao Fu-qiang ; Guan Sheng-yong
Author_Institution :
Inst. of Commun. Eng. of PLAUST, Nanjing
Abstract :
A novel adaptive algorithm for blind separation of the linearly mixed signals based on the relationship of the variability between the source and the mixed signals is presented, which utilizes the generalized eigen-subspace decomposition based on the recursive least square (RLS) parallel computation. The presented algorithm has less mean square error by avoiding the complexity of the estimation of the high-order statistics of the source signal, and it has faster speed of convergence. Simulation results illustrate the efficiency of the algorithm.
Keywords :
adaptive signal processing; blind source separation; eigenvalues and eigenfunctions; higher order statistics; least mean squares methods; adaptive algorithm; blind signal separation; eigen-subspace decomposition; less mean square error; linearly mixed signal; recursive least square parallel computation; signal high-order statistics; Adaptive algorithm; Algorithm design and analysis; Biomedical signal processing; Blind source separation; Convergence; Independent component analysis; Least squares methods; Multi-layer neural network; Signal processing algorithms; Source separation;
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2007. WiCom 2007. International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-1311-9
DOI :
10.1109/WICOM.2007.263
