A method for ICA for complex-valued sources
LI Xiao-jun · Journal of Xidian University · 2005
ICA is used to get the complex-valued signals. ICA is a statistical method for transforming an observed random vector into components that are as mutually independent as possible. By changing the extended Hebbian learning and directly using the nonline function, blind separation of complex-valued signals will be obtained. By using this method, the algorithm based on the gradient steepest ascent is proposed, with its computational efficiency shown by simulations.