Abstract
This paper proposes a robust independent component analysis (ICA) approach for noise reduction. Noise reduction is a difficult problem in ICA model. In general signal processing applications, there are more than one interference signal which may have unknown characteristics. In these situations, traditional linear ICA may lead to poor results. Hence, noise reduction is preferred to be performed with nonlinear adaptive filtering. In this paper, a radial basis function network (RBFN) is employed to transform the observed signals into output space in a nonlinear manner. The weights of RBFN are updated by utilizing a modified fixed-point algorithm. The proposed method has not only the capacity of recovering the mixed signals, but also reducing noise with unknown characteristics from observed signals. The simulation results and analysis show that the proposed algorithm is suitable for practical unsupervised noise reduction problem.
