Research On Blind Source Separation Algorithms

In recent years, Blind Signal Processing has become one of the hottest areas in Signal Processing. The problem is relevant in various applications, especially in wireless communication, biomedical engineering, speech and image enhancement, speech and image recognition, feature extraction, etc. A large number of papers have been published on the problem of blind signal processing. The main contributions of this dissertation are:1.Some cross-cumulant-based algorithms can't recover sources from the mixtures of super-Gaussian, sub-Gaussian and Gaussian signals. In some neural network approaches, the signs of the kurtosises of sources are assumed to be known. Some blind signal separation algorithms estimate the probability density function of the sources, with which then calculate the score function. But the method is often costly in computation and suffers from instability. In this dissertation, we propose a simple method to adaptively estimate the score function. In this method, only one parameter needs to be estimated. The simulations show the stability and effectiveness of the method.2. The nonlinear sub-system of the post-linear blind source separation structure is modeled by a multilayer perceptron. It is well known that the natural gradient learning has ideal performances for on-line training of multilayer peceptrons. The natural gradient rather than conventional one gives the steepest descent direction of loss function in the parameter space of blind source separation. The conventional backpropagation method based on the ordinary gradient suffers from the plateaus which give rise to slow convergence. The natural gradient based algorithm gives better performance and faster convergence speed than the conventional algorithm.3.We derive a new method based on kernel to solve the post-linear blind signal separation problem. We first project the observed data into feature space, and then apply the blind source separation algorithms developed for linear mixture model for separating the signals in feature space. In addition to the generalized eigenvalue method, we give the decorrelation method for source separation in the feature space.4.Maximum likelihood scheme is widely used to learn the parameters in the latent variable model. It has some drawbacks such as overfitting and sensitivity to local optimization. In order to circumvent the drawbaks, we use ensemble learning to...