This letter investigates an improved blind source separation algorithm based on Maximum Entropy(ME) criteria. The original ME algorithm chooses the fixed exponential or sigmoid function as the nonlinear mapping functi...This letter investigates an improved blind source separation algorithm based on Maximum Entropy(ME) criteria. The original ME algorithm chooses the fixed exponential or sigmoid function as the nonlinear mapping function which can not match the original signal very well. A parameter es-timation method is employed in this letter to approach the probability of density function of any signal with parameter-steered generalized exponential function. An improved learning rule and a natural gradient update formula of unmixing matrix are also presented. The algorithm of this letter can separate the mixture of super-Gaussian signals and also the mixture of sub-Gaussian signals. The simulation experiment demonstrates the efficiency of the algorithm.展开更多
文摘This letter investigates an improved blind source separation algorithm based on Maximum Entropy(ME) criteria. The original ME algorithm chooses the fixed exponential or sigmoid function as the nonlinear mapping function which can not match the original signal very well. A parameter es-timation method is employed in this letter to approach the probability of density function of any signal with parameter-steered generalized exponential function. An improved learning rule and a natural gradient update formula of unmixing matrix are also presented. The algorithm of this letter can separate the mixture of super-Gaussian signals and also the mixture of sub-Gaussian signals. The simulation experiment demonstrates the efficiency of the algorithm.
