Paper No: 142
A Robust Complex FastICA Algorithm Using the Huber M-Estimator Cost Function
Author(s): Jih-Cheng Chao, Scott Douglas
In this paper, we propose to use the Huber M-estimator cost function as a contrast function within the complex FastICA algorithm of Bingham and Hyvarinen for the blind separation of mixtures of independent, non-Gaussian, and proper complex-valued signals. Sufficient and necessary conditions for the local stability of the complex-circular FastICA algorithm for an arbitrary cost are provided. A local stability analysis shows that the algorithm based on the Huber M-estimator cost has behavior that is largely independent of the cost function's threshold parameter for mixtures of non-Gaussian signals. Simulations demonstrate the ability of the proposed algorithm to separate mixtures of various complex-valued sources with performance that meets or exceeds that obtained by the FastICA algorithm using kurtosis-based and other contrast functions.