2015•Computer Engineering and Applications JournalOpen access
Novel blind source separation method based on FSS-kernel and Fast ICA combination
Wang Daod
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Abstract
Fast ICA algorithm has a faster and more robust convergence speed than the traditional ICA algorithm. But its recovery results are not satisfied. Because the specified non-linear function is not well in line with the statistical properties of the source signal. In order to solve the problem, a novel blind source separation method based on FSS-kernel and Fast ICA combination is proposed in this paper. Probability density function of the source signal is estimated by the FSS-kernel algorithm, and then, to restore the blind separation of mixed signals, Fast ICA algorithm is used, the negative entropy is the objective function. The simulation results show that the signal aliasing could be separated effectively by this method.It is proved that the method has higher separation accuracy and adaptive capacity, by contrasting with the traditional ICA algorithms and Fast ICA algorithm.