ICA 2007

ICA 2007
7th International Conference on
Independent Component Analysis
and Signal Separation

London, UK        9 - 12 September 2007

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Paper No: 148

Blind Instantaneous Noisy Mixture Separation with Best Interference-plus-noise Rejection

Author(s): Zbynek Koldovsky, Petr Tichavsky

Abstract

In this paper, a variant of the well known algorithm FastICA is proposed to be used for blind source separation in off-line (block processing) setup and a noisy environment. The algorithm combines symmetric FastICA with test of saddle points to achieve fast global convergence and one-unit refinement to obtain high noise rejection ability. It's computational complexity is similar to that of the original FastICA, i.e. it is fast. It is shown that the bias of the algorithm due to additive noise is reduced; it is proportional to sigma^3, where sigma^2 is the variance of the additive noise, while the bias of the other methods (namely of all methods using the orthogonality constraint, and even of recently proposed algorithm EFICA) is asymptotically proportional to sigma^2. Thanks to the reduced bias, the novel algorithm exhibits a significantly lower symbol-error rate when it is applied to blindly separate noisy mixtures of finite alphabet signals that are typical for communication systems.

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