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. |
