Paper data
Title:
Using Farther Correlations to Further Improve the OptimallyWeighted SOBI Algrithm Author(s): Yeredor Arie, TelAviv University Doron Eran, TelAviv University Page numbers in the proceedings: Volume II pp 119122 Session: Blind Identification and Deconvolution
Paper abstract
The WeightsAdjusted SecondOrder Blind Identification (WASOBI) algorithm was recently proposed (Yeredor, 2000) as an optimized version of the SOBI Algorithm (Belouchrani et al., 1997) for blind separation of static mixtures of Gaussian Moving Average (MA) sources. The optimization consists of transforming the approximate joint diagonalization in SOBI into a properly weighted LeastSquares problem, with the asymptotically optimal weights specified in terms of the estimated correlations. However, only correlations up to the lag of the maximal MA order were used. Somewhat counterintuitively, it turns out that estimated correlation matrices beyond this lag are also useful, although the respective true correlations are known to be zero and have no direct dependence on the mixing matrix. Nevertheless, when properly incorporated into the weighted leastsquares problem, these estimated matrices can significantly improve performance, since they bear information on the estimation errors of the shorterlags matrices. In this paper we show how to modify the WASOBI algorithm accordingly, and demonstrate the improvement via analysis and simulation results.
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