Recursive complex BSS via generalized eigendecomposition and application in image rejection for BPSK

Authors:
Puskal P. Pokharel;Umut Ozertem;Deniz Erdogmus;Jose C. Principe
Affiliations:
Computational NeuroEngineering Laboratory, ECE Department, University of Florida, Gainesville, FL 32611, USA;Adaptive Systems Laboratory, CSEE Department, Oregon Health and Science University, OR 97006, USA;Adaptive Systems Laboratory, CSEE Department, Oregon Health and Science University, OR 97006, USA;Computational NeuroEngineering Laboratory, ECE Department, University of Florida, Gainesville, FL 32611, USA
Venue:
Signal Processing
Year:
2008

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Abstract

Under the assumptions of non-Gaussian, non-stationary, or non-white independent sources, linear blind source separation can be formulated as generalized eigenvalue decomposition. Here we provide an elegant method of doing this on-line, instead of waiting for a sufficiently large batch of data. This is done through a recursive generalized eigendecomposition algorithm that tracks the optimal solution that one would obtain using all the data observed. The algorithms proposed in this paper follow the well-known recursive least squares (RLS) algorithm in spirit. We also propose to employ this on-line approach for joint image rejection in separating audio signals with the linear mixing varying with time and in slow fading wireless receivers with successful results.