Recursive Complex Blind Source Separation via Eigendecomposition of Cumulant Matrices
Under the assumptions of non-Gaussian, non-stationary, or non-white independent sources, linear blind source separation can be formulated as a generalized eigenvalue decomposition problem. Here we provide an elegant method of doing this online, instead of waiting for a sufficiently large batch of data. This is done through a recursive generalized eigendecomposition algorithm that tracks the optimal solution, which is obtained using all the data observed. The algorithms proposed in this paper follow the well-known recursive least squares (RLS) algorithm in nature.