A multistage representation of the Wiener filter based on orthogonal projections

The Wiener filter is analyzed for stationary complex Gaussian signals from an information-theoretic point of view. A dual-port analysis of the Wiener filter leads to a decomposition based on orthogonal projections and results in a new multistage method for implementing the Wiener filter using a nest ed chain of scalar Wiener filters. This new representation of the Wiener fi lter provides the capability to perform an information-theoretic analysis o f previous, basis-dependent, reduced-rank Wiener filters. This analysis dem onstrates that the recently introduced cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and des ired processes. A new reduced-rank Wiener filter is developed based on this new structure which evolves a basis using successive projections of the de sired signal onto orthogonal, lower dimensional subspaces. The performance is evaluated using a comparative computer analysis model and it is demonstr ated that the low-complexity multistage reduced-rank Wiener filter is capab le of outperforming the more complex eigendecomposition-based methods.