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 nested chain of scalar Wiener filters. This new representation of the Wiener filter provides the capability to perform an information-theoretic analysis of previous, basis-dependent, reduced-rank Wiener filters. This analysis demonstrates that the recently introduced cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and desired processes. A new reduced-rank Wiener filter is developed based on this new structure which evolves a basis using successive projections of the desired signal onto orthogonal, lower dimensional subspaces. The performance is evaluated using a comparative computer analysis model and it is demonstrated that the low-complexity multistage reduced-rank Wiener filter is capable of outperforming the more complex eigendecomposition-based methods.

A class of adaptive reduced-rank interference suppression algorithms is presented based on the multi-stage Wiener filter (MSWF). The performance is examined in the context of direct-sequence (DS) code division multiple access (CDMA). Unlike the Principal Components method for reduced-rank filtering, the algorithms presented can achieve near full-rank performance with a filter rank much less than the dimension of the signal subspace. We present batch and recursive algorithms for estimating the filter parameters, which do not require an eigen-decomposition. Algorithm performance in a heavily loaded DS-CDMA system is characterized via computer simulation. Results show that the reduced-rank algorithms require significantly fewer training samples than other reduced-and full-rank algorithms.

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This paper investigates the optimization of both single and full polarization radar transmission waveforms to maximize target identification discrimination. This theory is applied to the discrimination of the T-72 and Ml battle tanks based upon simulated target frequency response data. Significant performance improvement in identification is obtained using an optimized transmission waveform over that of a standard chirped pulse. generated using the Fast Illinois Solver Code Q^ISC) that applies a method-of-moments technique to provide high fidelity at relatively low radar frequencies. The specific VHF-band data generated by S AIC-Champaign cover frequencies between 225-375 MHz at an aspect interval of 2°.

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This paper introduces a cross-spectral metric for subspace selection and rank reduction in partially adaptive minimum variance array processing. The counter-intuitive result that it is suboptimal to perform rank reduction via the selection of the subspace formed by the principal eigenvectors of the array covariance matrix is demonstrated. A cross-spectral metric is shown to be the optimal criterion for reduced-rank Wiener filtering.