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 capa...

In this paper, we provide a new theoretical analysis of the amount of musical noise generated via Wiener filter based on higher-order statistics. Spectral subtraction and Wiener filter are the most commonly used noise reduction methods, and in our previous study a musical noise assessment theory limited to the spectral subtraction was proposed. Therefore, in this paper, we propose a new theoret...

This paper communicates a novel method of Wiener filtering signals from scientific instruments to reduce their noise content. The main prerequisite for the applicability of this technique is that the pure (i.e. noiseless) signal is reproducible, a condition that is satisfied by a wide range of experimental measurements. The benefits of this Wiener filter design approach are its simplicity, gene...

If two wide sense stationary time series are correlated then one can be used to predict the other. The reduced rank Wiener filter is the rank constrained linear operator which maps the current value of one time series to an estimate of the current value of the other time series in an optimal way. A closed form solution exists for the reduced rank Wiener filter. This paper studies the problem of...

Wiener filtering has many applications in the area of imaging science. In image processing, for instance, it is a common way of reducing Gaussian noise. In color science it is often used to estimate reflectances from camera response data on a pixel by pixel basis. Based on a priori assumptions the Wiener filter is the optimal linear filter in the sense of the minimal mean square error to the ac...

Sampling is a very important and basic technique for signal processing. In the case that noise is added to a signal in the sampling process, we may use a reconstruction and noise reduction filter such as the Wiener filter. The Wiener filter provides a restored signal of which mean square error is minimized. However, the mean square error by the Wiener filter depends on the sampling vectors. We ...

Increasing GPS precision at low cost has always been a challenge for the manufacturers of the GPS receivers. This paper proposes the use of a Wiener filter for increasing precision in substitution of traditional GPS/INS fusion systems, which require expensive inertial systems. In this paper, we first implement and compare three GPS signal processing schemes: a Kalman filter, a neural network an...

An innovative space-time adaptive preprocessing algorithm based on a low complexity multistage nested Wiener filter is introduced. A closed form expression for the Wiener-Hopf filter weights is developed using the multistage nested Wiener falter parameters. It is demonstrated that this algorithm is capable of suppressing both wideband and narrowband jammers while maintaining low complexity.

A simpliied way of deriving of realizable and explicit Wiener lters is presented. Discrete time problems are discussed, in a polynomial equation framework. Optimal lters, predictors and smoothers are calculated by means of spectral factorizations and linear polynomial equations. A new tool for obtaining these equations, for a given problem structure, is described. It is based on evaluation of o...

We formulate a robust Wiener filtering problem for wide-sense stationary (WSS) Gaussian processes in the presence of modelling errors. It requires solving a minimax problem that consists of finding the best filter for the least-favorable statistical model within a neighborhood of the nominal model. The neighborhood is formed by models whose relative entropy with respect to the nominal model is ...