Time series: data analysis and theory
Time series: data analysis and theory
Linear Estimation and Detection in Krylov Subspaces
Linear Estimation and Detection in Krylov Subspaces
Subspace Expansion and the Equivalence of Conjugate Direction and Multistage Wiener Filters
IEEE Transactions on Signal Processing - Part II
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
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The multistage Wiener filter (MWF) is an iterative Schur complement procedure for estimating a scalar or a vector of random variables in the linear minimum mean square error sense. When a complementary orthogonal projector is used as a blocking matrix, intermediate vectors in the development of the MWF have singular covariance matrices, and the rationale of the MWF is undermined. For this situation, we validate the structure of the MWF by showing that it can be developed as an iterative generalized Schur complement procedure which relies on the Moore-Penrose generalized inverse. The validated filter does not require any generalized inverse, and it can be implemented as usual by using scalar Wiener filters when estimating a scalar random variable. Next, we show, under a condition which is commonly met in practice, that the MWF is a least squares reduced rank estimate of the Wiener filter from the Krylov subspace associated with the Wiener-Hopf equation. The MWF is then compared with Brillinger's reduced rank minimum mean square error filter. The two filters differ in the subspaces from which signal estimation is performed and in the order in which projection onto the respective subspaces and Wiener estimation are performed. A forward recursion for the mean square estimation error in each stage of the MWF is provided, and the performance of the MWF is demonstrated by a numerical example.