Title :
Optimal deconvolution based on polynomial methods
Author :
Ahlén, Anders ; Sternad, Mikael
Author_Institution :
Dept. of Technol., Uppsala Univ., Sweden
fDate :
2/1/1989 12:00:00 AM
Abstract :
The problem of estimating the input to a known linear system is treated in a shift operator polynomial formulation. The mean-square estimation error is minimized. The input and a colored measurement noise are described by independent ARMA (autoregressive moving average) processes. The filter is calculated by performing a spectral factorization and solving a polynomial equation. The approach can be applied to input prediction, filtering, and smoothing problems as well as to the use of prefilters in the quadratic criterion. It applies to nonminimum-phase as well as unstable systems, as illustrated by two examples
Keywords :
estimation theory; filtering and prediction theory; linear systems; polynomials; filter; independent ARMA; input estimation; input prediction; known linear system; mean-square estimation error; nonminimum-phase; optimal deconvolution; polynomial methods; prefilters; quadratic criterion; shift operator polynomial formulation; smoothing; spectral factorization; unstable systems; Colored noise; Deconvolution; Equations; Estimation error; Filtering; Filters; Linear systems; Noise measurement; Polynomials; Smoothing methods;
Journal_Title :
Acoustics, Speech and Signal Processing, IEEE Transactions on
