Linear prediction, filtering, and smoothing: An information-theoretic approach

Abstract

Information-theoretic concepts are used to derive some fundamental principles for the general estimation problem. With these basic principles, a minimal-error entropy estimator for linear systems disturbed by Gaussian random processes is easily derived, which is identical to the Kalman filter. Under non-Gaussian disturbances it is shown that the Kalman… (More)
DOI: 10.1016/0020-0255(79)90039-2

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@article{Kalata1979LinearPF, title={Linear prediction, filtering, and smoothing: An information-theoretic approach}, author={Paul Kalata and Roland Priemer}, journal={Inf. Sci.}, year={1979}, volume={17}, pages={1-14} }