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State estimators and observers for continuous and discrete linear systems. Part 1. Differential asymptotic state estimators

Byrski J., Byrski W.

Science, Technology and Innovation

2018

Vol. 3, no. 2

62--68

In the paper an overview of state estimators and state observers used in linear systems, will be presented. The state estimators and observers can be used in many applications like the state reconstruction for the control purposes or for the diagnosis and fault detection in technical processes or for the virtual measurements of inaccessible variables of the system as well as for the best filtration of the differential equation solution. As the standard most commonly the Kalman filter and Luenberger type observers are used. Although the Kalman filter guarantees optimal filtering quality of the state, reconstructed from the noisy measurements, both Kalman filter and the Luenberger observer guarantee only asymptotic quality of the real state changes and tracking, basing on the current measurements of the system output and input signals. Unfortunately, the value of the estimation error at any moment of time cannot be calculated. The discussion on differences between continuous and two types of discrete Kalman Filter will be presented. This paper is planned to be the introduction to presentation of another type of the state observers which have the structure given by the integral operators. Based on measurements of the system output and input signals on some predefined finite time interval, they can reconstruct, after this interval, the observed state exactly.

On admissible quadratic estimation in a special linear model

Neumann K.

Probability and Mathematical Statistics

2005

Vol. 25, Fasc. 2

257--265

A random linear model for spatially located sensors measured intensity of a source of signals in discrete instants of time is considered. A characterization of admissible quadratic estimators of the mean squared error of a linear estimator of the expectation of the intensity is given.

On admissibility in estimating the mean squared error of a linear estimator

Stępniak Czesław

Probability and Mathematical Statistics

1998

Vol. 18, Fasc. 1

33--38

Consider a linear estimator of a parametric vector Cß in the normal Gauss-Markov model Y ~ N {Xß, σV). The Mean Squared Error of such an estimator may be presented in the form kσ+ß' X' KXß and may be estimated by quadratics in Y Some basic decision-theoretic questions in the estimation are discussed. Among others, some admissible estimators of the MSE in several classes of the quadratics are given.