Approximate state estimation for linear systems with quantized data

Author

Faridani, H.M.

Author_Institution

IBM Canada, Toronto, Ont., Canada

Volume

13

Issue

1

fYear

1988

Firstpage

32

Lastpage

38

Abstract

Considers the problem of sequential state estimation of discrete-time processes based on quantized measurements. An approximate minimum variance estimator algorithm that recursively updates the state estimate and its error covariance and closely approximates the exact minimum variance estimator is derived. The results of Monte-Carlo simulation are presented and the performance of the algorithm is compared to that of a Kalman filter in which the quantization error is approximated by an additive white Gaussian measurement noise.

Keywords

analogue-digital conversion; approximation theory; filtering and prediction theory; linear systems; state estimation; Kalman filter; Monte-Carlo simulation; additive white Gaussian measurement noise; approximate minimum variance estimator algorithm; discrete-time processes; error covariance; linear systems; quantization error; quantized data; quantized measurements; sequential state estimation; Approximation methods; Equations; Kalman filters; Mathematical model; Noise; Noise measurement; Quantization (signal);

fLanguage

English

Journal_Title

Electrical Engineering Journal, Canadian

Publisher

ieee

ISSN

0700-9216

Type

jour

DOI

10.1109/CEEJ.1988.6592903

Filename

6592903