Robust recursive estimation for linear systems with non-Gaussian state and measurement noises

Author

Lee, Ki Yong ; Lee, Byung-Gook ; Ann, Souguil ; Song, Lickho

Author_Institution

Changwon Nat. Univ., South Korea

Volume

iv

fYear

1994

fDate

19-22 Apr 1994

Abstract

In many applications we have to deal with densities which are highly non-Gaussian or which may have Gaussian shape in the middle but have potent deviations in the tails. To fight against this deviation, we consider Bayesian estimation in the case of non-Gaussian state and measurement noises. The robust estimation problem for linear discrete-time systems is considered here. The non-Gaussian noises are modeled as a mixture. We present a robust recursive estimation model that is an approximate minimum variance estimator with a maximum a posteriori (MAP) decision rule for determining the noise sequence distribution

Keywords

Bayes methods; discrete time systems; maximum likelihood estimation; noise; recursive estimation; Bayesian estimation; Gaussian shape; linear discrete-time systems; linear systems; maximum a posteriori decision rule; measurement noise; minimum variance estimator; mixture; noise sequence distribution; nonGaussian state noise; robust recursive estimation; Gaussian noise; Linear systems; Noise measurement; Noise robustness; Noise shaping; Random variables; Recursive estimation; Shape measurement; State estimation; Tail;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1994. ICASSP-94., 1994 IEEE International Conference on

Conference_Location

Adelaide, SA

ISSN

1520-6149

Print_ISBN

0-7803-1775-0

Type

conf

DOI

10.1109/ICASSP.1994.389808

Filename

389808