Optimal state estimation for uncertain, time varying systems with non-Gaussian initial state

Author

Lainiotis, D.G. ; Giannakopoulos, P.K. ; Kas, S. K Katsi

Author_Institution

Dept. of Electr. & Comput. Eng., Florida Inst. of Technol., Melbourne, FL, USA

fYear

1991

fDate

14-17 Apr 1991

Firstpage

1877

Abstract

The problem of state estimation for partially unknown, time-varying, linear systems with non-Gaussian initial conditions is addressed. It is shown that the optimal estimator for this problem is an adaptive Lainiotis (1989) filter with nonlinear Lainiotis filters for non-Gaussian initial conditions as elemental filters. Closed-form solutions for several explicit cases of the initial state PDFs are given. Simulation experiments show the superiority of the proposed algorithm over an adaptive Lainiotis filter with Kalman filters as elemental filters

Keywords

adaptive filters; digital filters; filtering and prediction theory; linear systems; state estimation; time-varying systems; Kalman filters; PDF; adaptive Lainiotis filter; closed form solutions; elemental filters; linear systems; nonGaussian initial state; nonlinear Lainiotis filters; optimal state estimation; probability density functions; simulation experiments; time varying systems; Adaptive filters; Airplanes; Closed-form solution; Instruments; Linear systems; Marine vehicles; Nonlinear filters; State estimation; Time varying systems; Transient response;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Conference_Location

Toronto, Ont.

ISSN

1520-6149

Print_ISBN

0-7803-0003-3

Type

conf

DOI

10.1109/ICASSP.1991.150736

Filename

150736