A state decoupling approach to estimate unobservable tracking systems

Author

Liu, Pan-Tai ; Li, Fu ; Xiao, Heng

Author_Institution

Dept. of Math., Rhode Island Univ., Kingston, RI, USA

Volume

21

Issue

3

fYear

1996

fDate

7/1/1996 12:00:00 AM

Firstpage

256

Lastpage

259

Abstract

If a system is unobservable, the error covariance associated with a Kalman filter will be nearly singular. As a consequence, an optimum estimation in the sense of minimum error covariance does not exist. In this paper, we show that this (unobservable) system can be transformed into a nonlinear system with a linear measurement equation. In addition to other useful features, this transformation also serves to decouple the state in such a way that an observable part can be extracted and estimated while no information can be gained and processed for the unobservable part

Keywords

Kalman filters; filtering theory; nonlinear systems; observability; state estimation; tracking; tracking filters; Kalman filter; error covariance; linear measurement equation; minimum error covariance; nonlinear system; optimum estimation; state decoupling; tracking; unobservable system; unobservable tracking systems; Covariance matrix; Data mining; Kalman filters; Linear systems; Nonlinear equations; Nonlinear systems; Observability; Riccati equations; State estimation; Vectors;

fLanguage

English

Journal_Title

Oceanic Engineering, IEEE Journal of

Publisher

ieee

ISSN

0364-9059

Type

jour

DOI

10.1109/48.508156

Filename

508156