DocumentCode :
1082427
Title :
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
Link To Document :
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