Title :
State decoupling in estimation theory
Author :
Liu, Pan-Tai ; Fang, Hui ; Li, Fu ; Xiao, Heng
Author_Institution :
Dept. of Math., Rhode Island Univ., Kingston, RI, USA
Abstract :
When a system is unobservable, the error covariance associated with a Kalman filter will be nearly singular. As a consequence, an optimum estimation does not exist. In this paper, we show that this 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; covariance analysis; filtering theory; nonlinear systems; observability; state estimation; Kalman filter; error covariance; estimation theory; linear measurement equation; nonlinear system; observability; optimum estimation; state decoupling; transformation; Covariance matrix; Data mining; Estimation theory; Kalman filters; Linear systems; Nonlinear equations; Nonlinear systems; Observability; State estimation; Vectors;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1995. ICASSP-95., 1995 International Conference on
Conference_Location :
Detroit, MI
Print_ISBN :
0-7803-2431-5
DOI :
10.1109/ICASSP.1995.478677
