Title :
Kalman filtering utilizing future dynamics for descriptor systems
Author :
Yu, Tie-Jun ; Lin, Ching-Fang ; Müller, Peter C.
Author_Institution :
American GNC Corp., Chatsworth, CA, USA
Abstract :
This paper studies the filtering problem of descriptor systems. The noncausal behaviour of descriptor systems leads to filtering that takes into account not only past and present dynamics, but also the future dynamics. Using the maximum likelihood estimation technique, a recursive filter for general time-varying descriptor systems is developed which makes use of past, present as well as one-step future dynamics. The existence condition of the filter is also given which is weaker than that of the filter in Nikoukhah et al. (1992) and is identical to the infinity observability in the time-invariant case
Keywords :
Kalman filters; maximum likelihood estimation; observability; recursive filters; time-varying systems; Kalman filtering; descriptor systems; existence condition; general time-varying descriptor systems; infinity observability; maximum likelihood estimation technique; noncausal behaviour; one-step future dynamics; past dynamics; present dynamics; recursive filter; Covariance matrix; Estimation error; Filtering; Kalman filters; Lagrangian functions; Maximum likelihood estimation; Recursive estimation; Sufficient conditions;
Conference_Titel :
American Control Conference, Proceedings of the 1995
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-2445-5
DOI :
10.1109/ACC.1995.529220
