Title :
State-space estimation with uncertain data: finite and infinite-horizon results
Author_Institution :
Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA
Abstract :
Develops a robust estimation procedure for state-space models with parametric uncertainties. Compared with existing robust filters, the proposed filter performs data regularization rather than de-regularization. It is shown that, under certain stabilizability and detectability conditions, the steady-state filter is stable and that, for quadratically-stable models, it guarantees a bounded error variance
Keywords :
Kalman filters; estimation theory; filtering theory; matrix algebra; stability; state estimation; state-space methods; bounded error variance; data regularization; detectability conditions; parametric uncertainties; quadratically-stable models; robust filters; stabilizability conditions; state-space estimation; steady-state filter; uncertain data; Cost function; Degradation; Ear; Filters; Robustness; Stability; State estimation; Steady-state; Uncertainty;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2001.914664
