Title :
Nonstationary noise identification with the interacting multiple model algorithm
Author :
BarShalom, Y. ; Li, Xiaorong ; Chang, K.C.
Author_Institution :
Connecticut Univ., Storrs, CT, USA
Abstract :
The interacting multiple-model state estimation algorithm has been shown to be one of the most cost-effective schemes for estimating the state of hybrid systems. Such systems, which have continuous and discrete uncertainties, are represented by a finite set of noisy state equations, each pertaining to a certain mode. The system can switch from one mode to another according to an assumed underlying Markov chain. This framework is described and used here to estimate the time-varying intensity of the noise processes in a dynamic system
Keywords :
Markov processes; linear systems; state estimation; Markov chain; dynamic system; hybrid systems; interacting multiple model algorithm; interacting multiple-model state estimation; noise processes; noisy state equations; nonstationary noise identification; time-varying intensity; uncertainties; Additive noise; Equations; History; Noise measurement; Power system modeling; Sampling methods; State estimation; Statistics; Switches; Uncertainty;
Conference_Titel :
Intelligent Control, 1990. Proceedings., 5th IEEE International Symposium on
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-8186-2108-7
DOI :
10.1109/ISIC.1990.128516
