DocumentCode
1961484
Title
Recursive nonlinear estimation of random parameter AR models with Poisson observations
Author
Evans, Jamie S. ; Krishnamurthy, Vikram
Author_Institution
Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia
Volume
5
fYear
1997
fDate
10-12 Dec 1997
Firstpage
5042
Abstract
We derive exact filters for the state of a doubly stochastic AR process with parameters which vary according to a nonlinear function of a Gauss-Markov process. The observations consist of a discrete time Poisson process with rate a positive function of the Gauss-Markov process. The dimension of the sufficient statistic increases linearly with the number of observed events
Keywords
Markov processes; autoregressive processes; filtering theory; observers; recursive estimation; Gauss-Markov process; Poisson observations; discrete time Poisson process; doubly stochastic AR process; exact filters; random parameter AR models; recursive nonlinear estimation; sufficient statistic; Filters; Gaussian processes; Markov processes; Parameter estimation; Position measurement; Recursive estimation; State estimation; Statistics; Stochastic processes; Target tracking;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1997., Proceedings of the 36th IEEE Conference on
Conference_Location
San Diego, CA
ISSN
0191-2216
Print_ISBN
0-7803-4187-2
Type
conf
DOI
10.1109/CDC.1997.649860
Filename
649860
Link To Document