Title :
Exact filters for doubly stochastic AR models with conditionally Poisson observations
Author :
Evans, Jamie ; Krishnamurthy, Vikram
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., California Univ., Berkeley, CA, USA
fDate :
4/1/1999 12:00:00 AM
Abstract :
The authors derive exact filters for the state of a doubly stochastic auto-regressive (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 :
Gaussian processes; Markov processes; autoregressive processes; filtering theory; nonlinear filters; AR models; Gauss-Markov process; Poisson observations; autoregressive process; doubly stochastic models; nonlinear filters; probability space; Application software; Filtering; Gaussian processes; Image sensors; Nonlinear filters; Optical filters; Position measurement; Statistics; Stochastic processes; Target tracking;
Journal_Title :
Automatic Control, IEEE Transactions on
