Title :
Minimal dimensional linear filters for discrete-time Markov processes with finite state space
Author :
Masi, G. B Di ; Kitsul, P.I.
Author_Institution :
Dipartimento di Matematica Pura e Applicata, Padova Univ., Italy
fDate :
10/1/1996 12:00:00 AM
Abstract :
We consider a filtering problem for a discrete-time Markov process with k states observed in white Gaussian noise. It is known that in this situation the best linear estimate is given by a k-dimensional Kalman filter, and in some cases the dimension of such a filter can be reduced. Here, using a backward semimartingale description of the process and results from stochastic realization theory, we provide an algorithm for the construction of the minimal dimensional linear filter
Keywords :
Gaussian noise; Kalman filters; Markov processes; discrete time systems; filtering theory; realisation theory; state estimation; state-space methods; white noise; backward semimartingale description; best linear estimate; discrete-time Markov processes; finite state space; k-dimensional Kalman filter; minimal dimensional linear filters; stochastic realization theory; white Gaussian noise; Filtering; Filtration; Gaussian noise; Markov processes; Nonlinear filters; Random processes; State-space methods; Statistics; Stochastic resonance; Stochastic systems;
Journal_Title :
Automatic Control, IEEE Transactions on
