Exact finite-dimensional filters for doubly stochastic auto-regressive processes

Author

Krishnamurthy, Vikram ; Elliott, Robert J.

Author_Institution

Dept. of Electr. & Electron. Eng., Melbourne Univ., Parkville, Vic., Australia

Volume

42

Issue

9

fYear

1997

fDate

9/1/1997 12:00:00 AM

Firstpage

1289

Lastpage

1293

Abstract

In this paper, we derive exact finite-dimensional recursive filters for a class of doubly stochastic auto-regressive (AR) models. We assume that the parameters of the doubly stochastic AR process vary according to a nonlinear polynomial function of a Gaussian state-space process. Apart from being of mathematical interest, these finite-dimensional filters have potential applications in time-series analysis and image-enhanced tracking of maneuvering targets

Keywords

autoregressive processes; computational complexity; filtering theory; image processing; polynomial matrices; probability; recursive filters; state-space methods; Gaussian state-space process; doubly stochastic autoregressive models; finite-dimensional filters; image-enhanced tracking; nonlinear polynomial function; probability; recursive filters; time-series; Hidden Markov models; Image analysis; Nonlinear filters; Polynomials; Random variables; Signal processing; Stochastic processes; Stochastic resonance; Target tracking; Time series analysis;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.623095

Filename

623095