Filtering with discrete state observations
type: Item
instance of: scholarly article
title: Filtering with discrete state observations
zbMATH Open document ID: 0955.62094
published in: Applied Mathematics and Optimization
publication date: 2001-02-27T00:00:00Z
review text: In this paper, a finite state signal Markov chain is meant to model the possible orientations (for example, forward, up, down, right turn, left turn) of a target. The states of the Markov chain are observed indirectly through a process with ``information'' that has two sources, an independent counting source (noise), and a source that contains information about the Markov chain in one of two possible forms, the first in terms of \(\sigma\)-fields (this yields the weak formulation of the problem) and the second in terms of counting related to the state (this yields the strong formulation). In both cases a finite dimensional filtering formula is provided. A suboptimal filter is shown to converge to the signal Markov chain when the intensity of the independent counting component goes to zero.
Mathematics Subject Classification ID: 62M20
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 60J27
zbMATH DE Number: 1340512
zbMATH Keywords: tracking finite Markov chains
zbMATH Keywords: suboptimal filtering
zbMATH Keywords: filtering formula
author: F. Dufour
author: Robert J. Elliott
reviewed by: A. F. Gualtierotti
MaRDI profile type: Publication
full work available at URL: s002459900125
OpenAlex ID: W3178761443
DOI: 10.1007/S002459900125
description: scientific article; zbMATH DE number 1340512