Approximations for nonlinear filters based on quantisation
type: Item
instance of: scholarly article
zbMATH Open document ID: 0980.65062
author: Nigel J. Newton
published in: Monte Carlo Methods and Applications
publication date: 2002-02-25T00:00:00Z
Mathematics Subject Classification ID: 65K10
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 65C40
Mathematics Subject Classification ID: 60J22
Mathematics Subject Classification ID: 60G35
zbMATH DE Number: 1623585
zbMATH Keywords: nonlinear filters
zbMATH Keywords: observation quantisation
zbMATH Keywords: Markow chain
zbMATH Keywords: stochastic particle methods
MaRDI profile type: Publication
full work available at URL: mcma.2001.7.3-4.311
OpenAlex ID: W1978090776
DOI: 10.1515/MCMA.2001.7.3-4.311
title: Approximations for nonlinear filters based on quantisation
review text: The author studies the use of observation quantisation with discrete-time nonlinear filters in which the signal is a finite-state Markov chain, and the observation is the signal plus white noise. It is shown that the reduction of the filter accuracy is small for filters with slow signal dynamics and substantially noisy observations. A version of this result for continuous-time filters is presented. Optimal quantisation schemes are also discussed. At the end, three numerical methods for continuous-time Markov chain filters (including deterministic and stochastic particle methods based on quantised observations) are compared.
reviewed by: Mikhail Krastanov
description: scientific article; zbMATH DE number 1623585