State smoothing and filtering using a constant memory for dynamic systems with nonlinear interference and missing obvservations
type: Item
instance of: scholarly article
title: State smoothing and filtering using a constant memory for dynamic systems with nonlinear interference and missing obvservations
zbMATH Open document ID: 0703.93070
DOI: 10.1016/0016-0032(90)90013-9
author: Kerim Demirbaş
published in: Journal of the Franklin Institute
publication date: 1990-01-01T00:00:00Z
review text: The estimation of the state of a partially observed nonlinear discrete-time stochastic system by finite-state approximation is proposed. Previous schemes require an exponentially increasing memory with time. Here the state estimation is carried out in blocks so that its implementation requires only a constant memory. Numerical simulations are presented. No convergence property is discussed.
reviewed by: Mauro Piccioni
Mathematics Subject Classification ID: 93E11
Mathematics Subject Classification ID: 93E25
Mathematics Subject Classification ID: 93C10
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 93E14
zbMATH DE Number: 4154352
zbMATH Keywords: finite-state approximation
MaRDI profile type: Publication
full work available at URL: 0016-0032(90)90013-9
OpenAlex ID: W2002801019
cites work: A suboptimum decoding-based smoothing algorithm for dynamic systems with or without interference
cites work: Q5670067
cites work: A survey of data smoothing for linear and nonlinear dynamic systems
cites work: A view of three decades of linear filtering theory
cites work: Discrete optimal linear smoothing for systems with uncertain observations
cites work: State estimation for nonlinear discrete dynamic systems with missing observations
cites work: Convolutional codes II. Maximum-likelihood decoding
description: scientific article; zbMATH DE number 4154352