An interacting multiple model algorithm with a switching Markov chain
type: Item
instance of: scholarly article
title: An interacting multiple model algorithm with a switching Markov chain
zbMATH Open document ID: 0876.93099
DOI: 10.1016/S0895-7177(96)00180-X
author: O. Diekmann
published in: Mathematical and Computer Modelling
publication date: 1997-08-03T00:00:00Z
review text: A Markov chain plays an important role in an interacting multiple model algorithm which has been shown to be effective for target tracking systems. Such systems are described by a mixing of continuous states and discrete modes. The switching between system modes is governed by a Markov chain. In real world applications, this Markov chain may change or needs to be changed. Therefore, one may be concerned about a target tracking algorithm associated to the switching of a Markov chain. This paper concentrates on fault-tolerant algorithm design and algorithm analysis of interacting multiple model estimation with the switching of a Markov chain. Monte Carlo simulations are carried out and several conclusions are given.
Mathematics Subject Classification ID: 93E35
Mathematics Subject Classification ID: 93A30
zbMATH DE Number: 1042727
zbMATH Keywords: model selection
zbMATH Keywords: interacting multiple model algorithm
zbMATH Keywords: target tracking systems
zbMATH Keywords: fault-tolerant algorithm
zbMATH Keywords: switching of a Markov chain
reviewed by: Yasuo Sugai
MaRDI profile type: Publication
cites work: The interacting multiple model algorithm for systems with Markovian switching coefficients
cites work: Investigation of moving-bank multiple model adaptive algorithms
cites work: An optimizing design strategy for multiple model adaptive estimation and control
cites work: Multiple-model estimation with variable structure
cites work: Multiresolutional distributed filtering
full work available at URL: s0895-7177(96)00180-x
OpenAlex ID: W2083295113
description: scientific article; zbMATH DE number 1042727