Adaptive Chandrasekhar filter for linear discrete-time stationary stochastic systems
type: Item
instance of: scholarly article
title: Adaptive Chandrasekhar filter for linear discrete-time stationary stochastic systems
zbMATH Open document ID: 0823.65066
DOI: 10.1016/0096-3003(94)00104-C
author: Masanori Sugisaka
published in: Applied Mathematics and Computation
publication date: 1995-10-24T00:00:00Z
review text: The adaptive Chandrasekhar filter based on the SSM is developed for the adaptive estimation of the states and parameters in linear discrete time- invariant dynamical systems. Two types of models are presented to calculate the sensitivity functions with respect to the unknown parameters. One is the conventional linear innovations model and the other is the simplified model which has less computational time and storage than the innovations model. It is explained that the adaptive Chandrasekhar filtering technique is superior to the adaptive Kalman filtering technique from a computational point of view. Also, the adaptive Chandrasekhar filter is stable in numerical calculations.
reviewed by: F. Zhang
Mathematics Subject Classification ID: 65K10
Mathematics Subject Classification ID: 65C99
Mathematics Subject Classification ID: 93C99
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 60G35
Mathematics Subject Classification ID: 93E11
zbMATH DE Number: 761777
zbMATH Keywords: stationary stochastic systems
zbMATH Keywords: adaptive Chandrasekhar filter
zbMATH Keywords: linear discrete time-invariant dynamical systems
zbMATH Keywords: adaptive Kalman filtering
zbMATH Keywords: time-invariant
MaRDI profile type: Publication
cites work: Q3799870
full work available at URL: 0096-3003(94)00104-c
OpenAlex ID: W1988938791
description: scientific article; zbMATH DE number 761777