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Citation

Šimandl, M. and Královec, J. : Filtering, prediction and smoothing with gaussian sum representation . System Identification (SYSID 2000), p. 1157-1162, IFAC, Oxford, 2001.

Abstract

The paper deals with the statee stimation problem for discrete-time nonlinear non-Gaussian stochastic dynamic systems. A description of all random variables of the system by the Gaussian sum probability density functions is considered. This assumption enables to obtain an explicit exact or approximate solution of the three basic types of state estimation, i.e. prediction, filtering, and smoothing. Multi-step prediction and smoothing for nonlinear and/or non-Gaussian systems are newly presented. The stress is laid also on systematic presentation of the new and current results of an application of the Gaussian sums in the nonlinear state estimation problem.

Detail of publication

Title: Filtering, prediction and smoothing with gaussian sum representation
Author: Šimandl, M. ; Královec, J.
Language: English
Date of publication: 21 Jun 2000
Year: 2001
Type of publication: Papers in proceedings of reviewed conferences
Title of journal or book: System Identification (SYSID 2000)
Page: 1157 - 1162
ISBN: 0-08-043545-9
Publisher: IFAC
Address: Oxford
Date: 21 Jun 2000 - 23 Jun 2000
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Keywords

nonlinear state estimation, filtering, prediction, smoothing, probability density function, Gaussian sum representation

BibTeX

@INPROCEEDINGS{SimandlM_2001_Filteringprediction,
 author = {\v{S}imandl, M. and Kr\'{a}lovec, J.},
 title = {Filtering, prediction and smoothing with gaussian sum representation},
 year = {2001},
 publisher = {IFAC},
 journal = {System Identification (SYSID 2000)},
 address = {Oxford},
 pages = {1157-1162},
 ISBN = {0-08-043545-9},
 url = {http://www.kky.zcu.cz/en/publications/SimandlM_2001_Filteringprediction},
}