About: Sigma Point Gaussian Sum Filter Design Using Square Root Unscented Filters     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • Článek se zabývá lokálními a globálními přístupy k odhadu stavu. Důraz je především kladen na unscentovaný Kalmanův filtr a na filtr s vícenásobnou linearizací. Je zde odvozena odmocninová verze unscentovaného Kalmanova filtru, která je pak využita pro návrh filtru s vícenásobnou linearizací. Výsledný algoritmus tohoto nového filtru je v článku uveden a jsou zde také zmíněny některé jeho vlastnosti. Kvalita odhadu a numerická náročnost navrhovaného filtru je ukázána na příkladu. (cs)
  • Local and global estimation approaches are discussed, above all the Unscented Kalman Filter and the Gaussian Sum Filter. The square root modification of the Unscented Kalman Filter is derived and it is used in the Gaussian Sum Filter framework. The new Sigma Point Gaussian Sum Filter is designed and some aspects of the filter are presented. The estimation quality and computational demands of the designed filter are illustrated in a numerical example.
  • Local and global estimation approaches are discussed, above all the Unscented Kalman Filter and the Gaussian Sum Filter. The square root modification of the Unscented Kalman Filter is derived and it is used in the Gaussian Sum Filter framework. The new Sigma Point Gaussian Sum Filter is designed and some aspects of the filter are presented. The estimation quality and computational demands of the designed filter are illustrated in a numerical example. (en)
Title
  • Sigma Point Gaussian Sum Filter Design Using Square Root Unscented Filters
  • Využití odmocnivé verze unscentovaného Kalmanova filtru v návrhu filtru s vícenásobnou linearizací (cs)
  • Sigma Point Gaussian Sum Filter Design Using Square Root Unscented Filters (en)
skos:prefLabel
  • Sigma Point Gaussian Sum Filter Design Using Square Root Unscented Filters
  • Využití odmocnivé verze unscentovaného Kalmanova filtru v návrhu filtru s vícenásobnou linearizací (cs)
  • Sigma Point Gaussian Sum Filter Design Using Square Root Unscented Filters (en)
skos:notation
  • RIV/49777513:23520/06:00000428!RIV07-MSM-23520___
http://linked.open.../vavai/riv/strany
  • 1000-1005
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0572), Z(MSM 235200004)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 499214
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/06:00000428
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • stochastic systems, state estimation, estimation theory, filtering techniques, nonlinear filters (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [EE4DE58A4BA3]
http://linked.open...i/riv/mistoVydani
  • Oxford
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 16th IFAC World Congress
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Šimandl, Miroslav
  • Duník, Jindřich
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Elsevier
https://schema.org/isbn
  • 0-08-045108-X
http://localhost/t...ganizacniJednotka
  • 23520
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software