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Description
  • Článek se zabývá řešením Bayesových rekurzivních vztahů pomocí simulačních filtrů. Důraz je kladen na specifilkaci počtu vzorků. Jako nástroj pro specifikaci počtu vzorků pro filtraci, predikci i vyhlazování byla zvolena Cramér-Raova mez. Matice střední kvadratické chyby odhadu stavu pro simulační filtry byla porovnána s Cramér-Raovou mezí. Kvalita simulačních filtrů a jejich výpočetní náročnost je ilustrována na numerickém příkladu. (cs)
  • A solution of the Bayesian recursive relations by the particle filter approach is treated. The stress is laid on the sample size setting as the main user design problem. The Cramér-Rao bound was chosen as a tool for setting the sample size for the three basic types of the state estimation, for filtering, prediction and smoothing. The mean square error matrices of particle filter state estimates for different sample sizes and the CR bounds are compared. Quality of the particle filters and their computational load are illustrated in a numerical example.
  • A solution of the Bayesian recursive relations by the particle filter approach is treated. The stress is laid on the sample size setting as the main user design problem. The Cramér-Rao bound was chosen as a tool for setting the sample size for the three basic types of the state estimation, for filtering, prediction and smoothing. The mean square error matrices of particle filter state estimates for different sample sizes and the CR bounds are compared. Quality of the particle filters and their computational load are illustrated in a numerical example. (en)
Title
  • Nonlinear estimation by particle filters and Cramér-Rao bound
  • Nonlinear estimation by particle filters and Cramér-Rao bound (en)
  • Simulační filtry v úloze odhadu a Cramér-Raova mez (cs)
skos:prefLabel
  • Nonlinear estimation by particle filters and Cramér-Rao bound
  • Nonlinear estimation by particle filters and Cramér-Rao bound (en)
  • Simulační filtry v úloze odhadu a Cramér-Raova mez (cs)
skos:notation
  • RIV/49777513:23520/03:00000049!RIV07-GA0-23520___
http://linked.open.../vavai/riv/strany
  • 79-84
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/01/0021), 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
  • 618128
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/03:00000049
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Monte Carlo methods; Nonlinear filters; Cramér-Rao bound; Mean square error; Nonlinear systems (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [9D6673364BC0]
http://linked.open...i/riv/mistoVydani
  • Oxford
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 15th 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
  • Straka, Ondřej
  • Šimandl, Miroslav
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Elsevier
https://schema.org/isbn
  • 0-08-044221-8
http://localhost/t...ganizacniJednotka
  • 23520
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