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Description
  • Článek se zabývá simulačními filtry v úloze odhadu stavu diskrétních dynamických stochastických systémů. Důraz je kladen na návrh algoritmu pro adaptaci počtu vzorků. Navržený adaptační algoritmus způsobí, že kvalita odhadu stavu je nezávislá na použité vzorkovací hustotě. Algoritmus zachovává s ohledem na měření kvalitu celé množiny vzorků generovaných ze vzorkovací hustoty tím, že modifikuje počet vzorků. Na numerickém příkladu je ilustrována stejná kvalita odhadu simulačních filtrů s různými vzorkovacími hustotami použitím adaptačního algoritmu. (cs)
  • Particle filters for state estimation of discrete time dynamic stochastic systems are treated. The stress is laid on design of an algorithm for sample size adaptation. The proposed adaptation algorithm enables quality of state estimate to be independent of sampling density used in particle filter. The algorithm maintains quality of the whole set of samples generated from a sampling density with respect to the current measurement by modification of sample size. Equal estimate quality of particle filters with different sampling density achieved using the proposed adaptation algorithm is illustrated in a numerical example.
  • Particle filters for state estimation of discrete time dynamic stochastic systems are treated. The stress is laid on design of an algorithm for sample size adaptation. The proposed adaptation algorithm enables quality of state estimate to be independent of sampling density used in particle filter. The algorithm maintains quality of the whole set of samples generated from a sampling density with respect to the current measurement by modification of sample size. Equal estimate quality of particle filters with different sampling density achieved using the proposed adaptation algorithm is illustrated in a numerical example. (en)
Title
  • Sample size adaptation for particle filters
  • Adaptace počtu vzorků pro simulační filtry (cs)
  • Sample size adaptation for particle filters (en)
skos:prefLabel
  • Sample size adaptation for particle filters
  • Adaptace počtu vzorků pro simulační filtry (cs)
  • Sample size adaptation for particle filters (en)
skos:notation
  • RIV/49777513:23520/05:00000116!RIV07-MSM-23520___
http://linked.open.../vavai/riv/strany
  • 437-442
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • 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
  • 541845
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/05:00000116
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • state estimation; nonlinear systems; particle filters; sample sizes; probability density function; stochastic systems (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [6EF5F09F745A]
http://linked.open...i/riv/mistoVydani
  • Oxford
http://linked.open...i/riv/nazevZdroje
  • Automatic control in aerospace 2004
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
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
  • 0-08-044013-4
http://localhost/t...ganizacniJednotka
  • 23520
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