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Description
  • V tomto příspěvku je představena nová metoda pro získání časo-frekvenční reprezentace okamžité frekvence signálu. Kalmánův filtr je zde použit pro rozklad signálu do módů s dobře definovanou okamžitou frekvencí. Model rezonátoru druhého řádu je použit jako model komponent signálu - %22monokomponentních funkcí%22. Současně je jsou Kalmanovým filtrem odhadovány časově proměnné komponenty signálu v komplexní formě. Počáteční parametry pro Kalmanův filtr jsou získány z odhadu spektrální hustoty pomocí Burgova algoritmu. Pro ilustraci výkonosti metody je uveden příklad reálné aplikace, který ukazuje přínos této metody ke zlepšení časo-frekvenčního rozlišení. (cs)
  • In this paper, a new method for obtaining a time-frequency representation of instantaneous frequency is introduced . A Kalman filter serves for dissociation of signal into modes with well defined instantaneous frequency. A second order resonator model is used as a model of signal components - 'monocomponent functions'. Simultaneously, the Kalman filter estimates the time-varying signal components in a complex form. The initial parameters for Kalman filter are obtained from the estimation of the spectral density through the Burg's algorithm by fitting an auto-regressive prediction model to the signal. To illustrate the performance of the proposed method, example of real application shows the contribution of this method to improve the time-frequency resolution.
  • In this paper, a new method for obtaining a time-frequency representation of instantaneous frequency is introduced . A Kalman filter serves for dissociation of signal into modes with well defined instantaneous frequency. A second order resonator model is used as a model of signal components - 'monocomponent functions'. Simultaneously, the Kalman filter estimates the time-varying signal components in a complex form. The initial parameters for Kalman filter are obtained from the estimation of the spectral density through the Burg's algorithm by fitting an auto-regressive prediction model to the signal. To illustrate the performance of the proposed method, example of real application shows the contribution of this method to improve the time-frequency resolution. (en)
Title
  • Nonstationary Signals Analysis Using Kalman Filter
  • Nonstationary Signals Analysis Using Kalman Filter (en)
  • Analýza nestacionárních signálů s využitím Kalmanova filtru (cs)
skos:prefLabel
  • Nonstationary Signals Analysis Using Kalman Filter
  • Nonstationary Signals Analysis Using Kalman Filter (en)
  • Analýza nestacionárních signálů s využitím Kalmanova filtru (cs)
skos:notation
  • RIV/49777513:23520/07:00000473!RIV08-MSM-23520___
http://linked.open.../vavai/riv/strany
  • 422-425
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
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
  • 437560
http://linked.open...ai/riv/idVysledku
  • RIV/49777513:23520/07:00000473
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • time-frequency analysis, instantaneous frequency, Kalman filter, state estimation, Hilbert transform (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [8F93569513EB]
http://linked.open...v/mistoKonaniAkce
  • Štrbské Pleso
http://linked.open...i/riv/mistoVydani
  • Košice
http://linked.open...i/riv/nazevZdroje
  • Proceedings of 8th International Carpathian Control Conference ICCC'2007
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Janeček, Eduard
  • Liška, Jindřich
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Technical University, BERG Faculty
https://schema.org/isbn
  • 978-80-8073-805-1
http://localhost/t...ganizacniJednotka
  • 23520
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