Original title: On Estimation of Unknown Disturbances of Non-Linear State-Space Model Using Marginalized Particle Filter
Translated title: Odhad neznámé variance nelineárního stavového modelu pomocí marginalizovaného particle filteru
Authors: Šmídl, Václav
Document type: Research reports
Year: 2008
Language: eng
Series: Research Report, volume: 2245
Abstract: [eng] [cze]
The problem of estimation of unknown covariance matrix of non-linear state-space model is studied. The proposed methodology is based on combination of Extended Kalman Filter with particle filter. It is shown that the approach is promising for limited number of unknown parameters. More demanding problems with completely unknown covariance structures can not be reliably estimated since the observed data do not carry enough information. Práce se zabývá odhadem neznámé kovarianční matice nelineárního stavového modelu. Navržená metodika je kombinací rozšířeného Kalmanova filtru s particle filtrem. Výsledná metoda funguje velmi dobře pro kovarianční matice s danou omezenou strukturou. Složitější problémy s plnou strukturou kovarianční matice nelze spolehlivě odhadnout díky nedostatečné informační hodnotě pozorovaných dat.
Keywords: Bayesian filtering; particle filter; unknown covariance matrix
Project no.: CEZ:AV0Z10750506 (CEP), 1M0572 (CEP), GP102/08/P250 (CEP)
Funding provider: GA MŠk, GA ČR

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2008/AS/smidl-on estimation of unknown disturbances of non-linear state-space model using marginalized particle filter.pdf
Original record: http://hdl.handle.net/11104/0170734

Permalink: http://www.nusl.cz/ntk/nusl-40012


The record appears in these collections:
Research > Institutes ASCR > Institute of Information Theory and Automation
Reports > Research reports