Bayesian comparison of Kalman filters with known covariance matrices

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F15%3APU115897" target="_blank" >RIV/00216305:26620/15:PU115897 - isvavai.cz</a>
Result on the web
<a href="http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4912383" target="_blank" >http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.4912383</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1063/1.4912383" target="_blank" >10.1063/1.4912383</a>

Result language
angličtina
Original language name
Bayesian comparison of Kalman filters with known covariance matrices
Original language description
A growing-window recursive procedure for model comparison is proposed based on the Bayesian inference principle. This procedure, compared to the batch one, is capable of processing unlimited increases in the uncertainty of the initial parameter settings,which is a characteristic of Kalman type algorithms. The present paper applies the suggested procedure to assess the degree of support for the state point estimates generated by multiple Kalman filters. We investigate a case where the covariance of themeasurement noise and the normalized covariance matrix of the process noise are both available.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2015
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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