Comparison of Kalman filters formulated as the statistics of the Normal-inverse-Wishart distribution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F15%3APU116789" target="_blank" >RIV/00216305:26620/15:PU116789 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7403002" target="_blank" >http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7403002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC.2015.7403002" target="_blank" >10.1109/CDC.2015.7403002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Kalman filters formulated as the statistics of the Normal-inverse-Wishart distribution

Popis výsledku v původním jazyce

A novel growing-window recursive procedure for Kalman filter comparison is proposed based on the Bayesian inference principle. This procedure is capable of processing unlimited growth of 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 Kalman filters differing in their system model descriptions. The algebraic form of the comparison algorithm covers the situation when the covariance of the measurement noise is known as well as is unknown and the normalized covariance matrix of the process noise is always available. In this respect, the Kalman filter is formulated here as recursive learning of the sufficient statistics of the Normal and Normal-inverse-Wishart distributions.

Název v anglickém jazyce

Comparison of Kalman filters formulated as the statistics of the Normal-inverse-Wishart distribution

Popis výsledku anglicky

A novel growing-window recursive procedure for Kalman filter comparison is proposed based on the Bayesian inference principle. This procedure is capable of processing unlimited growth of 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 Kalman filters differing in their system model descriptions. The algebraic form of the comparison algorithm covers the situation when the covariance of the measurement noise is known as well as is unknown and the normalized covariance matrix of the process noise is always available. In this respect, the Kalman filter is formulated here as recursive learning of the sufficient statistics of the Normal and Normal-inverse-Wishart distributions.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

54th IEEE Conference on Decision and Control

ISBN

978-1-4799-7884-7

ISSN

0743-1546

e-ISSN

—

Počet stran výsledku

6

Strana od-do

5008-5013

Název nakladatele

Institute of electrical and electronics engineers inc.

Místo vydání

Neuveden

Místo konání akce

Osaka

Datum konání akce

15. 12. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000381554505033