Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00199315" target="_blank" >RIV/68407700:21230/13:00199315 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1002/acs.2369" target="_blank" >http://dx.doi.org/10.1002/acs.2369</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/acs.2369" target="_blank" >10.1002/acs.2369</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo

Popis výsledku v původním jazyce

Linear time-invariant systems play significant role in the control field. A number of methods have been published for identification of the deterministic part of a process. However, identification of the stochastic part has had much less attention. Thispaper deals with estimation of covariance matrices of the noise entering a linear system. The process and measurement noise covariance matrices are tuning parameters of the Kalman filter and they affect quality of the state estimation. The noise covariance matrices are generally not known and their estimation from the measured data is a challenging task. This paper introduces a method for estimation of the noise covariance matrices using Bayesian approach along with Monte Carlo numerical methods. Performance of the approach is tested on various systems and noise properties. The second part of the paper compares Monte Carlo approach to the recently published methods. The speed of convergence is compared to the Cramér-Rao bounds.

Název v anglickém jazyce

Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo

Popis výsledku anglicky

Linear time-invariant systems play significant role in the control field. A number of methods have been published for identification of the deterministic part of a process. However, identification of the stochastic part has had much less attention. Thispaper deals with estimation of covariance matrices of the noise entering a linear system. The process and measurement noise covariance matrices are tuning parameters of the Kalman filter and they affect quality of the state estimation. The noise covariance matrices are generally not known and their estimation from the measured data is a challenging task. This paper introduces a method for estimation of the noise covariance matrices using Bayesian approach along with Monte Carlo numerical methods. Performance of the approach is tested on various systems and noise properties. The second part of the paper compares Monte Carlo approach to the recently published methods. The speed of convergence is compared to the Cramér-Rao bounds.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BC - Teorie a systémy řízení

OECD FORD obor

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Projekt

<a href="/cs/project/GAP103%2F11%2F1353" target="_blank" >GAP103/11/1353: Odhad stavu dynamických stochastických systémů</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

International Journal of Adaptive Control and Signal Processing

ISSN

0890-6327

e-ISSN

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Svazek periodika

27

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

17

Strana od-do

957-973

Kód UT WoS článku

000326031500003

EID výsledku v databázi Scopus

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