Noise covariances estimation for Kalman filter tuning
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00171475" target="_blank" >RIV/68407700:21230/10:00171475 - isvavai.cz</a>
Výsledek na webu
<a href="http://dx.doi.org/10.3182/20100826-3-TR-4015.00009" target="_blank" >http://dx.doi.org/10.3182/20100826-3-TR-4015.00009</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3182/20100826-3-TR-4015.00009" target="_blank" >10.3182/20100826-3-TR-4015.00009</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Noise covariances estimation for Kalman filter tuning
Popis výsledku v původním jazyce
Kalman filter tuning is based on process and measurement noise covariances that are parameters of Riccati equation. Based on the Riccati equation solution, Kalman gain is calculated and used for state estimator. Noise covariances are generally not known.The latest methods and their modifications were published in 2005 and later. In many parts of technical science the Bayesian approach can be used for various estimation problems. However, many scientists and researchers a priori consider Bayesian principles to be unpractical because in most cases it is very difficult to work with probabilities or likelihood functions. The probability or likelihood functions cannot be solved analytically for most problems. In this paper, we will discuss the performanceof some published methods and compare them with the maximum likelihood approach using numerical methods. Properties of different approaches and qualities of maximum likelihood method will be demonstrated.
Název v anglickém jazyce
Noise covariances estimation for Kalman filter tuning
Popis výsledku anglicky
Kalman filter tuning is based on process and measurement noise covariances that are parameters of Riccati equation. Based on the Riccati equation solution, Kalman gain is calculated and used for state estimator. Noise covariances are generally not known.The latest methods and their modifications were published in 2005 and later. In many parts of technical science the Bayesian approach can be used for various estimation problems. However, many scientists and researchers a priori consider Bayesian principles to be unpractical because in most cases it is very difficult to work with probabilities or likelihood functions. The probability or likelihood functions cannot be solved analytically for most problems. In this paper, we will discuss the performanceof some published methods and compare them with the maximum likelihood approach using numerical methods. Properties of different approaches and qualities of maximum likelihood method will be demonstrated.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
BC - Teorie a systémy řízení
OECD FORD obor
—
Návaznosti výsledku
Projekt
<a href="/cs/project/GA102%2F08%2F0442" target="_blank" >GA102/08/0442: Spočitatelné aproximace duálních strategií řízení</a><br>
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2010
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů