Noise covariances estimation for Kalman filter tuning

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Noise covariances estimation for Kalman filter tuning
Original language description
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.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA102%2F08%2F0442" target="_blank" >GA102/08/0442: Feasible approximations of dual control strategies</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)