Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Noise covariance estimation for Kalman filter tuning using Bayesian approach and Monte Carlo
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BC - Theory and management systems
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GAP103%2F11%2F1353" target="_blank" >GAP103/11/1353: State Estimation of Dynamic Stochastic Systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Adaptive Control and Signal Processing
ISSN
0890-6327
e-ISSN
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Volume of the periodical
27
Issue of the periodical within the volume
11
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
957-973
UT code for WoS article
000326031500003
EID of the result in the Scopus database
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