Cramér-Rao Bounds for Estimation of Linear System Noise Covariances

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F12%3A00192714" target="_blank" >RIV/68407700:21230/12:00192714 - isvavai.cz</a>
Result on the web
<a href="http://article.sapub.org/10.5923.j.jmea.20120202.02.html" target="_blank" >http://article.sapub.org/10.5923.j.jmea.20120202.02.html</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5923/j.jmea.20120202.02" target="_blank" >10.5923/j.jmea.20120202.02</a>

Result language
angličtina
Original language name
Cramér-Rao Bounds for Estimation of Linear System Noise Covariances
Original language description
The performance of Kalman filter depends directly on the noise covariances, which are usually not known and need to be estimated. Several estimation algorithms have been published in past decades, but the measure of estimation quality is missing. The Cramér-Rao bounds represent limitation of quality of parameter estimation that can be obtained from given data. In this article, The Cramér-Rao bounds for noise covariance estimation of linear time-invariant stochastic system will be derived. Two differentlinear system models will be considered. Further, the performance of earlier published methods will be discussed according to the Cramér-Rao bounds. The analogy between the Cramér-Rao bounds and the Riccati equation will be pointed out.
Czech name
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Czech description
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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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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)

Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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