On Visualisation of Linear Estimation and Fusion: From Equations to Ellipses
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969665" target="_blank" >RIV/49777513:23520/23:43969665 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/SDF-MFI59545.2023.10361360" target="_blank" >https://doi.org/10.1109/SDF-MFI59545.2023.10361360</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SDF-MFI59545.2023.10361360" target="_blank" >10.1109/SDF-MFI59545.2023.10361360</a>
Alternative languages
Result language
angličtina
Original language name
On Visualisation of Linear Estimation and Fusion: From Equations to Ellipses
Original language description
Visualisation of mathematical objects often leads to faster and easier comprehension of theories. On the other hand, deriving conclusions exclusively from a graphical interpretation can be misleading. A typical case in estimation is an ellipse, which corresponds to contour lines of a bivariate Gaussian density. This paper combines insights from various areas. Algebraic relations are presented first, geometric objects are shown subsequently. Constructions of ellipses by determining radii and positions of tangent lines are discussed. The stress is laid on exposition of linear estimation with a focus on methodology of fusion of estimates, especially in the case when cross-correlations of estimation errors are not fully known. The exposition also covers multidimensional variables, where the ellipses become ellipsoids.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2023 IEEE Symposium Sensor Data Fusion and International Conference on Multisensor Fusion and Integration (SDF-MFI)
ISBN
979-8-3503-8258-7
ISSN
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e-ISSN
2767-9357
Number of pages
6
Pages from-to
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Publisher name
IEEE
Place of publication
Bonn, Německo
Event location
Bonn, Německo
Event date
Nov 27, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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