Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973056" target="_blank" >RIV/49777513:23520/24:43973056 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION59988.2024.10706476" target="_blank" >https://doi.org/10.23919/FUSION59988.2024.10706476</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION59988.2024.10706476" target="_blank" >10.23919/FUSION59988.2024.10706476</a>
Alternative languages
Result language
angličtina
Original language name
Stochastic Integration Based Estimator: Robust Design and Stone Soup Implementation
Original language description
This paper deals with state estimation of nonlinear stochastic dynamic models. In particular, the stochastic integration rule, which provides asymptotically unbiased estimates of the moments of nonlinearly transformed Gaussian random variables, is reviewed together with the recently introduced stochastic integration filter (SIF). Using SIF, the respective multi-step prediction and smoothing algorithms are developed in full and efficient square-root form. The stochastic-integration-rule-based algorithms are implemented in Python (within the Stone Soup framework) and in MATLAB® and are numerically evaluated and compared with the well-known unscented and extended Kalman filters using the Stone Soup defined tracking scenario.
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
<a href="/en/project/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 27th International Conference on Information Fusion (FUSION)
ISBN
978-1-73774-976-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
IEEE
Place of publication
Venice
Event location
Venice, Italy
Event date
Jul 7, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001334560000204