Rao-Blackwellised Point-Mass Smoothers for a Class of Conditionally Linear Dynamic Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956253" target="_blank" >RIV/49777513:23520/19:43956253 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9011246" target="_blank" >https://ieeexplore.ieee.org/document/9011246</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Rao-Blackwellised Point-Mass Smoothers for a Class of Conditionally Linear Dynamic Models
Original language description
The paper deals with the state estimation of nonlinear stochastic dynamic systems. The stress is laid on the numerical solution to the Bayes’ rule considering a class of conditionally linear Gaussian models typically appearing in navigation. In particular, three novel Rao-Blackwellised smoothers are proposed, where the nonlinear part of the model is solved by a computationally expensive point-mass smoother, whereas the conditionally linear part is solved by a set of linear smoothers. The proposed smoothers offer a tradeoff between the computational complexity and smoothing performance. The properties of the smoothers are theoretically analysed and discussed.
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/LO1506" target="_blank" >LO1506: Sustainability support of the centre NTIS - New Technologies for the Information Society</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
2019
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 2019 22th International Conference on Information Fusion (FUSION)
ISBN
978-0-9964527-8-6
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Ottawa
Event location
Ottawa, Kanada
Event date
Jul 2, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000567728800089