Point-Mass Filter with Decomposition of Transient Density
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F22%3A00559994" target="_blank" >RIV/67985556:_____/22:00559994 - isvavai.cz</a>
Alternative codes found
RIV/49777513:23520/22:43966076
Result on the web
<a href="http://dx.doi.org/10.1109/ICASSP43922.2022.9747607" target="_blank" >http://dx.doi.org/10.1109/ICASSP43922.2022.9747607</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP43922.2022.9747607" target="_blank" >10.1109/ICASSP43922.2022.9747607</a>
Alternative languages
Result language
angličtina
Original language name
Point-Mass Filter with Decomposition of Transient Density
Original language description
The paper deals with the state estimation of nonlinear stochastic dynamic systems with special attention on a grid-based numerical solution to the Bayesian recursive relations, the point-mass filter (PMF). In the paper, a novel functional decomposition of the transient density describing the system dynamics is proposed. The decomposition is based on a non-negative matrix factorization and separates the density into functions of the future and current states. Such decomposition facilitates a thrifty calculation of the convolution, which is a bottleneck of the PMF performance. The PMF estimate quality and computational costs can be efficiently controlled by choosing an appropriate rank of the decomposition. The performance of the PMF with the transient density decomposition is illustrated in a terrain-aided navigation 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/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing
ISBN
978-1-6654-0540-9
ISSN
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e-ISSN
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Number of pages
5
Pages from-to
5752-5756
Publisher name
IEEE
Place of publication
Singapore
Event location
Singapur
Event date
May 22, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000864187906010