Grid-based Bayesian Filters with Functional 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_____%2F23%3A00568617" target="_blank" >RIV/67985556:_____/23:00568617 - isvavai.cz</a>
Alternative codes found
RIV/49777513:23520/23:43969683
Result on the web
<a href="https://ieeexplore.ieee.org/document/10035470" target="_blank" >https://ieeexplore.ieee.org/document/10035470</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TSP.2023.3240359" target="_blank" >10.1109/TSP.2023.3240359</a>
Alternative languages
Result language
angličtina
Original language name
Grid-based Bayesian Filters with Functional Decomposition of Transient Density
Original language description
The paper deals with the state estimation of nonlinear stochastic dynamic systems with special attention to grid-based Bayesian filters such as the point-mass filter (PMF) and the marginal particle filter (mPF). In the paper, a novel functional decomposition of the transient density describing the system dynamics is proposed. The decomposition approximates the transient density in a closed region. It is based on a non-negative matrix/tensor factorization and separates the density into functions of the future and current states. Such decomposition facilitates a thrifty calculation of the convolution involving the density, which is a performance bottleneck of the standard PMF/mPF implementations. The estimate quality and computational costs can be efficiently controlled by choosing an appropriate decomposition rank. The performance of the PMF with the transient density decomposition is illustrated in a terrain-aided navigation scenario and a problem involving a univariate non-stationary growth model.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
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
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Signal Processing
ISSN
1053-587X
e-ISSN
1941-0476
Volume of the periodical
71
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
13
Pages from-to
92-104
UT code for WoS article
000935455200003
EID of the result in the Scopus database
2-s2.0-85148417650