Efficient Implementation of Marginal Particle Filter by Functional Density Decomposition

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966079" target="_blank" >RIV/49777513:23520/22:43966079 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION49751.2022.9841367" target="_blank" >https://doi.org/10.23919/FUSION49751.2022.9841367</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION49751.2022.9841367" target="_blank" >10.23919/FUSION49751.2022.9841367</a>

Result language
angličtina
Original language name
Efficient Implementation of Marginal Particle Filter by Functional Density Decomposition
Original language description
The paper considers the solution to the state estimation problem of nonlinear dynamic stochastic systems by the particle filters. It focuses on the marginal particle filter algorithms which generate samples directly in the marginal space for the recent state. Their standard implementation calculates the sample weights by combining the samples from two consecutive time instants in the transition and proposal density function evaluations. This results in computational complexity quadratic in sample size. The paper proposes an efficient implementation of the marginal particle filter for which a functional tensor decomposition of the transition and proposal densities is calculated. The computational complexity of the proposed implementation is linear in sample size and the decomposition rank can be used to achieve a trade-off between accuracy and computational costs. The balance between the complexity and the estimate quality can be tuned by selecting the rank of the decomposition. The proposed implementation is demonstrated using two numerical examples with a univariate non-stationary growth model and terrain-aided navigation scenario.
Czech name
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Czech description
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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
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 25th International Conference on Information Fusion, FUSION 2022
ISBN
978-1-73774-972-1
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Linköping, Sweden
Event location
Linköping, Sweden
Event date
Jul 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000855689000167

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