Distributed Point-Mass Filter with Reduced Data Transfer Using Copula Theory
Result description
This paper deals with distributed Bayesian state estimation of generally nonlinear stochastic dynamic systems. In particular, distributed point-mass filter algorithm is developed. It is comprised of a basic part that is accurate but data intense and optional step employing advanced copula theory. The optional step significantly reduces data transfer for the price of a small accuracy decrease. In the end, the developed algorithm is numerically compared to the usually employed distributed extended Kalman filter.
Keywords
data reductioncovariance intersectionpoint-mass filterDistributed estimation
The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
Alternative languages
Result language
angličtina
Original language name
Distributed Point-Mass Filter with Reduced Data Transfer Using Copula Theory
Original language description
This paper deals with distributed Bayesian state estimation of generally nonlinear stochastic dynamic systems. In particular, distributed point-mass filter algorithm is developed. It is comprised of a basic part that is accurate but data intense and optional step employing advanced copula theory. The optional step significantly reduces data transfer for the price of a small accuracy decrease. In the end, the developed algorithm is numerically compared to the usually employed distributed extended Kalman filter.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
EF19_073/0016931: Improving the quality of the internal grant structure at UWB
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
S - Specificky vyzkum na vysokych skolach
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
Article name in the collection
Proceedings of the 2023 American Control Conference
ISBN
979-8-3503-2806-6
ISSN
0743-1619
e-ISSN
2378-5861
Number of pages
6
Pages from-to
1649-1654
Publisher name
IEEE
Place of publication
San Diego, CA, USA
Event location
San Diego, CA, USA
Event date
May 31, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001027160301078
Basic information
Result type
D - Article in proceedings
OECD FORD
Automation and control systems
Year of implementation
2023