Conditional Density Driven Grid Design in Point-Mass Filter
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959473" target="_blank" >RIV/49777513:23520/20:43959473 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICASSP40776.2020.9052962" target="_blank" >https://doi.org/10.1109/ICASSP40776.2020.9052962</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP40776.2020.9052962" target="_blank" >10.1109/ICASSP40776.2020.9052962</a>
Alternative languages
Result language
angličtina
Original language name
Conditional Density Driven Grid Design in Point-Mass Filter
Original language description
The paper is devoted to the state estimation of nonlinear stochastic dynamic systems. The stress is laid on a grid-based numerical solution to the Bayesian recursive relations using the point-mass filter (PMF). In the paper, a novel conditional density driven grid (CDDG) design is proposed. The CDDG design takes advantage of non-equidistant grid points by combination of two grids; dense and sparse. The dense grid is designed to cover the state space region, where the significant mass of one or both conditional (i.e., predictive and filtering) densities is anticipated. The sparse grid covers the support of the conditional distribution tails only. As a consequence, the CDDG design improves the point-mass approximation of the conditional densities and offers better estimation performance compared to the standard equidistant grid with the same number of points and, thus, with the same computational complexity. Performance of the CDDG-based PMF 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
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
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 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN
978-1-5090-6631-5
ISSN
1520-6149
e-ISSN
2379-190X
Number of pages
5
Pages from-to
9180-9184
Publisher name
IEEE
Place of publication
Barcelona
Event location
Barcelona, Španělsko
Event date
May 4, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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