Conditional Density Driven Grid Design in Point-Mass Filter

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Conditional Density Driven Grid Design in Point-Mass Filter

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Conditional Density Driven Grid Design in Point-Mass Filter

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

5

Strana od-do

9180-9184

Název nakladatele

IEEE

Místo vydání

Barcelona

Místo konání akce

Barcelona, Španělsko

Datum konání akce

4. 5. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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