Reconstruction of Cross-Correlations with Constant Number of Deterministic Samples

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F18%3A43954763" target="_blank" >RIV/49777513:23520/18:43954763 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.23919/ICIF.2018.8455221" target="_blank" >http://dx.doi.org/10.23919/ICIF.2018.8455221</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/ICIF.2018.8455221" target="_blank" >10.23919/ICIF.2018.8455221</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Reconstruction of Cross-Correlations with Constant Number of Deterministic Samples

Popis výsledku v původním jazyce

Optimal fusion of estimates that are computed in a distributed fashion is a challenging task. In general, the sensor nodes cannot keep track of the cross-correlations required to fuse estimates optimally. In this paper, a novel technique is presented that provides the means to reconstruct the required correlation structure. For this purpose, each node computes a set of deterministic samples that provides all the information required to reassemble the cross-covariance matrix for each pair of estimates. As the number of samples is increasing over time, a method to reduce the size of the sample set is presented and studied. In doing so, communication expenses can be reduced significantly, but approximation errors are possibly introduced by neglecting past correlation terms. In order to keep approximation errors at a minimum, an appropriate set size can be determined and a trade-off between communication expenses and estimation quality can be found.

Název v anglickém jazyce

Reconstruction of Cross-Correlations with Constant Number of Deterministic Samples

Popis výsledku anglicky

Optimal fusion of estimates that are computed in a distributed fashion is a challenging task. In general, the sensor nodes cannot keep track of the cross-correlations required to fuse estimates optimally. In this paper, a novel technique is presented that provides the means to reconstruct the required correlation structure. For this purpose, each node computes a set of deterministic samples that provides all the information required to reassemble the cross-covariance matrix for each pair of estimates. As the number of samples is increasing over time, a method to reduce the size of the sample set is presented and studied. In doing so, communication expenses can be reduced significantly, but approximation errors are possibly introduced by neglecting past correlation terms. In order to keep approximation errors at a minimum, an appropriate set size can be determined and a trade-off between communication expenses and estimation quality can be found.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 2018 21st International Conference on Information Fusion (FUSION)

ISBN

978-0-9964527-6-2

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

8

Strana od-do

1638-1645

Název nakladatele

IEEE

Místo vydání

neuveden

Místo konání akce

Cambridge, UK

Datum konání akce

10. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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