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GBAS heavy-tail error overbounding with GARCH model

  • Aviation Data Communication Corporation

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

To reduce the inflation for statistical uncertainty and describe the real error distribution objectively, generalized autoregressive conditional heteroskedasticity (GARCH) model is utilized in this paper to model and overbound ground based augmentation system (GBAS) heavy-tail errors. Based on the GARCH model, heavy-tail errors are normalized to the standard Gaussian distribution, and error samples from all elevations are mixed together to calculate overbound without being grouped. By this means, compared with classic error distribution models, the heavy-tail errors are overbounded more tightly, and the calculated inflation factors, error confidence limits in pseudorange domain and protection levels in position domain are reduced correspondingly.

Original languageEnglish
Title of host publicationIEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems
EditorsFei Gao, Zan Li, Daniel Cascado Caballero, Jing Fan, Mohammad S. Obaidat, Petros Nicoploitidis, Kuei Fang Hsiao
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509034406
DOIs
StatePublished - 16 Aug 2016
Event2016 International Conference on Computer, Information and Telecommunication Systems, CITS 2016 - Kunming, China
Duration: 6 Jul 20168 Jul 2016

Publication series

NameIEEE CITS 2016 - 2016 International Conference on Computer, Information and Telecommunication Systems

Conference

Conference2016 International Conference on Computer, Information and Telecommunication Systems, CITS 2016
Country/TerritoryChina
CityKunming
Period6/07/168/07/16

Keywords

  • error overbound
  • generalized autoregressive conditional heteroskedasticity (GARCH) model
  • ground based augmentation system (GBAS)
  • inflation factor
  • protection level

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