Skip navigation
Skip navigation

Modeling Geodetic Processes with Levy a-Stable Distribution and FARIMA

Montillet, Jean-Philippe; Yu, Kegen

Description

Over the last years the scientific community has been using the autoregressive moving average (ARMA) model in the modeling of the noise in global positioning system (GPS) time series (daily solution). This work starts with the investigation of the limit of the ARMA model which is widely used in signal processing when the measurement noise is white. Since a typical GPS time series consists of geophysical signals (e.g., seasonal signal) and stochastic processes (e.g., coloured and white noise),...[Show more]

dc.contributor.authorMontillet, Jean-Philippe
dc.contributor.authorYu, Kegen
dc.date.accessioned2016-02-24T22:40:53Z
dc.identifier.issn1874-8961
dc.identifier.urihttp://hdl.handle.net/1885/98480
dc.description.abstractOver the last years the scientific community has been using the autoregressive moving average (ARMA) model in the modeling of the noise in global positioning system (GPS) time series (daily solution). This work starts with the investigation of the limit of the ARMA model which is widely used in signal processing when the measurement noise is white. Since a typical GPS time series consists of geophysical signals (e.g., seasonal signal) and stochastic processes (e.g., coloured and white noise), the ARMA model may be inappropriate. Therefore, the application of the fractional auto-regressive integrated moving average (FARIMA) model is investigated. The simulation results using simulated time series as well as real GPS time series from a few selected stations around Australia show that the FARIMA model fits the time series better than other models when the coloured noise is larger than the white noise. The second fold of this work focuses on fitting the GPS time series with the family of Levy $$\alpha $$α-stable distributions. Using this distribution, a hypothesis test is developed to eliminate effectively coarse outliers from GPS time series, achieving better performance than using the rule of thumb of $$n$$n standard deviations (with $$n$$n chosen empirically).
dc.publisherSpringer Verlag
dc.sourceMathematical Geosciences
dc.titleModeling Geodetic Processes with Levy a-Stable Distribution and FARIMA
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume47
dc.date.issued2015
local.identifier.absfor090902 - Geodesy
local.identifier.ariespublicationU3488905xPUB5516
local.type.statusPublished Version
local.contributor.affiliationMontillet, Jean-Philippe, College of Physical and Mathematical Sciences, ANU
local.contributor.affiliationYu, Kegen, Wuhan University
local.description.embargo2037-12-31
local.bibliographicCitation.issue6
local.bibliographicCitation.startpage627
local.bibliographicCitation.lastpage646
local.identifier.doi10.1007/s11004-014-9574-6
dc.date.updated2016-02-24T10:07:35Z
local.identifier.scopusID2-s2.0-84937407632
CollectionsANU Research Publications

Download

File Description SizeFormat Image
01_Montillet_Modeling_Geodetic_Processes_2015.pdf2.87 MBAdobe PDF    Request a copy


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator