Data Assimilation by Conditioning of Driving Noise on Future Observations

Handle URI:
http://hdl.handle.net/10754/597917
Title:
Data Assimilation by Conditioning of Driving Noise on Future Observations
Authors:
Lee, Wonjung; Farmer, Chris
Abstract:
Conventional recursive filtering approaches, designed for quantifying the state of an evolving stochastic dynamical system with intermittent observations, use a sequence of i) an uncertainty propagation step followed by ii) a step where the associated data is assimilated using Bayes' rule. Alternatively, the order of the steps can be switched to i) one step ahead data assimilation followed by ii) uncertainty propagation. In this paper, we apply this smoothing-based sequential filter to systems driven by random noise, however with the conditioning on future observation not only to the system variable but to the driving noise. Our research reveals that, for the nonlinear filtering problem, the conditioned driving noise is biased by a nonzero mean and in turn pushes forward the filtering solution in time closer to the true state when it drives the system. As a result our proposed method can yield a more accurate approximate solution for the state estimation problem. © 1991-2012 IEEE.
Citation:
Lee W, Farmer C (2014) Data Assimilation by Conditioning of Driving Noise on Future Observations. IEEE Trans Signal Process 62: 3887–3896. Available: http://dx.doi.org/10.1109/TSP.2014.2330807.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Signal Processing
KAUST Grant Number:
KUK-C1-013-04
Issue Date:
Aug-2014
DOI:
10.1109/TSP.2014.2330807
Type:
Article
ISSN:
1053-587X; 1941-0476
Sponsors:
This work was supported by King Abdullah University of Science and Technology (KAUST) Award No. KUK-C1-013-04.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorLee, Wonjungen
dc.contributor.authorFarmer, Chrisen
dc.date.accessioned2016-02-25T12:58:51Zen
dc.date.available2016-02-25T12:58:51Zen
dc.date.issued2014-08en
dc.identifier.citationLee W, Farmer C (2014) Data Assimilation by Conditioning of Driving Noise on Future Observations. IEEE Trans Signal Process 62: 3887–3896. Available: http://dx.doi.org/10.1109/TSP.2014.2330807.en
dc.identifier.issn1053-587Xen
dc.identifier.issn1941-0476en
dc.identifier.doi10.1109/TSP.2014.2330807en
dc.identifier.urihttp://hdl.handle.net/10754/597917en
dc.description.abstractConventional recursive filtering approaches, designed for quantifying the state of an evolving stochastic dynamical system with intermittent observations, use a sequence of i) an uncertainty propagation step followed by ii) a step where the associated data is assimilated using Bayes' rule. Alternatively, the order of the steps can be switched to i) one step ahead data assimilation followed by ii) uncertainty propagation. In this paper, we apply this smoothing-based sequential filter to systems driven by random noise, however with the conditioning on future observation not only to the system variable but to the driving noise. Our research reveals that, for the nonlinear filtering problem, the conditioned driving noise is biased by a nonzero mean and in turn pushes forward the filtering solution in time closer to the true state when it drives the system. As a result our proposed method can yield a more accurate approximate solution for the state estimation problem. © 1991-2012 IEEE.en
dc.description.sponsorshipThis work was supported by King Abdullah University of Science and Technology (KAUST) Award No. KUK-C1-013-04.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectBayesian statisticsen
dc.subjectcubature measureen
dc.subjectGaussian approximation filteren
dc.titleData Assimilation by Conditioning of Driving Noise on Future Observationsen
dc.typeArticleen
dc.identifier.journalIEEE Transactions on Signal Processingen
dc.contributor.institutionUniversity of Oxford, Oxford, United Kingdomen
kaust.grant.numberKUK-C1-013-04en
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