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Unscented kalman filter/smoother for a CBRN puff-based dispersion model

  • SUNY Buffalo

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

26 Scopus citations

Abstract

Fixed interval smoothing for systems with nonlinear process and measurement models is studied and applied to the assimilation of sensor data in a Chemical, Biological, Radiological or Nuclear (CBRN) incident scenario. A two-filter smoother that uses a Backward Sigma-Point Information Filter, and also a forward-backward Rauch-Tung-Striebel (RTS) smoothing form are re-derived using the weighted statistical linearization concept. Both methods are derived in the context of the Unscented Kalman Filter. The square root version of the resulting RTS Unscented Kalman Filter / Smoother is applied to a CBRN dispersion puff-based model with variable state dimension, and the data assimilation performance of the method is compared with a Particle Filter implementation.

Original languageEnglish
Title of host publicationFUSION 2007 - 2007 10th International Conference on Information Fusion
DOIs
StatePublished - 2007
EventFUSION 2007 - 2007 10th International Conference on Information Fusion - Quebec, QC, Canada
Duration: Jul 9 2007Jul 12 2007

Publication series

NameFUSION 2007 - 2007 10th International Conference on Information Fusion

Conference

ConferenceFUSION 2007 - 2007 10th International Conference on Information Fusion
Country/TerritoryCanada
CityQuebec, QC
Period07/9/0707/12/07

Keywords

  • Chemical dispersion
  • Data assimilation
  • Puff-based model
  • Sigma-point filtering
  • Unscented kalman smoother
  • Variable state dimension

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