Data assimilation in the detection of vortices

Andrea Barreiro, Shanshan Liu, N. Sri Namachchivaya, Peter W. Sauer, Richard B. Sowers

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

We develop new algorithms for target detection in multi-sensor environments. These methods are applied to study point vortex motion based on Lagrangian tracer information. First we solve analytically the nonlinear filtering problem for the special case of equal strength vortices. Recently developed methods, the particle filters that are based on importance sampling Monte Carlo simulations, are used for the detection of vortices in the the general case. Unlike the well-known extended Kalman filter, it is applicable to highly nonlinear systems with non-Gaussian uncertainties.

Original languageEnglish (US)
Title of host publicationApplications of Nonlinear Dynamics
Subtitle of host publicationModel and Design of Complex Systems
Pages47-59
Number of pages13
DOIs
StatePublished - Mar 27 2009

Publication series

NameUnderstanding Complex Systems
Volume2009
ISSN (Print)1860-0832
ISSN (Electronic)1860-0840

ASJC Scopus subject areas

  • Software
  • Computational Mechanics
  • Artificial Intelligence

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  • Cite this

    Barreiro, A., Liu, S., Sri Namachchivaya, N., Sauer, P. W., & Sowers, R. B. (2009). Data assimilation in the detection of vortices. In Applications of Nonlinear Dynamics: Model and Design of Complex Systems (pp. 47-59). (Understanding Complex Systems; Vol. 2009). https://doi.org/10.1007/978-3-540-85632-0_5