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Nonlinear Filtering Using Sparse Grids

  • Carolyn Kalender
  • Alfred Schöttl
Conference paper

Abstract

This paper presents a new nonlinear filtering algorithm applicable in realtime. Nonlinear filtering problems are mostly solved with the Extended Kalman Filter which due to the nonlinearities is a suboptimal estimator. Optimal estimates are provided by Fokker-Planck-Equation in combination with Bayes rule. Conventional approaches for the numerical solution of this equation suffer from the “curse of dimension” and are therefore not applicable in higher dimensions. We use sparse grids for solving the Fokker-Planck-Equation and present a six dimensional nonlinear problem solved in real-time with this new approach.

Keywords

Extend Kalman Filter Sparse Grid Shift Vector Nonlinear Filter Interpolation Accuracy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Carolyn Kalender
    • 1
  • Alfred Schöttl
    • 1
  1. 1.LFK-Lenkflugkörpersysteme GmbHUnterschleißheimGermany

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