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A Theoretical Framework for Sequential Importance Sampling with Resampling

  • Jun S. Liu
  • Rong Chen
  • Tanya Logvinenko
Chapter
Part of the Statistics for Engineering and Information Science book series (ISS)

Abstract

Monte Carlo filters (MCF) can be loosely defined as a set of methods that use Monte Carlo simulation to solve on-line estimation and prediction problems in a dynamic system. Compared with traditional filtering methods, simple, flexible — yet powerful — MCF techniques provide effective means to overcome computational difficulties in dealing with nonlinear dynamic models. One key element of MCF techniques is the recursive use of the importance sampling principle, which leads to the more precise name sequential importance sampling (SIS) for the techniques that are to be the focus of this article.

Keywords

Kalman Filter Importance Sampling Target Tracking Importance Weight Nonlinear Dynamic Model 
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 Science+Business Media New York 2001

Authors and Affiliations

  • Jun S. Liu
  • Rong Chen
  • Tanya Logvinenko

There are no affiliations available

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