A minimum relative entropy-based density selection scheme for bayesian estimations of energy-related problems

Xiaoran Shi, Nurcin Celik

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Particle filtering procedures are widely used in Bayesian state estimation of large-scale dynamic systems given their massive datasets. However, they may end up in a situation called degeneracy, where a single particle abruptly possesses significant amount of normalized weights. An efficacious selection of the importance sampling density plays a pivotal role in achieving good performance of the particle filters by preventing the sampling procedure from generating degenerated weights for particles at early stages of the process. Such a selection of a proposal distribution that takes into account both the transition prior and the likelihood becomes especially crucial for better estimation accuracy when the observation data has significant impacts on the posterior states. In this study, we propose a novel importance density selection structure for particle filters based on the minimized relative entropy principle. First, the theoretical derivation of the proposed structure is presented. Then, the proposed scheme is benchmarked against other sampling schemes that exist in the literature for state estimation in terms of their estimation qualities and computational efficiencies through a set of synthetic experiments. Finally, various energy-related estimation problems that match the applicable circumstances of the proposed scheme are briefly discussed.

Original languageEnglish
Title of host publication62nd IIE Annual Conference and Expo 2012
PublisherInstitute of Industrial Engineers
Pages769-778
Number of pages10
StatePublished - Jan 1 2012
Event62nd IIE Annual Conference and Expo 2012 - Orlando, FL, United States
Duration: May 19 2012May 23 2012

Other

Other62nd IIE Annual Conference and Expo 2012
CountryUnited States
CityOrlando, FL
Period5/19/125/23/12

Keywords

  • Importance density selection
  • Importance sampling
  • Relative entropy
  • Sequential monte carlo methods

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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