Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter
Medarametla, Krishna Kalyan
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https://hdl.handle.net/2142/50584
Description
Title
Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter
Author(s)
Medarametla, Krishna Kalyan
Issue Date
2014-09-16
Director of Research (if dissertation) or Advisor (if thesis)
Mehta, Prashant G.
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
Extended Kalman filter
Feedback particle filter
Comparison
Nonlinear filtering
Abstract
In a recent work it has been shown that importance sampling can be avoided in particle filter through an innovation structure inspired by traditional nonlinear filtering combined with optimal control and mean-field game formalisms. The
resulting algorithm is referred to as feedback particle filter (FPF).
The purpose of this thesis is to provide a comparative study of the feedback particle filter (FPF) with the extended Kalman filter (EKF) for a scalar filtering
problem which has linear signal dynamics and nonlinear observation dynamics. Different parameters of the signal model and observation model will be varied and performance of the two filtering techniques FPF, EKF will be compared.
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