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)
Filtering
state estimation
particle filtering
Kalman filter
feedback particle filter
Abstract
In a recent work it is shown that importance sampling can be avoided in the
particle filter through an innovation structure inspired by traditional nonlinear
filtering combined with optimal control formalisms. The resulting algorithm is
referred to as feedback particle filter.
The purpose of this thesis is to provide a comparative study of the feedback
particle filter (FPF). Two types of comparisons are discussed: i) with the extended
Kalman filter, and ii) with the conventional resampling-based particle filters. The
comparison with Kalman filter is used to highlight the feedback structure of the
FPF. Also computational cost estimates are discussed, in terms of number of op-
erations relative to EKF. Comparison with the conventional particle filtering ap-
proaches is based on a numerical example taken from the survey article on the
topic of nonlinear filtering. Comparisons are provided for both computational
cost and accuracy.
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