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Title:Comparison of nonlinear filtering techniques
Author(s):Ghiotto, Shane
Advisor(s):Mehta, Prashant G.
Department / Program:Mechanical Sci & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
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.
Issue Date:2014-05-30
Rights Information:Copyright 2014 Shane Ghiotto
Date Available in IDEALS:2014-05-30
Date Deposited:2014-05

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