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Title:Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter
Author(s):Medarametla, Krishna Kalyan
Advisor(s):Mehta, Prashant G.
Department / Program:Mechanical Sci & Engineering
Discipline:Mechanical Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(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.
Issue Date:2014-09-16
URI:http://hdl.handle.net/2142/50584
Rights Information:Copyright 2014 Krishna Kalyan Medarametla
Date Available in IDEALS:2014-09-16
Date Deposited:2014-08


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