Comparison of nonlinear filtering techniques

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mechanical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2014

Author dc:creator

• Ghiotto, Shane

Contributors dc:contributor

• Mehta, Prashant G.

• Filtering
• state estimation
• particle filtering
• Kalman filter
• feedback particle filter

Statement dc:rights

• Copyright 2014 Shane Ghiotto

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/49437

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/49437