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oai:www.ideals.illinois.edu:2142/50584 2023-07-11 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Mehta, Prashant G. Medarametla, Krishna Kalyan 2014-09-16T17:24:08Z 2014-09-16T17:24:08Z 2014-08 2014-09-16 2014-08 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-07-16T14:07:46Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Medarametla_Krishna Kalyan.pdf: 877748 bytes, checksum: ec1624b2c9f339668e7b8886f0361cfd (MD5) Made available in DSpace on 2014-09-16T17:24:08Z (GMT). No. of bitstreams: 2 Krishna Kalyan_Medarametla.pdf: 877748 bytes, checksum: ec1624b2c9f339668e7b8886f0361cfd (MD5) license.txt: 4076 bytes, checksum: a6ffacc1650105678d12e0b5b72d9404 (MD5) http://hdl.handle.net/2142/50584 en Copyright 2014 Krishna Kalyan Medarametla Extended Kalman filter Feedback particle filter Comparison Nonlinear filtering Comparison of two nonlinear filtering techniques - the extended Kalman filter and the feedback particle filter text Mechanical Sci & Engineering 1917 Mechanical Engineering 0133 University of Illinois at Urbana-Champaign Thesis M.S. MS:Mechanical Engineerng -UIUC 10KS0133MS