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oai:www.ideals.illinois.edu:2142/49437 2023-07-11 col_2142_5131 col_2142_14787 com_2142_5130 com_2142_9630 com_2142_234

Mehta, Prashant G. Ghiotto, Shane 2014-05-30T16:44:03Z 2014-05-30T16:44:03Z 2014-05 2014-05-30T16:44:03Z 2014-05 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. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2014-04-24T19:29:05Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Ghiotto_Shane.pdf: 1605485 bytes, checksum: 184c958ed3ed8235d089576fa09c24c1 (MD5) Made available in DSpace on 2014-05-30T16:44:03Z (GMT). No. of bitstreams: 2 Shane_Ghiotto.pdf: 1605498 bytes, checksum: a599f1cfc01e6c467dc905b4f4e1be4b (MD5) license.txt: 4063 bytes, checksum: b6df58830014ec29d909dce114169ed6 (MD5) http://hdl.handle.net/2142/49437 en Copyright 2014 Shane Ghiotto Filtering state estimation particle filtering Kalman filter feedback particle filter Comparison of nonlinear filtering techniques text Mechanical Sci & Engineering 1917 Mechanical Engineering 0133 University of Illinois at Urbana-Champaign Thesis M.S. MS:Mechanical Engineerng -UIUC 10KS0133MS