On improving the performance of the Gauss-Newton filter

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dc.contributor.advisorInggs, Michaelen_ZA
dc.contributor.authorNadjiasngar, Roaldjeen_ZA
dc.date.accessioned2014-07-31T10:54:25Z
dc.date.available2014-07-31T10:54:25Z
dc.date.issued2013en_ZA
dc.descriptionIncludes abstract.
dc.descriptionIncludes bibliographical references.
dc.description.abstractThe Gauss-Newton filter is a tracking filter developed by Norman Morrison around the same time as the celebrated Kalman filter. It received little attention, primarily due to the computation requirements at the time. Today computers have vast processing capacity and computation is no-longer an issue. The filter finite memory length is identified as the key element in the Gauss-Newton filter adaptability and robustness. This thesis focuses on improving the performance of the Gauss-Newton. We incorporate the process noise statistics into the filter algorithm to obtain a filter which explains the error covariance inconsistency of the Kalaman filter. In addition, a biased version of the linear Gauss-Newton filter, with lower mean squared error than the unbiased filter, is proposed. Furthermore the Gauss-Newton filter is adapted using the Levenberg Marquardt method for improved convergence. In order to improve the computation requirements, a recursive version of the filter is obtained.en_ZA
dc.identifier.apacitationNadjiasngar, R. (2013). <i>On improving the performance of the Gauss-Newton filter</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/5142en_ZA
dc.identifier.chicagocitationNadjiasngar, Roaldje. <i>"On improving the performance of the Gauss-Newton filter."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2013. http://hdl.handle.net/11427/5142en_ZA
dc.identifier.citationNadjiasngar, R. 2013. On improving the performance of the Gauss-Newton filter. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Nadjiasngar, Roaldje AB - The Gauss-Newton filter is a tracking filter developed by Norman Morrison around the same time as the celebrated Kalman filter. It received little attention, primarily due to the computation requirements at the time. Today computers have vast processing capacity and computation is no-longer an issue. The filter finite memory length is identified as the key element in the Gauss-Newton filter adaptability and robustness. This thesis focuses on improving the performance of the Gauss-Newton. We incorporate the process noise statistics into the filter algorithm to obtain a filter which explains the error covariance inconsistency of the Kalaman filter. In addition, a biased version of the linear Gauss-Newton filter, with lower mean squared error than the unbiased filter, is proposed. Furthermore the Gauss-Newton filter is adapted using the Levenberg Marquardt method for improved convergence. In order to improve the computation requirements, a recursive version of the filter is obtained. DA - 2013 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2013 T1 - On improving the performance of the Gauss-Newton filter TI - On improving the performance of the Gauss-Newton filter UR - http://hdl.handle.net/11427/5142 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/5142
dc.identifier.vancouvercitationNadjiasngar R. On improving the performance of the Gauss-Newton filter. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2013 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/5142en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleOn improving the performance of the Gauss-Newton filteren_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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