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Robustifying the Kalman filter

dc.contributor.authorGaver, Donald Paul
dc.contributor.authorJacobs, Patricia A.
dc.date1987-11
dc.date.accessioned2013-03-07T21:54:03Z
dc.date.available2013-03-07T21:54:03Z
dc.date.issued1987-11
dc.identifier.urihttps://hdl.handle.net/10945/30147
dc.description.abstractKalman filters are tracking and prediction algorithms based on Gaussian measurement errors and structural models. The Kalman filter performance may degrade if the measurement errors come from a thicker-tailed-than Gaussian distribution. In this report non-linear procedures are described which are based on Kalman-type models, but work with student-t measurement errors. Keywords: Kalman filter; Student-t measurement errors; Iterative reweighting procedure; Nonlinear filter; Biweight; Robust estimationen_US
dc.description.sponsorshipPrepared for: Chief of Naval Researchen_US
dc.description.urihttp://archive.org/details/robustifyingkalm00gave
dc.language.isoen_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.lcshKALMAN FILTERING.en_US
dc.titleRobustifying the Kalman filteren_US
dc.typeTechnical Reporten_US
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.subject.authorKalman filteren_US
dc.subject.authorstudent-t measurement errorsen_US
dc.subject.authorIterative re-weighting procedureen_US
dc.subject.authorNon-linear Filteren_US
dc.subject.authorBiweighten_US
dc.subject.authorRobust Estimationen_US
dc.description.funderfunds provided by the Chief of Naval Research, Arlington, VA.en_US
dc.identifier.npsreportNPS55-87-014


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