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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.urihttp://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. As such, it is in the public domain, and under the provisions of Title 17, United States Code, Section 105, may not be copyrighted.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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