Accession Number : ADA189240
Title : Robustifying the Kalman Filter.
Descriptive Note : Master's thesis,
Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA
Personal Author(s) : Gaver, D P ; Jacobs, P A
PDF Url : ADA189240
Report Date : Nov 1987
Pagination or Media Count : 22
Abstract : Kalman 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 estimation.
Descriptors : *KALMAN FILTERING, ALGORITHMS, ERRORS, MEASUREMENT, MODELS, NONLINEAR SYSTEMS, PERFORMANCE(ENGINEERING), PREDICTIONS, STRUCTURAL PROPERTIES, TRACKING, WEIGHTING FUNCTIONS, ITERATIONS
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE