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