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Recursive Outlier-Robust Filtering And Smoothing For Nonlinear Systems Using The Multivariate Student-T Distribution

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Details

Original languageEnglish
Title of host publication2012 IEEE International Workshop on Machine Learning for Signal Processing, MLSP, September 23-26 2012, Santander, Spain
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)978-1-4673-1025-3
ISBN (Print)978-1-4673-1024-6
DOIs
Publication statusPublished - 2012
Publication typeA4 Article in a conference publication

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
ISSN (Print)1551-2541

Abstract

Nonlinear Kalman filter and Rauch–Tung–Striebel smoother type recursive estimators for nonlinear discrete-time state space models with multivariate Student’s t-distributed measurement noise are presented. The methods approximate the posterior state at each time step using the variational Bayes method. The nonlinearities in the dynamic and measurement models are handled using the nonlinear Gaussian filtering and smoothing approach, which encompasses many known nonlinear Kalman-type filters. The method is compared to alternative methods in a computer simulation.

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