Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate student-t distribution

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...

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Bibliographic Details
Published in2012 IEEE International Workshop on Machine Learning for Signal Processing pp. 1 - 6
Main Authors Piche, R., Sarkka, S., Hartikainen, J.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2012
Subjects
Approximation methods
Computational modeling
Gaussian filter
Gaussian smoother
Kalman filters
Noise
Noise measurement
Robust filtering
Robust smoothing
Smoothing methods
Time measurement
Variational Bayes
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ISBN1467310247
9781467310246
ISSN1551-2541
DOI10.1109/MLSP.2012.6349794

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Summary: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.
ISBN:1467310247
9781467310246
ISSN:1551-2541
DOI:10.1109/MLSP.2012.6349794