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Bibliografie

Conference Paper (international conference)

Bayesian Estimation of Forgetting Factor in Adaptive Filtering and Change Detection

Šmídl Václav, Gustafsson F.

: Proceedings of the IEEE Statistical Signal Processing Workshop 2012, p. 197-200

: 2012 IEEE Statistical Signal Processing Workshop, (Ann Arbor, US, 05.08.2012-08.08.2012)

: GAP102/11/0437, GA ČR

: Marginalized particle filter, Rao-Blackwellization, maximum entropy

: 10.1109/SSP.2012.6319658

: http://library.utia.cas.cz/separaty/2012/AS/Smidl-bayesian estimation of forgetting factor in adaptive filtering and change detection.pdf

(eng): An adaptive filter is derived in a Bayesian framework from the assumption that the difference in the parameter distribution from one time to another is bounded in terms of the Kullback-Leibler divergence. We show an explicit link to the general concepts of exponential forgetting, and outline the details for a linear Gaussian model with unknown parameter and covariance. We extend the problem to an unknown forgetting factor, where we provide a particular prior that allows for abrupt changes in forgetting, which is useful in change detection problems. The Rao-Blackwellized particle filter is used for the implementation, and its performance is assessed in a simulation of system with abrupt changes of parameters.

: BD

07.01.2019 - 08:39