Bayesian Estimation of Forgetting Factor in Adaptive Filtering and Change Detection

0379258 - ÚTIA 2013 RIV US eng C - Conference Paper (international conference)
Šmídl, Václav - Gustafsson, F.
Bayesian Estimation of Forgetting Factor in Adaptive Filtering and Change Detection.
Proceedings of the IEEE Statistical Signal Processing Workshop 2012. Ann Arbor: IEEE, 2012, s. 197-200. ISBN 978-1-4673-0182-4.
[2012 IEEE Statistical Signal Processing Workshop. Ann Arbor (US), 05.08.2012-08.08.2012]
R&D Projects: GA ČR(CZ) GAP102/11/0437
Institutional support: RVO:67985556
Keywords : Marginalized particle filter * Rao-Blackwellization * maximum entropy
Subject RIV: BD - Theory of Information
http://library.utia.cas.cz/separaty/2012/AS/Smidl-bayesian estimation of forgetting factor in adaptive filtering and change detection.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0210510