bibtype C - Conference Paper (international conference)
ARLID 0379258
utime 20240103201107.4
mtime 20120828235959.9
WOS 000309943200050
DOI 10.1109/SSP.2012.6319658
title (primary) (eng) Bayesian Estimation of Forgetting Factor in Adaptive Filtering and Change Detection
specification
page_count 4 s.
media_type P
serial
ARLID cav_un_epca*0379257
ISBN 978-1-4673-0182-4
title Proceedings of the IEEE Statistical Signal Processing Workshop 2012
page_num 197-200
publisher
place Ann Arbor
name IEEE
year 2012
keyword Marginalized particle filter
keyword Rao-Blackwellization
keyword maximum entropy
author (primary)
ARLID cav_un_auth*0101207
name1 Šmídl
name2 Václav
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0264146
name1 Gustafsson
name2 F.
country SE
source
url http://library.utia.cas.cz/separaty/2012/AS/Smidl-bayesian estimation of forgetting factor in adaptive filtering and change detection.pdf
cas_special
project
project_id GAP102/11/0437
agency GA ČR
country CZ
ARLID cav_un_auth*0273082
abstract (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.
action
ARLID cav_un_auth*0282796
name 2012 IEEE Statistical Signal Processing Workshop
place Ann Arbor
dates 05.08.2012-08.08.2012
country US
reportyear 2013
RIV BD
presentation_type PO
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0210510
arlyear 2012
mrcbU34 000309943200050 WOS
mrcbU63 cav_un_epca*0379257 Proceedings of the IEEE Statistical Signal Processing Workshop 2012 978-1-4673-0182-4 197 200 Ann Arbor IEEE 2012