bibtype	J - Journal Article
ARLID	0466448
utime	20240103213049.2
mtime	20161206235959.9
SCOPUS	84997787314
WOS	000403462300006
DOI	10.1002/acs.2739
title (primary) (eng)	Adaptive kernels in approximate filtering of state-space models
specification	page_count 15 s. media_type P
serial	ARLID cav_un_epca*0256772 ISSN 0890-6327 title International Journal of Adaptive Control and Signal Processing volume_id 31 volume 6 (2017) page_num 938-952 publisher name Wiley
keyword	filtering
keyword	nonlinear filters
keyword	Bayesian filtering
keyword	sequential Monte Carlo
keyword	approximate filtering
author (primary)	ARLID cav_un_auth*0242543 name1 Dedecius name2 Kamil institution UTIA-B full_dept (cz) Adaptivní systémy full_dept (eng) Department of Adaptive Systems department (cz) AS department (eng) AS full_dept Department of Adaptive Systems country CZ fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
source	url http://library.utia.cas.cz/separaty/2016/AS/dedecius-0466448.pdf
cas_special	project ARLID cav_un_auth*0303543 project_id GP14-06678P agency GA ČR country CZ abstract (eng) Standard Bayesian algorithms used for online filtering of states of hidden Markov models from noisy measurements assume an accurate knowledge of the measurement model in the form of a conditional probability density function. However, this knowledge is often unreachable in practice, and the used models are more or less misspecified, or it is too complex, making the resulting models intractable. This paper focuses on these issues from the particle filtering perspective. It adopts the principles of the approximate Bayesian filtering, where the particle weights are based on the (dis)similarity of the true measurements and the pseudo-measurements obtained by plugging the state particles directly into the measurement equation. Specifically, a new robust method for online tuning of the weighting kernel is proposed. RIV BB FORD0 10000 FORD1 10100 FORD2 10103 reportyear 2018 num_of_auth 1 mrcbC52 4 A hod 4ah 20231122142047.6 inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0265788 mrcbC64 1 Department of Adaptive Systems UTIA-B 10200 COMPUTER SCIENCE, THEORY & METHODS confidential S mrcbC86 3+4 Article Automation Control Systems|Engineering Electrical Electronic mrcbC86 3+4 Article Automation Control Systems|Engineering Electrical Electronic mrcbC86 3+4 Article Automation Control Systems|Engineering Electrical Electronic mrcbT16-e AUTOMATIONCONTROLSYSTEMS|ENGINEERINGELECTRICALELECTRONIC mrcbT16-j 0.638 mrcbT16-s 0.915 mrcbT16-B 49.11 mrcbT16-D Q3 mrcbT16-E Q1 arlyear 2017 mrcbTft \nSoubory v repozitáři: dedecius-0466448.pdf mrcbU14 84997787314 SCOPUS mrcbU24 PUBMED mrcbU34 000403462300006 WOS mrcbU63 cav_un_epca*0256772 International Journal of Adaptive Control and Signal Processing 0890-6327 1099-1115 Roč. 31 č. 6 2017 938 952 Wiley