bibtype	V - Research Report
ARLID	0562434
utime	20231122150844.7
mtime	20221017235959.9
title (primary) (eng)	Diffusion Kalman filtering under unknown process and measurement noise covariance matrices
publisher	place Praha name ÚTIA AV ČR, v. v. i., pub_time 2022
specification	page_count 29 s. media_type P
edition	name Research Report volume_id 2395
keyword	Collaborative estimation
keyword	State estimation
keyword	Variational Bayesian methods
author (primary)	ARLID cav_un_auth*0438006 name1 Vlk name2 T. country CZ
author	ARLID cav_un_auth*0242543 name1 Dedecius name2 Kamil institution UTIA-B full_dept (cz) Adaptivní systémy full_dept Department of Adaptive Systems department (cz) AS department 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/2022/AS/dedecius-0562434.pdf
cas_special	abstract (eng) The state-of-the-art algorithms for Kalman filtering in agent networks with information diffusion impose the requirement of well-defined state-space models. In particular, they assume that both the process and measurement noise covariance matrices are known and properly set. This is a relatively strong assumption in the signal processing domain. By design, the Kalman filters are rather sensitive to its violation, which may potentially lead to their divergence. In this paper, we propose a novel distributed filtering algorithm with increased robustness under unknown process and measurement noise covariance matrices. It is formulated as a Bayesian variational message passing procedure for simultaneous analytically tractable inference of states and measurement noise covariance matrices. RIV IN FORD0 20000 FORD1 20200 FORD2 20205 reportyear 2023 num_of_auth 2 mrcbC52 4 O 4o 20231122150844.7 inst_support RVO:67985556 permalink https://hdl.handle.net/11104/0334861 confidential S arlyear 2022 mrcbTft \nSoubory v repozitáři: 0562434.pdf mrcbU10 2022 mrcbU10 Praha ÚTIA AV ČR, v.v.i.,