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Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering

SYSNO ASEP	0363163
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering
Author(s)	Okzan, E. (SE) Saha, S. (SE) Gustafsson, F. (SE) Šmídl, Václav (UTIA-B)_{RID, ORCID}
Source Title	Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011. - Piscataway : IEEE, 2011 - ISBN 978-1-4577-0539-7
Pages	s. 5924-5927
Number of pages	4 s.
Publication form	www - www
Action	IEEE International Conference on Acoustics, Speech and Signal Processing
Event date	22.05.2011-27.05.2011
VEvent location	Praha
Country	CZ - Czech Republic
Event type	WRD
Language	eng - English
Country	US - United States
Keywords	Particle filtering ; Dirichlet process ; Bayesian Estimation
Subject RIV	BD - Theory of Information
CEZ	AV0Z10750506 - UTIA-B (2005-2011)
Annotation	In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle ﬁlters and Dirichlet process mixtures. Dirichlet processes are widely used in statistics for nonparametric density estimation. In the proposed method, the unknown noise is modeled as a Gaussian mixture with unknown number of components. The joint estimation of the state and the noise density is done via particle ﬁlters. Furthermore, the number of components and the noise statistics are allowed to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced.
Workplace	Institute of Information Theory and Automation
Contact	Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
Year of Publishing	2012

Number of the records: 1