Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F11%3A00363163" target="_blank" >RIV/67985556:_____/11:00363163 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
Result language
angličtina
Original language name
Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering
Original language description
In this study, we investigate online Bayesian estimation of the measurement noise density of a given state space model using particle ?lters 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 ?lters. Furthermore, the number of components and the noisestatistics are allowed to vary in time. An extension of the method for the estimation of time varying noise characteristics is also introduced.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
BD - Information theory
OECD FORD branch
—
Result continuities
Project
—
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2011
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011
ISBN
978-1-4577-0539-7
ISSN
—
e-ISSN
—
Number of pages
4
Pages from-to
5924-5927
Publisher name
IEEE
Place of publication
Piscataway
Event location
Praha
Event date
May 22, 2011
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—