Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering
2011 (English)In: Acoustics, Speech and Signal Processing (ICASSP), 2011, IEEE , 2011, 5924-5927 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
IEEE , 2011. 5924-5927 p.
, IEEE International Conference on Acoustics, Speech and Signal Processing. Proceedings, ISSN 1520-6149
Particle filtering, Dirichlet process, Bayesian estimation, Adaptive filtering, Marginalized particle filters
IdentifiersURN: urn:nbn:se:liu:diva-73373DOI: 10.1109/ICASSP.2011.5947710ISI: 000296062406158ISBN: 978-1-4577-0538-0 (print)ISBN: 978-1-4577-0537-3 (online)OAI: oai:DiVA.org:liu-73373DiVA: diva2:471284
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 22-27 May 2011, Prague, Czech Republic