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

0363163 - ÚTIA 2012 RIV US eng C - Conference Paper (international conference)
Okzan, E. - Saha, S. - Gustafsson, F. - Šmídl, Václav
Non-Parametric Bayesian Measurement Noise Density Estimation in Non-Linear Filtering.
Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011. Piscataway: IEEE, 2011, s. 5924-5927. ISBN 978-1-4577-0539-7.
[IEEE International Conference on Acoustics, Speech and Signal Processing. Praha (CZ), 22.05.2011-27.05.2011]
Institutional research plan: CEZ:AV0Z10750506
Keywords : Particle filtering * Dirichlet process * Bayesian Estimation
Subject RIV: BD - Theory of Information
http://library.utia.cas.cz/separaty/2011/AS/smidl-non-parametric bayesian measurement noise density estimation in non-linear filtering.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0199219