Sciweavers

Share
ICASSP
2011
IEEE

Non-parametric bayesian measurement noise density estimation in non-linear filtering

8 years 10 months ago
Non-parametric bayesian measurement noise density estimation in non-linear filtering
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.
Emre Özkan, Saikat Saha, Fredrik Gustafsson,
Added 21 Aug 2011
Updated 21 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Emre Özkan, Saikat Saha, Fredrik Gustafsson, Václav Smídl
Comments (0)
books