Non parametric bayesian measurement noise density estimation in non linear filtering

Date

2011-05-22

Author

Özkan, Emre
Fredrik, Gustafsson
Smidl, Vaclav

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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.

Subject Keywords

Particle filtering, Dirichlet process, Bayesian estimation, Adaptive filtering, Marginalized particle filters

URI

https://hdl.handle.net/11511/43334

DOI

https://doi.org/10.1109/icassp.2011.5947710

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

E. Özkan, G. Fredrik, and V. Smidl, “Non parametric bayesian measurement noise density estimation in non linear filtering,” 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/43334.