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

Conference Paper (international conference)

Okzan E., Saha S., Gustafsson F., Šmídl Václav

serial: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2011

action: IEEE International Conference on Acoustics, Speech and Signal Processing, (Praha, CZ, 22.05.2011-27.05.2011)

research: CEZ:AV0Z10750506

keywords: Particle filtering, Dirichlet process, Bayesian Estimation

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abstract (eng):

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.

RIV: BD